From d267aa3ff47d70460a5faa85049527cf8e92df96 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Thu, 4 Feb 2021 18:20:07 +0000
Subject: [PATCH 01/46] Create Yang2017

---
 .../models/full_battery_models/lithium_ion/Yang2017  | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 pybamm/models/full_battery_models/lithium_ion/Yang2017

diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017 b/pybamm/models/full_battery_models/lithium_ion/Yang2017
new file mode 100644
index 0000000000..f9a9712b96
--- /dev/null
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017
@@ -0,0 +1,12 @@
+
+import pybamm
+from .dfn import DFN
+
+class Yang2017(DFN):
+   def __init__(self):
+        options = options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible"}
+        super().__init__(self, options=options, name="Yang 2017")
+  
+   @property
+   def default_parameter_values(self):
+       return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)

From 28679bd2dc9fd0330bd289d158dac2ac788ff585 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <k.nwanoro@lancaster.ac.uk>
Date: Thu, 4 Feb 2021 22:47:19 +0000
Subject: [PATCH 02/46] Create parameters.csv

---
 .../lithium-ion/cells/Yang2017/parameters.csv | 34 +++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/Yang2017/parameters.csv

diff --git a/pybamm/input/parameters/lithium-ion/cells/Yang2017/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/Yang2017/parameters.csv
new file mode 100644
index 0000000000..88d23e4db7
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/Yang2017/parameters.csv
@@ -0,0 +1,34 @@
+Name [units],Value,Reference,Notes,,
+# Empty rows and rows starting with ‘#’ will be ignored,,,,,
+,,,,,
+# Macroscale geometry,,,,,
+Negative current collector thickness [m],2.50E-05,Scott Moura FastDFN,no info from Peyman MPM,,`
+Negative electrode thickness [m],4.87E-05,Yang 2017,,,
+Separator thickness [m],2.50E-05,Yang 2017,,,
+Positive electrode thickness [m],4.75E-05,Yang 2017,,,
+Positive current collector thickness [m],2.50E-05,Scott Moura FastDFN,no info from Peyman MPM,,
+Electrode height [m],1,KOKAM SLPB78205130H,Not needed for 1D,,
+Electrode width [m],0.205,KOKAM SLPB78205130H,Not needed for 1D,,
+Cell cooling surface area [m2],0.41,,pouch,,
+Cell volume [m3],3.92E-05,,pouch,,
+,,,,,
+# Current collector properties ,,,,,
+Negative current collector conductivity [S.m-1],59600000,LIONSIMBA,carbon,,
+Positive current collector conductivity [S.m-1],35500000,LIONSIMBA,aluminium,,
+,,,,,
+# Density,,,,,
+Negative current collector density [kg.m-3],8954,,,,
+Positive current collector density [kg.m-3],2707,,,,
+,,,,,
+# Specific heat capacity,,,,,
+Negative current collector specific heat capacity [J.kg-1.K-1],385,,,,
+Positive current collector specific heat capacity [J.kg-1.K-1],897,,,,
+,,,,,
+# Thermal conductivity,,,,,
+Negative current collector thermal conductivity [W.m-1.K-1],401,,,,
+Positive current collector thermal conductivity [W.m-1.K-1],237,,,,
+,,,,,
+# Electrical,,,,,
+Nominal cell capacity [A.h],12.4,Yang2017,,,
+Typical current [A],12.4,,1C current,,
+Current function [A],12.4,default current function,,,

From 55821b4177bd8469bf22a52473e180459ec7d91b Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Tue, 9 Feb 2021 18:05:51 +0000
Subject: [PATCH 03/46] Rename Yang2017 to Yang2017.py

---
 .../full_battery_models/lithium_ion/{Yang2017 => Yang2017.py}     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename pybamm/models/full_battery_models/lithium_ion/{Yang2017 => Yang2017.py} (100%)

diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017 b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
similarity index 100%
rename from pybamm/models/full_battery_models/lithium_ion/Yang2017
rename to pybamm/models/full_battery_models/lithium_ion/Yang2017.py

From 38df92998e6de6a13757bb485c6f3798d336be96 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Wed, 17 Feb 2021 13:45:25 +0000
Subject: [PATCH 04/46] Check issues associated with calling Yang2017

---
 pybamm/CITATIONS.txt                          |  14 ++
 .../yang2017_Li_plating/README.md             |  11 ++
 .../yang2017_Li_plating/parameters.csv        |   8 ++
 .../graphite_Yang2017/README.md               |  12 ++
 .../graphite_diffusivity_Ecker2015.py         |  37 +++++
 ...lyte_exchange_current_density_Ecker2015.py |  48 +++++++
 .../graphite_ocp_Ecker2015.csv                |  41 ++++++
 .../graphite_ocp_Ecker2015_function.py        |  62 +++++++++
 ...easured_graphite_diffusivity_Ecker2015.csv |  23 ++++
 .../graphite_Yang2017/parameters.csv          |  25 ++++
 ...ting_exchange_current_density_OKane2020.py |  27 ++++
 .../lithium-ion/seis/yang2017_sei/README.md   |  11 ++
 .../seis/yang2017_sei/parameters.csv          |  23 ++++
 .../full_battery_models/base_battery_model.py |  14 ++
 .../lithium_ion/Yang2017.py                   |   4 +-
 .../full_battery_models/lithium_ion/dfn.py    |   7 +-
 .../full_battery_models/lithium_ion/spm.py    |   5 +-
 .../full_battery_models/lithium_ion/spme.py   |   6 +-
 .../interface/lithium_plating/base_plating.py |  15 +-
 .../lithium_plating/irreversible_plating.py   |  20 ++-
 .../lithium_plating/reversible_plating.py     |  19 ++-
 .../porosity/full_reaction_driven_porosity.py |  38 +++++-
 .../leading_reaction_driven_porosity.py       |  41 +++++-
 pybamm/parameters/lithium_ion_parameters.py   | 129 +++++++++++-------
 pybamm/parameters/parameter_sets.py           |  15 ++
 pybamm/parameters/parameter_values.py         |   5 +
 26 files changed, 561 insertions(+), 99 deletions(-)
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv

diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt
index 4190938539..9e887a78c3 100644
--- a/pybamm/CITATIONS.txt
+++ b/pybamm/CITATIONS.txt
@@ -347,3 +347,17 @@ doi={10.1149/2.0661810jes}
   publisher = {Elsevier Science (USA)},
   doi = {10.1006/jcph.2002.7041},
 }
+
+
+@article{Yang2017,
+  author = {Yang, Xiao-Guang and Leng, Yungjun and Zhang, Guangsheng
+            and Ge, Shanhai and Wang, Chao-Yang},
+  title = {Modeling of lithium plating induced aging of lithium-ion batteries: 
+  Transion from linear to nonlinear aging},
+  journal = {Power Sources},
+  volume = {360},
+  pages = {28--40},
+  year = {2017},
+  publisher = {Elsevier},
+  url = https://doi.org/10.1016/j.jpowsour.2017.05.110
+}
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/README.md b/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/README.md
new file mode 100644
index 0000000000..3c63aa585e
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/README.md
@@ -0,0 +1,11 @@
+# SEI parameters
+
+Some example parameters for SEI growth from the papers:
+
+> Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004). Development of first principles capacity fade model for Li-ion cells. Journal of the Electrochemical Society, 151(2), A196-A203.
+> Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for aging of lithium-ion battery cells. Journal of The Electrochemical Society, 151(3), A456-A462.
+> Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of continued growth of the solid–electrolyte interphase. ChemSusChem, 11(12), 1950-1955.
+> Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society, 156(3), 
+> Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40.
+
+Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit SEI models to observed experimental data in the referenced papers.
diff --git a/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/parameters.csv
new file mode 100644
index 0000000000..daaa55b5f0
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/parameters.csv
@@ -0,0 +1,8 @@
+Name [units],Value,Reference,Notes
+,,,
+,,,
+# Lithium plating parameters,,,
+Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
+Lithium plating kinetic rate constant [m.s-1],1.00E+00,,Not required for Yang2017
+Exchange-current density for Lithium plating [A.m-2],[function]plating_exchange_current_density_OKane2020,,
+Initial plated lithium concentration [mol.m-3],0.00E+00,,
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
new file mode 100644
index 0000000000..bf092e7d95
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
@@ -0,0 +1,12 @@
+# Graphite negative electrode parameters
+
+Parameters for a graphite negative electrode, from the papers:
+
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery I. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
+
+>Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+
+The fits to data for the electrode and electrolyte properties are those provided
+by Dr. Simon O’Kane in the paper:
+
+> Richardson, Giles, et. al. "Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation." Electrochemica Acta 339 (2020): 135862
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py
new file mode 100644
index 0000000000..91103baedc
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py
@@ -0,0 +1,37 @@
+from pybamm import exp, constants
+
+
+def graphite_diffusivity_Ecker2015(sto, T):
+    """
+    Graphite diffusivity as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+     .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_ref = 8.4e-13 * exp(-11.3 * sto) + 8.2e-15
+    E_D_s = 3.03e4
+    arrhenius = exp(-E_D_s / (constants.R * T)) * exp(E_D_s / (constants.R * 296))
+
+    return D_ref * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py
new file mode 100644
index 0000000000..140f1cbe70
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py
@@ -0,0 +1,48 @@
+from pybamm import exp, constants, Parameter
+
+
+def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_ref = 1.995 * 1e-10
+
+    # multiply by Faraday's constant to get correct units
+    m_ref = constants.F * k_ref  # (A/m2)(mol/m3)**1.5 - includes ref concentrations
+    E_r = 53400
+
+    arrhenius = exp(-E_r / (constants.R * T)) * exp(E_r / (constants.R * 296.15))
+
+    c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]")
+
+    return (
+        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5
+    )
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv
new file mode 100644
index 0000000000..a578a7fa09
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv
@@ -0,0 +1,41 @@
+0.0015151515151514694, 1.4325153374233128
+0.0060606060606061, 0.8619631901840491
+0.010606060606060619, 0.7914110429447854
+0.016666666666666607, 0.6595092024539877
+0.021212121212121238, 0.5797546012269938
+0.022727272727272707, 0.5245398773006136
+0.030303030303030276, 0.4754601226993864
+0.039393939393939315, 0.4141104294478526
+0.045454545454545414, 0.3680981595092023
+0.05303030303030298, 0.3312883435582821
+0.06666666666666665, 0.28220858895705514
+0.07878787878787874, 0.24846625766871155
+0.08939393939393936, 0.2239263803680982
+0.10151515151515145, 0.22085889570552153
+0.12727272727272732, 0.21165644171779152
+0.14242424242424245, 0.2024539877300613
+0.15909090909090917, 0.19938650306748462
+0.17727272727272725, 0.19325153374233128
+0.19393939393939397, 0.18404907975460127
+0.21363636363636362, 0.1809815950920246
+0.23333333333333328, 0.17177914110429437
+0.25757575757575757, 0.16564417177914104
+0.2787878787878788, 0.16257668711656437
+0.303030303030303, 0.15337423312883436
+0.32878787878787885, 0.14110429447852746
+0.35151515151515156, 0.13496932515337412
+0.3712121212121211, 0.13190184049079745
+0.39242424242424234, 0.128834355828221
+0.5681818181818183, 0.1257668711656441
+0.5878787878787879, 0.12269938650306744
+0.6060606060606062, 0.1165644171779141
+0.6272727272727272, 0.10122699386503076
+0.6545454545454545, 0.0950920245398772
+0.6742424242424243, 0.0950920245398772
+0.6939393939393939, 0.08895705521472386
+0.7181818181818183, 0.08895705521472386
+0.7393939393939393, 0.08588957055214719
+0.8909090909090911, 0.08282208588957074
+0.956060606060606, 0.08272208588957074
+0.9772727272727273, 0.07975460122699385
+1, 0.07055214723926384
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py
new file mode 100644
index 0000000000..8ac61354f4
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py
@@ -0,0 +1,62 @@
+from pybamm import exp, tanh
+
+
+def graphite_ocp_Ecker2015_function(sto):
+    """
+    Graphite OCP as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+     .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Open circuit potential
+    """
+
+    # Graphite negative electrode from Ecker, Kabitz, Laresgoiti et al.
+    # Analytical fit (WebPlotDigitizer + gnuplot)
+    a = 0.716502
+    b = 369.028
+    c = 0.12193
+    d = 35.6478
+    e = 0.0530947
+    g = 0.0169644
+    h = 27.1365
+    i = 0.312832
+    j = 0.0199313
+    k = 28.5697
+    m = 0.614221
+    n = 0.931153
+    o = 36.328
+    p = 1.10743
+    q = 0.140031
+    r = 0.0189193
+    s = 21.1967
+    t = 0.196176
+
+    u_eq = (
+        a * exp(-b * sto)
+        + c * exp(-d * (sto - e))
+        - r * tanh(s * (sto - t))
+        - g * tanh(h * (sto - i))
+        - j * tanh(k * (sto - m))
+        - n * exp(o * (sto - p))
+        + q
+    )
+
+    return u_eq
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv
new file mode 100644
index 0000000000..936c491f96
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv
@@ -0,0 +1,23 @@
+0.04291659469592768,2.5318983605709854e-13
+0.08025337997334403,4.438292394020732e-14
+0.12014956931041065,3.1947426343055446e-14
+0.15796120718295306,2.6077962994674755e-14
+0.19575227382393767,2.255908580891848e-14
+0.2357650334731653,1.16865022242173e-14
+0.27144412034088194,1.2386927328130891e-14
+0.3100288936661386,2.0291954487412547e-14
+0.3484165260222996,3.2604716406478646e-15
+0.3866704453733347,7.640156635519089e-16
+0.4264157790123111,8.417747371928056e-16
+0.4641862744217376,7.717093525037831e-16
+0.5018779134435263,8.837921553351043e-16
+0.5412118224513462,3.109171322297572e-15
+0.5766749113877059,6.062184667385398e-15
+0.6148036890800979,3.595937210129949e-14
+0.6545815938357071,2.0323093806678467e-15
+0.7681399348426814,7.802664217099549e-16
+0.8089429726375879,7.728942922045022e-16
+0.8823034129113143,9.65829674321414e-16
+0.9220864604748132,9.566999594213241e-16
+0.9607500901877076,1.2545776383613633e-15
+0.9976360226401492,1.395684713792463e-14
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
new file mode 100644
index 0000000000..7b57d24ce8
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
@@ -0,0 +1,25 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrode properties,,,
+Negative electrode conductivity [S.m-1],14,,
+Maximum concentration in negative electrode [mol.m-3],31920,,
+Measured negative electrode diffusivity [m2.s-1],[data]measured_graphite_diffusivity_Ecker2015,,
+Negative electrode diffusivity [m2.s-1],[function]graphite_diffusivity_Ecker2015,,
+Measured negative electrode OCP [V],[data]graphite_ocp_Ecker2015,,
+Negative electrode OCP [V],[function]graphite_ocp_Ecker2015_function,,
+,,,
+# Microstructure,,,
+Negative electrode porosity,0.32,Yang2017,
+Negative electrode active material volume fraction,0.372403,,
+Negative particle radius [m],1.00E-05,Yang2017,
+Negative electrode Bruggeman coefficient (electrolyte),1.637278934,Solve for permeability factor B=0.162=eps^b,
+Negative electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to the solid conductivity,
+,,,
+# Interfacial reactions,,,
+Negative electrode cation signed stoichiometry,-1,,
+Negative electrode electrons in reaction,1,,
+Negative electrode exchange-current density [A.m-2],[function]graphite_electrolyte_exchange_current_density_Ecker2015,,
+,,,
+# Thermal parameters,,,
+Negative electrode OCP entropic change [V.K-1],0,,
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
new file mode 100644
index 0000000000..a3d97cc84e
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
@@ -0,0 +1,27 @@
+from pybamm import constants, Parameter,Scalar
+
+
+def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li plating reaction [A.m-2].
+    References
+    ----------
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+   # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    return Scalar(0.001)
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md
new file mode 100644
index 0000000000..3c63aa585e
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md
@@ -0,0 +1,11 @@
+# SEI parameters
+
+Some example parameters for SEI growth from the papers:
+
+> Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004). Development of first principles capacity fade model for Li-ion cells. Journal of the Electrochemical Society, 151(2), A196-A203.
+> Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for aging of lithium-ion battery cells. Journal of The Electrochemical Society, 151(3), A456-A462.
+> Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of continued growth of the solid–electrolyte interphase. ChemSusChem, 11(12), 1950-1955.
+> Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society, 156(3), 
+> Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40.
+
+Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit SEI models to observed experimental data in the referenced papers.
diff --git a/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv
new file mode 100644
index 0000000000..bfc0d7ad59
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv
@@ -0,0 +1,23 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# SEI properties,,,
+Inner SEI reaction proportion,0.5,,
+Inner SEI partial molar volume [m3.mol-1],9.47E-05, Safari paper,
+Outer SEI partial molar volume [m3.mol-1],9.47E-05, Safari paper,
+SEI reaction exchange current density [A.m-2],1.50E-07, Guess,
+SEI resistivity [Ohm.m],5.00E+06, Safari paper,
+Outer SEI solvent diffusivity [m2.s-1],2.50E-22, Single paper,
+Bulk solvent concentration [mol.m-3],2.64E+03, Ploehn paper,
+Ratio of inner and outer SEI exchange current densities,1, Assume same,
+Inner SEI open-circuit potential [V],0.1,,
+Outer SEI open-circuit potential [V],0.8,,
+Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper,
+Inner SEI lithium interstitial diffusivity [m2.s-1],1.00E-20, Guess,
+Lithium interstitial reference concentration [mol.m-3],15, Single paper,
+Initial inner SEI thickness [m],2.50E-09, 2.5E-9 1/2 of initial thickness in Safari paper,
+Initial outer SEI thickness [m],2.50E-09, 1/2 of initial thickness in Safari paper,
+EC initial concentration in electrolyte [mol.m-3],4.54E+03, Safari paper,
+EC diffusivity [m2.s-1],2.00E-18, adjusted parameter in Yang paper,
+SEI kinetic rate constant [m.s-1],1.00E-12, adjusted parameter in Yang paper,
+SEI open-circuit potential [V],0.4, Safari paper,
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index ff6268efa0..419e27e855 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -232,6 +232,7 @@ def options(self, extra_options):
             "sei": "none",
             "lithium plating": "none",
             "sei porosity change": "false",
+            "lithium plating porosity change": "false",
             "loss of active material": "none",
             "working electrode": "none",
             "particle cracking": "none",
@@ -399,6 +400,19 @@ def options(self, extra_options):
             raise pybamm.OptionError(
                 "Unknown lithium plating model '{}'".format(options["lithium plating"])
             )
+            
+            
+        if options["lithium plating porosity change"] not in ["true", "false"]:
+            if options["lithium plating porosity change"] in [True, False]:
+                raise pybamm.OptionError(
+                    "lithium plating porosity change must now be given in string format "
+                    "('true' or 'false')"
+                )
+            raise pybamm.OptionError(
+                "Unknown lithium plating porosity change '{}'".format(
+                    options["sei porosity change"]
+                )
+            )
 
         if options["loss of active material"] not in [
             "none",
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index f9a9712b96..4bfbfb2e52 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -4,9 +4,11 @@
 
 class Yang2017(DFN):
    def __init__(self):
-        options = options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible"}
+        options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible", "lithium plating porosity change": "true" }
         super().__init__(self, options=options, name="Yang 2017")
   
    @property
    def default_parameter_values(self):
        return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+   
+ 
\ No newline at end of file
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index 5180a923f9..8ff174dd98 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -56,11 +56,12 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman model", build=True):
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false":
+        if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
             self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
-        elif self.options["sei porosity change"] == "true":
+        elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
             self.submodels["porosity"] = pybamm.porosity.Full(self.param)
-
+         
+     
     def set_active_material_submodel(self):
 
         if self.options["loss of active material"] == "none":
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index 93e1201f8b..a9c6160090 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -57,10 +57,11 @@ def __init__(self, options=None, name="Single Particle Model", build=True):
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false":
+         if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
             self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
-        elif self.options["sei porosity change"] == "true":
+         elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
             self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)
+            
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py
index 50f84df24d..5b40eef43f 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spme.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spme.py
@@ -59,10 +59,10 @@ def __init__(
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false":
+         if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
             self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
-        elif self.options["sei porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)
+         elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
+               self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)     
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
index ecd833b35b..4359481a96 100644
--- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
@@ -7,21 +7,18 @@
 
 class BasePlating(BaseInterface):
     """Base class for lithium plating models.
-
     Parameters
     ----------
     param : parameter class
         The parameters to use for this submodel
     reactions : dict, optional
         Dictionary of reaction terms
-
     References
     ----------
     .. [1] SEJ O'Kane, ID Campbell, MWJ Marzook, GJ Offer and M Marinescu. "Physical
            Origin of the Differential Voltage Minimum Associated with Li Plating in
            Lithium-Ion Batteries". Journal of The Electrochemical Society,
            167:090540, 2019
-
     **Extends:** :class:`pybamm.interface.BaseInterface`
     """
 
@@ -56,7 +53,7 @@ def _get_standard_concentration_variables(self, c_plated_Li):
             c_scale = 1
             L_scale = 1
         else:
-            c_scale = param.c_e_typ
+            c_scale = param.c_Li_typ
             L_scale = param.V_bar_plated_Li * c_scale / param.a_n_typ
 
         c_plated_Li_av = pybamm.x_average(c_plated_Li)
@@ -71,14 +68,16 @@ def _get_standard_concentration_variables(self, c_plated_Li):
             f"{Domain} lithium plating concentration": c_plated_Li,
             f"{Domain} lithium plating concentration [mol.m-3]": c_plated_Li * c_scale,
             f"{Domain} X-averaged lithium plating concentration": c_plated_Li_av,
-            f"X-averaged {domain} lithium plating concentration [mol.m-3]":
-            c_plated_Li_av * c_scale,
+            f"X-averaged {domain} lithium plating concentration"
+            " [mol.m-3]": c_plated_Li_av * c_scale,
             f"{Domain} lithium plating thickness [m]": L_plated_Li * L_scale,
             f"X-averaged {domain} lithium plating thickness [m]": L_plated_Li_av
             * L_scale,
             f"Loss of Li to {domain} lithium plating [mol]": Q_plated_Li * c_scale,
             f"Loss of capacity to {domain} lithium plating [A.h]": Q_plated_Li
-            * c_scale * param.F / 3600,
+            * c_scale
+            * param.F
+            / 3600,
         }
 
         return variables
@@ -118,4 +117,4 @@ def _get_standard_reaction_variables(self, j_stripping):
             f"interfacial current density [A.m-2]": j_stripping_av * j_scale,
         }
 
-        return variables
+        return variables
\ No newline at end of file
diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index 1227c54891..ef7c493740 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -1,4 +1,3 @@
-#
 # Class for irreversible lithium plating
 #
 import pybamm
@@ -7,21 +6,18 @@
 
 class IrreversiblePlating(BasePlating):
     """Base class for irreversible lithium plating.
-
     Parameters
     ----------
     param : parameter class
         The parameters to use for this submodel
     domain : str
         The domain of the model either 'Negative' or 'Positive'
-
     References
     ----------
     .. [1] SEJ O'Kane, ID Campbell, MWJ Marzook, GJ Offer and M Marinescu. "Physical
            Origin of the Differential Voltage Minimum Associated with Li Plating in
            Lithium-Ion Batteries". Journal of The Electrochemical Society,
            167:090540, 2019
-
     **Extends:** :class:`pybamm.lithium_plating.BasePlating`
     """
 
@@ -31,7 +27,7 @@ def __init__(self, param, domain):
 
     def get_fundamental_variables(self):
         c_plated_Li = pybamm.Variable(
-            "Plated lithium concentration",
+            f"{self.domain.capitalize()} electrode lithium plating concentration",
             domain=self.domain.lower() + " electrode",
             auxiliary_domains={"secondary": "current collector"},
         )
@@ -45,16 +41,18 @@ def get_coupled_variables(self, variables):
         phi_s_n = variables[f"{self.domain} electrode potential"]
         phi_e_n = variables[f"{self.domain} electrolyte potential"]
         c_e_n = variables[f"{self.domain} electrolyte concentration"]
+        T = variables[f"{self.domain} electrode temperature"]
         eta_sei = variables[f"{self.domain} electrode sei film overpotential"]
-        C_plating = param.C_plating
+        c_plated_Li = variables[
+            f"{self.domain} electrode lithium plating concentration"
+        ]
+        j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
         phi_ref = param.U_n_ref / param.potential_scale
 
         # need to revise for thermal case
         # j_stripping is always negative, because there is no stripping, only plating
-        j_stripping = (
-            -(1 / C_plating)
-            * c_e_n
-            * (pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei)))
+        j_stripping = -j0_plating * (
+            pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
         )
 
         variables.update(self._get_standard_reaction_variables(j_stripping))
@@ -90,4 +88,4 @@ def set_initial_conditions(self, variables):
         ]
         c_plated_Li_0 = self.param.c_plated_Li_0
 
-        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
+        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
\ No newline at end of file
diff --git a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
index 8bbd13156a..9513ed8d4a 100644
--- a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
@@ -1,4 +1,3 @@
-#
 # Class for reversible lithium plating
 #
 import pybamm
@@ -7,21 +6,18 @@
 
 class ReversiblePlating(BasePlating):
     """Base class for reversible lithium plating.
-
     Parameters
     ----------
     param : parameter class
         The parameters to use for this submodel
     domain : str
         The domain of the model either 'Negative' or 'Positive'
-
     References
     ----------
     .. [1] SEJ O'Kane, ID Campbell, MWJ Marzook, GJ Offer and M Marinescu. "Physical
            Origin of the Differential Voltage Minimum Associated with Li Plating in
            Lithium-Ion Batteries". Journal of The Electrochemical Society,
            167:090540, 2019
-
     **Extends:** :class:`pybamm.lithium_plating.BasePlating`
     """
 
@@ -31,7 +27,7 @@ def __init__(self, param, domain):
 
     def get_fundamental_variables(self):
         c_plated_Li = pybamm.Variable(
-            "Plated lithium concentration",
+            f"{self.domain.capitalize()} electrode lithium plating concentration",
             domain=self.domain.lower() + " electrode",
             auxiliary_domains={"secondary": "current collector"},
         )
@@ -45,18 +41,19 @@ def get_coupled_variables(self, variables):
         phi_s_n = variables[f"{self.domain} electrode potential"]
         phi_e_n = variables[f"{self.domain} electrolyte potential"]
         c_e_n = variables[f"{self.domain} electrolyte concentration"]
+        T = variables[f"{self.domain} electrode temperature"]
         eta_sei = variables[f"{self.domain} electrode sei film overpotential"]
         c_plated_Li = variables[
             f"{self.domain} electrode lithium plating concentration"
         ]
-        C_plating = param.C_plating
+        j0_stripping = param.j0_stripping(c_e_n, c_plated_Li, T)
+        j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
         phi_ref = param.U_n_ref / param.potential_scale
 
         # need to revise for thermal case
-        j_stripping = (1 / C_plating) * (
-            c_plated_Li * pybamm.exp(0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
-            - c_e_n * pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
-        )
+        j_stripping = j0_stripping * pybamm.exp(
+            0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei)
+        ) - j0_plating * pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
 
         variables.update(self._get_standard_reaction_variables(j_stripping))
 
@@ -91,4 +88,4 @@ def set_initial_conditions(self, variables):
         ]
         c_plated_Li_0 = self.param.c_plated_Li_0
 
-        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
+        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
\ No newline at end of file
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index e9e37ac61e..c564ccec4c 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -33,13 +33,43 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["Negative electrode interfacial current density"]
         j_p = variables["Positive electrode interfacial current density"]
-        j_sei_n = variables["Negative electrode sei interfacial current density"]
-        beta_sei_n = self.param.beta_sei_n
-
-        deps_n_dt = -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n
+       
+        
+        if self.options["sei porosity change"] == "true" and self.options["lithium plating porosity change"] == "false":
+            
+             j_sei_n = variables["Negative electrode sei interfacial current density"]
+             beta_sei_n = self.param.beta_sei_n
+             deps_n_dt = -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n
+             
+        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "false": 
+            
+            j_plating = variables[
+            "Negative electrode lithium plating interfacial current density"
+        ]
+            beta_plating = self.param.beta_Li_plating
+            
+            deps_n_dt = -self.param.beta_surf_n * j_n + beta_plating * j_plating
+            
+        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "true":
+           
+            j_plating = variables[
+            "Negative electrode lithium plating interfacial current density"
+        ]
+            
+            j_sei_n = variables["Negative electrode sei interfacial current density"]
+            
+            beta_plating = self.param.beta_Li_plating
+            beta_sei_n = self.param.beta_sei_n
+            
+            deps_n_dt = -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n + beta_plating * j_plating
+        
+        
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
+        
+        
+       
         deps_p_dt = -self.param.beta_surf_p * j_p
 
         variables.update(
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index a3715736f5..bcc5cd3e71 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -37,14 +37,43 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["X-averaged negative electrode interfacial current density"]
         j_p = variables["X-averaged positive electrode interfacial current density"]
-        j_sei_n = variables[
-            "X-averaged negative electrode sei interfacial current density"
-        ]
-        beta_sei_n = self.param.beta_sei_n
-
-        deps_n_dt = pybamm.PrimaryBroadcast(
+       
+           
+        if self.options["sei porosity change"] == "true" and self.options["lithium plating porosity change"] == "false":
+            
+             j_sei_n = variables["X-averaged negative electrode sei interfacial current density"]
+               
+             beta_sei_n = self.param.beta_sei_n
+             deps_n_dt = pybamm.PrimaryBroadcast(
             -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n, ["negative electrode"]
         )
+             
+        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "false": 
+            
+            j_plating = variables[
+            "X-averaged negative electrode lithium plating interfacial current density"]
+        
+            beta_plating = self.param.beta_Li_plating
+            
+            deps_n_dt = pybamm.PrimaryBroadcast(
+            -self.param.beta_surf_n * j_n + beta_plating * j_plating, ["negative electrode"]
+        )
+            
+        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "true":
+           
+            j_plating = variables[
+            "X-averaged negative electrode lithium plating interfacial current density"]
+            
+            j_sei_n = variables["X-averaged negative electrode sei interfacial current density"]
+            
+            beta_plating = self.param.beta_Li_plating
+            beta_sei_n = self.param.beta_sei_n
+            
+            deps_n_dt = pybamm.PrimaryBroadcast(
+            -self.param.beta_surf_n * j_n + beta_plating * j_plating + beta_sei_n * j_sei_n, ["negative electrode"]
+        )
+        
+        
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 30ed07711f..da96cbdaab 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -1,4 +1,3 @@
-#
 # Standard parameters for lithium-ion battery models
 #
 import pybamm
@@ -8,7 +7,6 @@
 class LithiumIonParameters:
     """
     Standard parameters for lithium-ion battery models
-
     Layout:
         1. Dimensional Parameters
         2. Dimensional Functions
@@ -16,14 +14,11 @@ class LithiumIonParameters:
         4. Dimensionless Parameters
         5. Dimensionless Functions
         6. Input Current
-
     Parameters
     ----------
-
     options : dict, optional
         A dictionary of options to be passed to the parameters. The options that
         can be set are listed below.
-
             * "particle shape" : str, optional
                 Sets the model shape of the electrode particles. This is used to
                 calculate the surface area to volume ratio. Can be "spherical"
@@ -49,7 +44,7 @@ def __init__(self, options=None):
         self._set_input_current()
 
     def _set_dimensional_parameters(self):
-        "Defines the dimensional parameters"
+        """Defines the dimensional parameters"""
 
         # Physical constants
         self.R = pybamm.constants.R
@@ -213,16 +208,10 @@ def _set_dimensional_parameters(self):
         self.V_bar_plated_Li = pybamm.Parameter(
             "Lithium metal partial molar volume [m3.mol-1]"
         )
-        self.k_plating = pybamm.Parameter(
-            "Lithium plating kinetic rate constant [m.s-1]"
-        )
         self.c_plated_Li_0_dim = pybamm.Parameter(
             "Initial plated lithium concentration [mol.m-3]"
         )
 
-        # Exchange current density for scaling
-        self.j0_plating_dimensional = self.F * self.k_plating * self.c_e_typ
-
         # Initial conditions
         # Note: the initial concentration in the electrodes can be set as a function
         # of through-cell position, so is defined later as a function
@@ -302,12 +291,12 @@ def _set_dimensional_parameters(self):
         )
 
     def D_e_dimensional(self, c_e, T):
-        "Dimensional diffusivity in electrolyte"
+        """Dimensional diffusivity in electrolyte"""
         inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
         return pybamm.FunctionParameter("Electrolyte diffusivity [m2.s-1]", inputs)
 
     def kappa_e_dimensional(self, c_e, T):
-        "Dimensional electrolyte conductivity"
+        """Dimensional electrolyte conductivity"""
         inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
         return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs)
 
@@ -342,7 +331,7 @@ def D_p_dimensional(self, sto, T):
         )
 
     def j0_n_dimensional(self, c_e, c_s_surf, T):
-        "Dimensional negative exchange-current density [A.m-2]"
+        """Dimensional negative exchange-current density [A.m-2]"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e,
             "Negative particle surface concentration [mol.m-3]": c_s_surf,
@@ -353,7 +342,7 @@ def j0_n_dimensional(self, c_e, c_s_surf, T):
         )
 
     def j0_p_dimensional(self, c_e, c_s_surf, T):
-        "Dimensional negative exchange-current density [A.m-2]"
+        """Dimensional negative exchange-current density [A.m-2]"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e,
             "Positive particle surface concentration [mol.m-3]": c_s_surf,
@@ -363,14 +352,36 @@ def j0_p_dimensional(self, c_e, c_s_surf, T):
             "Positive electrode exchange-current density [A.m-2]", inputs
         )
 
+    def j0_stripping_dimensional(self, c_e, c_Li, T):
+        """Dimensional exchange-current density for stripping [A.m-2]"""
+        inputs = {
+            "Electrolyte concentration [mol.m-3]": c_e,
+            "Plated lithium concentration [mol.m-3]": c_Li,
+            "Temperature [K]": T,
+        }
+        return pybamm.FunctionParameter(
+            "Exchange-current density for stripping [A.m-2]", inputs
+        )
+
+    def j0_plating_dimensional(self, c_e, c_Li, T):
+        """Dimensional exchange-current density for plating [A.m-2]"""
+        inputs = {
+            "Electrolyte concentration [mol.m-3]": c_e,
+            "Plated lithium concentration [mol.m-3]": c_Li,
+            "Temperature [K]": T,
+        }
+        return pybamm.FunctionParameter(
+            "Exchange-current density for plating [A.m-2]", inputs
+        )
+
     def U_n_dimensional(self, sto, T):
-        "Dimensional open-circuit potential in the negative electrode [V]"
+        """Dimensional open-circuit potential in the negative electrode [V]"""
         inputs = {"Negative particle stoichiometry": sto}
         u_ref = pybamm.FunctionParameter("Negative electrode OCP [V]", inputs)
         return u_ref + (T - self.T_ref) * self.dUdT_n_dimensional(sto)
 
     def U_p_dimensional(self, sto, T):
-        "Dimensional open-circuit potential in the positive electrode [V]"
+        """Dimensional open-circuit potential in the positive electrode [V]"""
         inputs = {"Positive particle stoichiometry": sto}
         u_ref = pybamm.FunctionParameter("Positive electrode OCP [V]", inputs)
         return u_ref + (T - self.T_ref) * self.dUdT_p_dimensional(sto)
@@ -396,45 +407,45 @@ def dUdT_p_dimensional(self, sto):
         )
 
     def R_n_dimensional(self, x):
-        "Negative particle radius as a function of through-cell distance"
+        """Negative particle radius as a function of through-cell distance"""
         inputs = {"Through-cell distance (x_n) [m]": x}
         return pybamm.FunctionParameter("Negative particle radius [m]", inputs)
 
     def R_p_dimensional(self, x):
-        "Positive particle radius as a function of through-cell distance"
+        """Positive particle radius as a function of through-cell distance"""
         inputs = {"Through-cell distance (x_p) [m]": x}
         return pybamm.FunctionParameter("Positive particle radius [m]", inputs)
 
     def epsilon_s_n(self, x):
-        "Negative electrode active material volume fraction"
+        """Negative electrode active material volume fraction"""
         inputs = {"Through-cell distance (x_n) [m]": x * self.L_x}
         return pybamm.FunctionParameter(
             "Negative electrode active material volume fraction", inputs
         )
 
     def epsilon_s_p(self, x):
-        "Positive electrode active material volume fraction"
+        """Positive electrode active material volume fraction"""
         inputs = {"Through-cell distance (x_p) [m]": x * self.L_x}
         return pybamm.FunctionParameter(
             "Positive electrode active material volume fraction", inputs
         )
 
     def c_n_init_dimensional(self, x):
-        "Initial concentration as a function of dimensionless position x"
+        """Initial concentration as a function of dimensionless position x"""
         inputs = {"Dimensionless through-cell position (x_n)": x}
         return pybamm.FunctionParameter(
             "Initial concentration in negative electrode [mol.m-3]", inputs
         )
 
     def c_p_init_dimensional(self, x):
-        "Initial concentration as a function of dimensionless position x"
+        """Initial concentration as a function of dimensionless position x"""
         inputs = {"Dimensionless through-cell position (x_p)": x}
         return pybamm.FunctionParameter(
             "Initial concentration in positive electrode [mol.m-3]", inputs
         )
 
     def _set_scales(self):
-        "Define the scales used in the non-dimensionalisation scheme"
+        """Define the scales used in the non-dimensionalisation scheme"""
 
         # Microscale (typical values at electrode/current collector interface)
         self.R_n_typ = self.R_n_dimensional(0)
@@ -517,7 +528,7 @@ def _set_scales(self):
         self.timescale = self.tau_discharge
 
     def _set_dimensionless_parameters(self):
-        "Defines the dimensionless parameters"
+        """Defines the dimensionless parameters"""
 
         # Timescale ratios
         self.C_n = self.tau_diffusion_n / self.tau_discharge
@@ -733,15 +744,13 @@ def _set_dimensionless_parameters(self):
         self.beta_sei_n = self.a_n_typ * self.L_sei_0_dim * self.Gamma_SEI_n
 
         # lithium plating parameters
-
-        self.C_plating = self.j_scale_n / self.j0_plating_dimensional
-
-        self.c_plated_Li_0 = self.c_plated_Li_0_dim / self.c_e_typ
+        self.c_Li_typ = self.c_e_typ
+        self.c_plated_Li_0 = self.c_plated_Li_0_dim / self.c_Li_typ
 
         # ratio of lithium plating reaction scaled to intercalation reaction
-        self.Gamma_plating = (
-            self.a_n_typ * self.j_scale_n * self.tau_discharge
-        ) / (self.F * self.c_e_typ)
+        self.Gamma_plating = (self.a_n_typ * self.j_scale_n * self.tau_discharge) / (
+            self.F * self.c_Li_typ
+        )
 
         # Initial conditions
         self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity")
@@ -775,7 +784,7 @@ def chi(self, c_e, T):
         return (2 * (1 - self.t_plus(c_e, T))) * (self.one_plus_dlnf_dlnc(c_e, T))
 
     def t_plus(self, c_e, T):
-        "Cation transference number (dimensionless)"
+        """Cation transference number (dimensionless)"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ,
             "Temperature [K]": self.Delta_T * T + self.T_ref,
@@ -783,7 +792,7 @@ def t_plus(self, c_e, T):
         return pybamm.FunctionParameter("Cation transference number", inputs)
 
     def one_plus_dlnf_dlnc(self, c_e, T):
-        "Thermodynamic factor (dimensionless)"
+        """Thermodynamic factor (dimensionless)"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ,
             "Temperature [K]": self.Delta_T * T + self.T_ref,
@@ -791,20 +800,20 @@ def one_plus_dlnf_dlnc(self, c_e, T):
         return pybamm.FunctionParameter("1 + dlnf/dlnc", inputs)
 
     def D_e(self, c_e, T):
-        "Dimensionless electrolyte diffusivity"
+        """Dimensionless electrolyte diffusivity"""
         c_e_dimensional = c_e * self.c_e_typ
         T_dim = self.Delta_T * T + self.T_ref
         return self.D_e_dimensional(c_e_dimensional, T_dim) / self.D_e_typ
 
     def kappa_e(self, c_e, T):
-        "Dimensionless electrolyte conductivity"
+        """Dimensionless electrolyte conductivity"""
         c_e_dimensional = c_e * self.c_e_typ
         kappa_scale = self.F ** 2 * self.D_e_typ * self.c_e_typ / (self.R * self.T_ref)
         T_dim = self.Delta_T * T + self.T_ref
         return self.kappa_e_dimensional(c_e_dimensional, T_dim) / kappa_scale
 
     def D_n(self, c_s_n, T):
-        "Dimensionless negative particle diffusivity"
+        """Dimensionless negative particle diffusivity"""
         sto = c_s_n
         T_dim = self.Delta_T * T + self.T_ref
         return self.D_n_dimensional(sto, T_dim) / self.D_n_dimensional(
@@ -812,7 +821,7 @@ def D_n(self, c_s_n, T):
         )
 
     def D_p(self, c_s_p, T):
-        "Dimensionless positive particle diffusivity"
+        """Dimensionless positive particle diffusivity"""
         sto = c_s_p
         T_dim = self.Delta_T * T + self.T_ref
         return self.D_p_dimensional(sto, T_dim) / self.D_p_dimensional(
@@ -820,7 +829,7 @@ def D_p(self, c_s_p, T):
         )
 
     def j0_n(self, c_e, c_s_surf, T):
-        "Dimensionless negative exchange-current density"
+        """Dimensionless negative exchange-current density"""
         c_e_dim = c_e * self.c_e_typ
         c_s_surf_dim = c_s_surf * self.c_n_max
         T_dim = self.Delta_T * T + self.T_ref
@@ -831,7 +840,7 @@ def j0_n(self, c_e, c_s_surf, T):
         )
 
     def j0_p(self, c_e, c_s_surf, T):
-        "Dimensionless positive exchange-current density"
+        """Dimensionless positive exchange-current density"""
         c_e_dim = c_e * self.c_e_typ
         c_s_surf_dim = c_s_surf * self.c_p_max
         T_dim = self.Delta_T * T + self.T_ref
@@ -841,25 +850,41 @@ def j0_p(self, c_e, c_s_surf, T):
             / self.j0_p_ref_dimensional
         )
 
+    def j0_stripping(self, c_e, c_Li, T):
+        """Dimensionless exchange-current density for stripping"""
+        c_e_dim = c_e * self.c_e_typ
+        c_Li_dim = c_Li * self.c_Li_typ
+        T_dim = self.Delta_T * T + self.T_ref
+
+        return self.j0_stripping_dimensional(c_e_dim, c_Li_dim, T_dim) / self.j_scale_n
+
+    def j0_plating(self, c_e, c_Li, T):
+        """Dimensionless reverse plating current"""
+        c_e_dim = c_e * self.c_e_typ
+        c_Li_dim = c_Li * self.c_Li_typ
+        T_dim = self.Delta_T * T + self.T_ref
+
+        return self.j0_plating_dimensional(c_e_dim, c_Li_dim, T_dim) / self.j_scale_n
+
     def U_n(self, c_s_n, T):
-        "Dimensionless open-circuit potential in the negative electrode"
+        """Dimensionless open-circuit potential in the negative electrode"""
         sto = c_s_n
         T_dim = self.Delta_T * T + self.T_ref
         return (self.U_n_dimensional(sto, T_dim) - self.U_n_ref) / self.potential_scale
 
     def U_p(self, c_s_p, T):
-        "Dimensionless open-circuit potential in the positive electrode"
+        """Dimensionless open-circuit potential in the positive electrode"""
         sto = c_s_p
         T_dim = self.Delta_T * T + self.T_ref
         return (self.U_p_dimensional(sto, T_dim) - self.U_p_ref) / self.potential_scale
 
     def dUdT_n(self, c_s_n):
-        "Dimensionless entropic change in negative open-circuit potential"
+        """Dimensionless entropic change in negative open-circuit potential"""
         sto = c_s_n
         return self.dUdT_n_dimensional(sto) * self.Delta_T / self.potential_scale
 
     def dUdT_p(self, c_s_p):
-        "Dimensionless entropic change in positive open-circuit potential"
+        """Dimensionless entropic change in positive open-circuit potential"""
         sto = c_s_p
         return self.dUdT_p_dimensional(sto) * self.Delta_T / self.potential_scale
 
@@ -880,15 +905,19 @@ def R_p(self, x):
         return self.R_p_dimensional(x_dim) / self.R_p_typ
 
     def c_n_init(self, x):
-        "Dimensionless initial concentration as a function of dimensionless position x"
+        """
+        Dimensionless initial concentration as a function of dimensionless position x
+        """
         return self.c_n_init_dimensional(x) / self.c_n_max
 
     def c_p_init(self, x):
-        "Dimensionless initial concentration as a function of dimensionless position x"
+        """
+        Dimensionless initial concentration as a function of dimensionless position x
+        """
         return self.c_p_init_dimensional(x) / self.c_p_max
 
     def rho(self, T):
-        "Dimensionless effective volumetric heat capacity"
+        """Dimensionless effective volumetric heat capacity"""
         return (
             self.rho_cn(T) * self.l_cn
             + self.rho_n(T) * self.l_n
@@ -898,7 +927,7 @@ def rho(self, T):
         ) / self.l
 
     def _set_input_current(self):
-        "Set the input current"
+        """Set the input current"""
 
         self.dimensional_current_with_time = pybamm.FunctionParameter(
             "Current function [A]", {"Time [s]": pybamm.t * self.timescale}
@@ -994,4 +1023,4 @@ def options(self, extra_options):
                     options["particle cracking"]
                 )
             )
-        self._options = options
+        self._options = options
\ No newline at end of file
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index c523135985..6051fe9a63 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -89,6 +89,21 @@
     "citation": ["Ecker2015i", "Ecker2015ii", "Richardson2020"],
 }
 
+Yang2017 = {
+    "chemistry": "lithium-ion",
+    "cell": "Yang2017",
+    "negative electrode": "graphite_Yang2017",
+    "separator": "separator_Ecker2015",
+    "positive electrode": "LiNiCoO2_Ecker2015",
+    "electrolyte": "lipf6_Ecker2015",
+    "experiment": "1C_discharge_from_full_Ecker2015",
+    "sei": "yang2017_sei",
+    "lithium plating": "yang2017_Li_plating",
+    "citation": ["Yang2017", "Ecker2015i"],
+}
+
+
+
 Marquis2019 = {
     "chemistry": "lithium-ion",
     "cell": "kokam_Marquis2019",
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 1b73ee1f1a..90d045b46f 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -152,6 +152,11 @@ def update_from_chemistry(self, chemistry):
         # add sei parameters if provided
         if "sei" in chemistry:
             component_groups += ["sei"]
+        
+         # add lithium plating parameters if provided
+        if "lithium plating" in chemistry:
+            component_groups += ["lithium plating"]
+       
 
         if "anode" in chemistry.keys():
             if "negative electrode" in chemistry.keys():

From 0e3a63ebf020b3028f68dc2d784cf437bd4034f6 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 22 Feb 2021 12:15:41 +0000
Subject: [PATCH 05/46] Corrections on Yang2017

---
 .../yang2017_Li_plating/README.md             |  0
 .../yang2017_Li_plating/parameters.csv        |  0
 ...ting_exchange_current_density_OKane2020.py | 27 +++++++++++++++++++
 .../lithium_ion/Yang2017.py                   |  4 +--
 .../lithium_ion/__init__.py                   |  1 +
 .../full_battery_models/lithium_ion/dfn.py    |  4 +--
 .../submodels/porosity/base_porosity.py       |  2 +-
 .../porosity/full_reaction_driven_porosity.py | 27 ++++++-------------
 .../leading_reaction_driven_porosity.py       |  8 +++---
 pybamm/parameters/lithium_ion_parameters.py   |  5 ++++
 10 files changed, 50 insertions(+), 28 deletions(-)
 rename pybamm/input/parameters/lithium-ion/{lithium_plating => lithium_platings}/yang2017_Li_plating/README.md (100%)
 rename pybamm/input/parameters/lithium-ion/{lithium_plating => lithium_platings}/yang2017_Li_plating/parameters.csv (100%)
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py

diff --git a/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/README.md b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/README.md
similarity index 100%
rename from pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/README.md
rename to pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/README.md
diff --git a/pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
similarity index 100%
rename from pybamm/input/parameters/lithium-ion/lithium_plating/yang2017_Li_plating/parameters.csv
rename to pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
new file mode 100644
index 0000000000..a3d97cc84e
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -0,0 +1,27 @@
+from pybamm import constants, Parameter,Scalar
+
+
+def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li plating reaction [A.m-2].
+    References
+    ----------
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+   # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    return Scalar(0.001)
\ No newline at end of file
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index 4bfbfb2e52..37f0c051f9 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -5,8 +5,8 @@
 class Yang2017(DFN):
    def __init__(self):
         options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible", "lithium plating porosity change": "true" }
-        super().__init__(self, options=options, name="Yang 2017")
-  
+        super().__init__(options=options, name="Yang2017")
+        pybamm.citations.register("Yang2017")
    @property
    def default_parameter_values(self):
        return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py
index 52e9e3dc76..40b6b38c76 100644
--- a/pybamm/models/full_battery_models/lithium_ion/__init__.py
+++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py
@@ -8,3 +8,4 @@
 from .basic_dfn import BasicDFN
 from .basic_spm import BasicSPM
 from .basic_dfn_half_cell import BasicDFNHalfCell
+from .Yang2017 import Yang2017
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index 8ff174dd98..0e5281e354 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -57,9 +57,9 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman model", build=True):
     def set_porosity_submodel(self):
 
         if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
+            self.submodels["porosity"] = pybamm.porosity.Constant(self.param,self.options)
         elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.Full(self.param)
+            self.submodels["porosity"] = pybamm.porosity.Full(self.param,self.options)
          
      
     def set_active_material_submodel(self):
diff --git a/pybamm/models/submodels/porosity/base_porosity.py b/pybamm/models/submodels/porosity/base_porosity.py
index f05120b578..cb8f12b734 100644
--- a/pybamm/models/submodels/porosity/base_porosity.py
+++ b/pybamm/models/submodels/porosity/base_porosity.py
@@ -16,7 +16,7 @@ class BaseModel(pybamm.BaseSubModel):
     **Extends:** :class:`pybamm.BaseSubModel`
     """
 
-    def __init__(self, param):
+    def __init__(self, param,options):
         super().__init__(param)
 
     def _get_standard_porosity_variables(
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index c564ccec4c..9ee06c6802 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -17,8 +17,8 @@ class Full(BaseModel):
     **Extends:** :class:`pybamm.porosity.BaseModel`
     """
 
-    def __init__(self, param):
-        super().__init__(param)
+    def __init__(self, param,options):
+        super().__init__(param,options)
 
     def get_fundamental_variables(self):
 
@@ -33,35 +33,24 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["Negative electrode interfacial current density"]
         j_p = variables["Positive electrode interfacial current density"]
+        deps_n_dt = -self.param.beta_surf_n *  j_n
        
         
-        if self.options["sei porosity change"] == "true" and self.options["lithium plating porosity change"] == "false":
+        if self.options["sei porosity change"] == "true":
             
              j_sei_n = variables["Negative electrode sei interfacial current density"]
              beta_sei_n = self.param.beta_sei_n
-             deps_n_dt = -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n
+             deps_n_dt +=  beta_sei_n * j_sei_n
              
-        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "false": 
+        elif self.options["lithium plating porosity change"] == "true":
             
             j_plating = variables[
             "Negative electrode lithium plating interfacial current density"
         ]
-            beta_plating = self.param.beta_Li_plating
+            beta_plating = self.param.beta_plating 
             
-            deps_n_dt = -self.param.beta_surf_n * j_n + beta_plating * j_plating
+            deps_n_dt += beta_plating * j_plating
             
-        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "true":
-           
-            j_plating = variables[
-            "Negative electrode lithium plating interfacial current density"
-        ]
-            
-            j_sei_n = variables["Negative electrode sei interfacial current density"]
-            
-            beta_plating = self.param.beta_Li_plating
-            beta_sei_n = self.param.beta_sei_n
-            
-            deps_n_dt = -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n + beta_plating * j_plating
         
         
         deps_s_dt = pybamm.FullBroadcast(
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index bcc5cd3e71..45db54b315 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -17,8 +17,8 @@ class LeadingOrder(BaseModel):
     **Extends:** :class:`pybamm.porosity.BaseModel`
     """
 
-    def __init__(self, param):
-        super().__init__(param)
+    def __init__(self, param,options):
+        super().__init__(param,options)
 
     def get_fundamental_variables(self):
 
@@ -53,7 +53,7 @@ def get_coupled_variables(self, variables):
             j_plating = variables[
             "X-averaged negative electrode lithium plating interfacial current density"]
         
-            beta_plating = self.param.beta_Li_plating
+            beta_plating = self.param.beta_plating 
             
             deps_n_dt = pybamm.PrimaryBroadcast(
             -self.param.beta_surf_n * j_n + beta_plating * j_plating, ["negative electrode"]
@@ -66,7 +66,7 @@ def get_coupled_variables(self, variables):
             
             j_sei_n = variables["X-averaged negative electrode sei interfacial current density"]
             
-            beta_plating = self.param.beta_Li_plating
+            beta_plating = self.param.beta_plating 
             beta_sei_n = self.param.beta_sei_n
             
             deps_n_dt = pybamm.PrimaryBroadcast(
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index da96cbdaab..2d8de0a399 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -751,6 +751,11 @@ def _set_dimensionless_parameters(self):
         self.Gamma_plating = (self.a_n_typ * self.j_scale_n * self.tau_discharge) / (
             self.F * self.c_Li_typ
         )
+        
+        self.beta_plating = self.Gamma_plating * self.V_bar_plated_Li * self.c_Li_typ
+            
+            
+        
 
         # Initial conditions
         self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity")

From 7d9131440875ca6527e5fb0d97bfbfdc60c09b16 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Thu, 25 Feb 2021 08:29:12 +0000
Subject: [PATCH 06/46] Yang2017 corrections

---
 .../lithium_platings/yang2017_Li_plating/parameters.csv    | 2 +-
 pybamm/models/full_battery_models/lithium_ion/Yang2017.py  | 7 ++++---
 pybamm/models/full_battery_models/lithium_ion/spm.py       | 4 ++--
 pybamm/models/full_battery_models/lithium_ion/spme.py      | 4 ++--
 pybamm/models/submodels/porosity/base_porosity.py          | 2 +-
 .../submodels/porosity/full_reaction_driven_porosity.py    | 1 +
 .../submodels/porosity/leading_reaction_driven_porosity.py | 5 +++--
 7 files changed, 14 insertions(+), 11 deletions(-)

diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
index daaa55b5f0..bf8e28cef6 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
@@ -4,5 +4,5 @@ Name [units],Value,Reference,Notes
 # Lithium plating parameters,,,
 Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
 Lithium plating kinetic rate constant [m.s-1],1.00E+00,,Not required for Yang2017
-Exchange-current density for Lithium plating [A.m-2],[function]plating_exchange_current_density_OKane2020,,
+Exchange-current density for plating [A.m-2],[function]plating_exchange_current_density_OKane2020,,
 Initial plated lithium concentration [mol.m-3],0.00E+00,,
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index 37f0c051f9..8e3735f680 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -3,12 +3,13 @@
 from .dfn import DFN
 
 class Yang2017(DFN):
-   def __init__(self):
+   def __init__(self, options=None, name="Yang2017", build=True):
         options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible", "lithium plating porosity change": "true" }
-        super().__init__(options=options, name="Yang2017")
+        super().__init__(options=options, name=name)
         pybamm.citations.register("Yang2017")
    @property
    def default_parameter_values(self):
        return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
    
- 
\ No newline at end of file
+ 
+    
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index a9c6160090..9594867c0d 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -58,9 +58,9 @@ def __init__(self, options=None, name="Single Particle Model", build=True):
     def set_porosity_submodel(self):
 
          if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
+            self.submodels["porosity"] = pybamm.porosity.Constant(self.param, self.options)
          elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)
+            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param, self.options)
             
 
     def set_active_material_submodel(self):
diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py
index 5b40eef43f..16d2d4e19b 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spme.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spme.py
@@ -60,9 +60,9 @@ def __init__(
     def set_porosity_submodel(self):
 
          if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
+            self.submodels["porosity"] = pybamm.porosity.Constant(self.param, self.options)
          elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
-               self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)     
+               self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param, self.options)     
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/submodels/porosity/base_porosity.py b/pybamm/models/submodels/porosity/base_porosity.py
index cb8f12b734..6c6841e09b 100644
--- a/pybamm/models/submodels/porosity/base_porosity.py
+++ b/pybamm/models/submodels/porosity/base_porosity.py
@@ -16,7 +16,7 @@ class BaseModel(pybamm.BaseSubModel):
     **Extends:** :class:`pybamm.BaseSubModel`
     """
 
-    def __init__(self, param,options):
+    def __init__(self, param, options):
         super().__init__(param)
 
     def _get_standard_porosity_variables(
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index 9ee06c6802..9faaaa6b3f 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -19,6 +19,7 @@ class Full(BaseModel):
 
     def __init__(self, param,options):
         super().__init__(param,options)
+        self.options = options
 
     def get_fundamental_variables(self):
 
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index 45db54b315..5cc267f237 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -17,8 +17,9 @@ class LeadingOrder(BaseModel):
     **Extends:** :class:`pybamm.porosity.BaseModel`
     """
 
-    def __init__(self, param,options):
-        super().__init__(param,options)
+    def __init__(self, param, options):
+        super().__init__(param, options)
+        self.options = options
 
     def get_fundamental_variables(self):
 

From 3cd160980ae03c1a376df78ef1a7d16f7ec76b83 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 26 Feb 2021 10:20:14 +0000
Subject: [PATCH 07/46] Corrections

---
 pybamm/models/full_battery_models/lead_acid/full.py         | 2 +-
 pybamm/models/full_battery_models/lead_acid/higher_order.py | 4 ++--
 pybamm/models/full_battery_models/lead_acid/loqs.py         | 2 +-
 pybamm/parameters/lithium_ion_parameters.py                 | 2 --
 4 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/pybamm/models/full_battery_models/lead_acid/full.py b/pybamm/models/full_battery_models/lead_acid/full.py
index 897e1bf4b9..bc0b14cffe 100644
--- a/pybamm/models/full_battery_models/lead_acid/full.py
+++ b/pybamm/models/full_battery_models/lead_acid/full.py
@@ -54,7 +54,7 @@ def __init__(self, options=None, name="Full model", build=True):
         pybamm.citations.register("Sulzer2019physical")
 
     def set_porosity_submodel(self):
-        self.submodels["porosity"] = pybamm.porosity.Full(self.param)
+        self.submodels["porosity"] = pybamm.porosity.Full(self.param, self.options)
 
     def set_convection_submodel(self):
         if self.options["convection"] == "none":
diff --git a/pybamm/models/full_battery_models/lead_acid/higher_order.py b/pybamm/models/full_battery_models/lead_acid/higher_order.py
index 402f75c5f0..e99ffd0c88 100644
--- a/pybamm/models/full_battery_models/lead_acid/higher_order.py
+++ b/pybamm/models/full_battery_models/lead_acid/higher_order.py
@@ -198,7 +198,7 @@ def set_full_porosity_submodel(self):
         Update porosity submodel, now that we have the spatially heterogeneous
         interfacial current densities
         """
-        self.submodels["full porosity"] = pybamm.porosity.Full(self.param)
+        self.submodels["full porosity"] = pybamm.porosity.Full(self.param, self.options)
 
 
 class FOQS(BaseHigherOrderModel):
@@ -269,7 +269,7 @@ def set_full_porosity_submodel(self):
         Update porosity submodel, now that we have the spatially heterogeneous
         interfacial current densities
         """
-        self.submodels["full porosity"] = pybamm.porosity.Full(self.param)
+        self.submodels["full porosity"] = pybamm.porosity.Full(self.param, self.options)
 
 
 class CompositeExtended(Composite):
diff --git a/pybamm/models/full_battery_models/lead_acid/loqs.py b/pybamm/models/full_battery_models/lead_acid/loqs.py
index 83dab70e46..4036afc536 100644
--- a/pybamm/models/full_battery_models/lead_acid/loqs.py
+++ b/pybamm/models/full_battery_models/lead_acid/loqs.py
@@ -96,7 +96,7 @@ def set_current_collector_submodel(self):
     def set_porosity_submodel(self):
 
         self.submodels["leading-order porosity"] = pybamm.porosity.LeadingOrder(
-            self.param
+            self.param, self.options
         )
 
     def set_tortuosity_submodels(self):
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 2d8de0a399..a6c8c1e064 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -755,8 +755,6 @@ def _set_dimensionless_parameters(self):
         self.beta_plating = self.Gamma_plating * self.V_bar_plated_Li * self.c_Li_typ
             
             
-        
-
         # Initial conditions
         self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity")
         self.epsilon_s_init = pybamm.Parameter("Separator porosity")

From a14bf327920c9bd694ea3412c721c845e3fb3e11 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 26 Feb 2021 13:51:10 +0000
Subject: [PATCH 08/46] Formatted

---
 pybamm/citations.py                           |  4 +-
 pybamm/geometry/half_cell_geometry.py         |  4 +-
 ...ting_exchange_current_density_OKane2020.py |  6 +-
 .../graphite_LGM50_diffusivity_Chen2020.py    | 38 ++++-----
 ...ting_exchange_current_density_OKane2020.py |  6 +-
 .../full_battery_models/base_battery_model.py |  3 +-
 .../lithium_ion/Yang2017.py                   | 22 +++--
 .../lithium_ion/basic_dfn_half_cell.py        |  5 +-
 .../full_battery_models/lithium_ion/dfn.py    | 19 +++--
 .../full_battery_models/lithium_ion/spm.py    | 19 +++--
 .../full_battery_models/lithium_ion/spme.py   | 18 +++-
 .../interface/lithium_plating/base_plating.py |  2 +-
 .../lithium_plating/irreversible_plating.py   |  2 +-
 .../lithium_plating/reversible_plating.py     |  2 +-
 .../porosity/full_reaction_driven_porosity.py | 37 ++++-----
 .../leading_reaction_driven_porosity.py       | 82 +++++++++++--------
 pybamm/parameters/lithium_ion_parameters.py   |  7 +-
 pybamm/parameters/parameter_sets.py           |  1 -
 pybamm/parameters/parameter_values.py         |  5 +-
 pybamm/solvers/jax_bdf_solver.py              |  7 +-
 20 files changed, 165 insertions(+), 124 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index 04b21dea58..9ed6cc9084 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -99,9 +99,7 @@ def print(self, filename=None, output_format="text"):
         else:
             raise pybamm.OptionError(
                 "Output format {} not recognised."
-                "It should be 'text' or 'bibtex'.".format(
-                    output_format
-                )
+                "It should be 'text' or 'bibtex'.".format(output_format)
             )
 
         if filename is None:
diff --git a/pybamm/geometry/half_cell_geometry.py b/pybamm/geometry/half_cell_geometry.py
index 3c4b0f8ee6..c8998cacc6 100644
--- a/pybamm/geometry/half_cell_geometry.py
+++ b/pybamm/geometry/half_cell_geometry.py
@@ -47,9 +47,7 @@ def half_cell_geometry(
     }
     # Add particle domains
     if include_particles is True:
-        geometry.update(
-            {"working particle": {var.r_w: {"min": 0, "max": 1}}}
-        )
+        geometry.update({"working particle": {var.r_w: {"min": 0, "max": 1}}})
 
     if current_collector_dimension == 0:
         geometry["current collector"] = {var.z: {"position": 1}}
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
index a3d97cc84e..16fc2a76bc 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -1,4 +1,4 @@
-from pybamm import constants, Parameter,Scalar
+from pybamm import constants, Parameter, Scalar
 
 
 def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -23,5 +23,5 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
     :class:`pybamm.Symbol`
         Exchange-current density [A.m-2]
     """
-   # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
-    return Scalar(0.001)
\ No newline at end of file
+    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    return Scalar(0.001)
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py
index 7e39123d1c..1d636463af 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py
@@ -3,28 +3,28 @@
 
 def graphite_LGM50_diffusivity_Chen2020(sto, T):
     """
-      LG M50 Graphite diffusivity as a function of stochiometry, in this case the
-      diffusivity is taken to be a constant. The value is taken from [1].
+    LG M50 Graphite diffusivity as a function of stochiometry, in this case the
+    diffusivity is taken to be a constant. The value is taken from [1].
 
-      References
-      ----------
-      .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
-      Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
-      Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the
-      Electrochemical Society 167 (2020): 080534.
+    References
+    ----------
+    .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
+    Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
+    Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the
+    Electrochemical Society 167 (2020): 080534.
 
-      Parameters
-      ----------
-      sto: :class:`pybamm.Symbol`
-         Electrode stochiometry
-      T: :class:`pybamm.Symbol`
-         Dimensional temperature
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+       Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+       Dimensional temperature
 
-      Returns
-      -------
-      :class:`pybamm.Symbol`
-         Solid diffusivity
-   """
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+       Solid diffusivity
+    """
 
     D_ref = 3.3e-14
     E_D_s = 3.03e4
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
index a3d97cc84e..16fc2a76bc 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
@@ -1,4 +1,4 @@
-from pybamm import constants, Parameter,Scalar
+from pybamm import constants, Parameter, Scalar
 
 
 def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -23,5 +23,5 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
     :class:`pybamm.Symbol`
         Exchange-current density [A.m-2]
     """
-   # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
-    return Scalar(0.001)
\ No newline at end of file
+    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    return Scalar(0.001)
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 419e27e855..4038ece17f 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -400,8 +400,7 @@ def options(self, extra_options):
             raise pybamm.OptionError(
                 "Unknown lithium plating model '{}'".format(options["lithium plating"])
             )
-            
-            
+
         if options["lithium plating porosity change"] not in ["true", "false"]:
             if options["lithium plating porosity change"] in [True, False]:
                 raise pybamm.OptionError(
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index 8e3735f680..a2fe692253 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -1,15 +1,19 @@
-
 import pybamm
 from .dfn import DFN
 
+
 class Yang2017(DFN):
-   def __init__(self, options=None, name="Yang2017", build=True):
-        options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible", "lithium plating porosity change": "true" }
+    def __init__(self, options=None, name="Yang2017", build=True):
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
         super().__init__(options=options, name=name)
         pybamm.citations.register("Yang2017")
-   @property
-   def default_parameter_values(self):
-       return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
-   
- 
-    
+
+    @property
+    def default_parameter_values(self):
+        return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
index 3c672ea090..9bfdb6fcaf 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
@@ -222,7 +222,10 @@ def __init__(self, name="Doyle-Fuller-Newman half cell model", options=None):
         # derivatives
         self.boundary_conditions[c_s_w] = {
             "left": (pybamm.Scalar(0), "Neumann"),
-            "right": (-C_w * j_w / a_R_w / gamma_w / D_w(c_s_surf_w, T), "Neumann",),
+            "right": (
+                -C_w * j_w / a_R_w / gamma_w / D_w(c_s_surf_w, T),
+                "Neumann",
+            ),
         }
 
         # c_w_init can in general be a function of x
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index 0e5281e354..603210d652 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -56,12 +56,19 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman model", build=True):
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param,self.options)
-        elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.Full(self.param,self.options)
-         
-     
+        if (
+            self.options["sei porosity change"] == "false"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Constant(
+                self.param, self.options
+            )
+        elif (
+            self.options["sei porosity change"] == "true"
+            or self.options["lithium plating porosity change"] == "true"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Full(self.param, self.options)
+
     def set_active_material_submodel(self):
 
         if self.options["loss of active material"] == "none":
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index 9594867c0d..daf92ae56a 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -57,11 +57,20 @@ def __init__(self, options=None, name="Single Particle Model", build=True):
 
     def set_porosity_submodel(self):
 
-         if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param, self.options)
-         elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param, self.options)
-            
+        if (
+            self.options["sei porosity change"] == "false"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Constant(
+                self.param, self.options
+            )
+        elif (
+            self.options["sei porosity change"] == "true"
+            or self.options["lithium plating porosity change"] == "true"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(
+                self.param, self.options
+            )
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py
index 16d2d4e19b..9736387ce6 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spme.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spme.py
@@ -59,10 +59,20 @@ def __init__(
 
     def set_porosity_submodel(self):
 
-         if self.options["sei porosity change"] == "false" and self.options["lithium plating porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param, self.options)
-         elif self.options["sei porosity change"] == "true" or self.options["lithium plating porosity change"] == "true":
-               self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param, self.options)     
+        if (
+            self.options["sei porosity change"] == "false"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Constant(
+                self.param, self.options
+            )
+        elif (
+            self.options["sei porosity change"] == "true"
+            or self.options["lithium plating porosity change"] == "true"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(
+                self.param, self.options
+            )
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
index 4359481a96..5503ad05a3 100644
--- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
@@ -117,4 +117,4 @@ def _get_standard_reaction_variables(self, j_stripping):
             f"interfacial current density [A.m-2]": j_stripping_av * j_scale,
         }
 
-        return variables
\ No newline at end of file
+        return variables
diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index ef7c493740..94d0668885 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -88,4 +88,4 @@ def set_initial_conditions(self, variables):
         ]
         c_plated_Li_0 = self.param.c_plated_Li_0
 
-        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
\ No newline at end of file
+        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
diff --git a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
index 9513ed8d4a..ce9c95bbc3 100644
--- a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
@@ -88,4 +88,4 @@ def set_initial_conditions(self, variables):
         ]
         c_plated_Li_0 = self.param.c_plated_Li_0
 
-        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
\ No newline at end of file
+        self.initial_conditions = {c_plated_Li: c_plated_Li_0}
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index 9faaaa6b3f..5b83812e2a 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -17,8 +17,8 @@ class Full(BaseModel):
     **Extends:** :class:`pybamm.porosity.BaseModel`
     """
 
-    def __init__(self, param,options):
-        super().__init__(param,options)
+    def __init__(self, param, options):
+        super().__init__(param, options)
         self.options = options
 
     def get_fundamental_variables(self):
@@ -34,32 +34,27 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["Negative electrode interfacial current density"]
         j_p = variables["Positive electrode interfacial current density"]
-        deps_n_dt = -self.param.beta_surf_n *  j_n
-       
-        
+        deps_n_dt = -self.param.beta_surf_n * j_n
+
         if self.options["sei porosity change"] == "true":
-            
-             j_sei_n = variables["Negative electrode sei interfacial current density"]
-             beta_sei_n = self.param.beta_sei_n
-             deps_n_dt +=  beta_sei_n * j_sei_n
-             
+
+            j_sei_n = variables["Negative electrode sei interfacial current density"]
+            beta_sei_n = self.param.beta_sei_n
+            deps_n_dt += beta_sei_n * j_sei_n
+
         elif self.options["lithium plating porosity change"] == "true":
-            
+
             j_plating = variables[
-            "Negative electrode lithium plating interfacial current density"
-        ]
-            beta_plating = self.param.beta_plating 
-            
+                "Negative electrode lithium plating interfacial current density"
+            ]
+            beta_plating = self.param.beta_plating
+
             deps_n_dt += beta_plating * j_plating
-            
-        
-        
+
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
-        
-        
-       
+
         deps_p_dt = -self.param.beta_surf_p * j_p
 
         variables.update(
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index 5cc267f237..a5728ddbe5 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -38,43 +38,61 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["X-averaged negative electrode interfacial current density"]
         j_p = variables["X-averaged positive electrode interfacial current density"]
-       
-           
-        if self.options["sei porosity change"] == "true" and self.options["lithium plating porosity change"] == "false":
-            
-             j_sei_n = variables["X-averaged negative electrode sei interfacial current density"]
-               
-             beta_sei_n = self.param.beta_sei_n
-             deps_n_dt = pybamm.PrimaryBroadcast(
-            -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n, ["negative electrode"]
-        )
-             
-        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "false": 
-            
+
+        if (
+            self.options["sei porosity change"] == "true"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+
+            j_sei_n = variables[
+                "X-averaged negative electrode sei interfacial current density"
+            ]
+
+            beta_sei_n = self.param.beta_sei_n
+            deps_n_dt = pybamm.PrimaryBroadcast(
+                -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n,
+                ["negative electrode"],
+            )
+
+        elif (
+            self.options["lithium plating porosity change"] == "true"
+            and self.options["sei porosity change"] == "false"
+        ):
+
             j_plating = variables[
-            "X-averaged negative electrode lithium plating interfacial current density"]
-        
-            beta_plating = self.param.beta_plating 
-            
+                "X-averaged negative electrode lithium plating interfacial current density"
+            ]
+
+            beta_plating = self.param.beta_plating
+
             deps_n_dt = pybamm.PrimaryBroadcast(
-            -self.param.beta_surf_n * j_n + beta_plating * j_plating, ["negative electrode"]
-        )
-            
-        elif self.options["lithium plating porosity change"] == "true" and self.options["sei porosity change"] == "true":
-           
+                -self.param.beta_surf_n * j_n + beta_plating * j_plating,
+                ["negative electrode"],
+            )
+
+        elif (
+            self.options["lithium plating porosity change"] == "true"
+            and self.options["sei porosity change"] == "true"
+        ):
+
             j_plating = variables[
-            "X-averaged negative electrode lithium plating interfacial current density"]
-            
-            j_sei_n = variables["X-averaged negative electrode sei interfacial current density"]
-            
-            beta_plating = self.param.beta_plating 
+                "X-averaged negative electrode lithium plating interfacial current density"
+            ]
+
+            j_sei_n = variables[
+                "X-averaged negative electrode sei interfacial current density"
+            ]
+
+            beta_plating = self.param.beta_plating
             beta_sei_n = self.param.beta_sei_n
-            
+
             deps_n_dt = pybamm.PrimaryBroadcast(
-            -self.param.beta_surf_n * j_n + beta_plating * j_plating + beta_sei_n * j_sei_n, ["negative electrode"]
-        )
-        
-        
+                -self.param.beta_surf_n * j_n
+                + beta_plating * j_plating
+                + beta_sei_n * j_sei_n,
+                ["negative electrode"],
+            )
+
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index a6c8c1e064..962bfaab3b 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -751,10 +751,9 @@ def _set_dimensionless_parameters(self):
         self.Gamma_plating = (self.a_n_typ * self.j_scale_n * self.tau_discharge) / (
             self.F * self.c_Li_typ
         )
-        
+
         self.beta_plating = self.Gamma_plating * self.V_bar_plated_Li * self.c_Li_typ
-            
-            
+
         # Initial conditions
         self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity")
         self.epsilon_s_init = pybamm.Parameter("Separator porosity")
@@ -1026,4 +1025,4 @@ def options(self, extra_options):
                     options["particle cracking"]
                 )
             )
-        self._options = options
\ No newline at end of file
+        self._options = options
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 6051fe9a63..35b972b2dd 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -103,7 +103,6 @@
 }
 
 
-
 Marquis2019 = {
     "chemistry": "lithium-ion",
     "cell": "kokam_Marquis2019",
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 90d045b46f..a075be5474 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -152,11 +152,10 @@ def update_from_chemistry(self, chemistry):
         # add sei parameters if provided
         if "sei" in chemistry:
             component_groups += ["sei"]
-        
-         # add lithium plating parameters if provided
+
+        # add lithium plating parameters if provided
         if "lithium plating" in chemistry:
             component_groups += ["lithium plating"]
-       
 
         if "anode" in chemistry.keys():
             if "negative electrode" in chemistry.keys():
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index baff2ad883..9ee55791b8 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -715,7 +715,10 @@ def block_fun(i, j, Ai, Aj):
             return Ai
         else:
             return onp.zeros(
-                (Ai.shape[0] if Ai.ndim > 1 else 1, Aj.shape[1] if Aj.ndim > 1 else 1,),
+                (
+                    Ai.shape[0] if Ai.ndim > 1 else 1,
+                    Aj.shape[1] if Aj.ndim > 1 else 1,
+                ),
                 dtype=Ai.dtype,
             )
 
@@ -944,7 +947,7 @@ def scan_fun(carry, i):
             *args,
             mass=aug_mass,
             rtol=rtol,
-            atol=atol
+            atol=atol,
         )
         y_bar, t0_bar, args_bar = tree_map(op.itemgetter(1), (y_bar, t0_bar, args_bar))
         # Add gradient from current output

From e53004b741244ed12817d1792e27763883859b74 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 26 Feb 2021 16:50:18 +0000
Subject: [PATCH 09/46] Spacing corrections

---
 pybamm/expression_tree/symbol.py              |  3 +-
 ...ting_exchange_current_density_OKane2020.py |  3 +-
 ...ting_exchange_current_density_OKane2020.py |  3 +-
 .../full_battery_models/base_battery_model.py |  2 +-
 .../porosity/full_reaction_driven_porosity.py |  2 +-
 .../leading_reaction_driven_porosity.py       | 46 +++----------------
 pybamm/plotting/quick_plot.py                 |  3 +-
 pybamm/solvers/solution.py                    |  3 +-
 pybamm/util.py                                |  3 +-
 9 files changed, 15 insertions(+), 53 deletions(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 127118a6fc..65e5f93f78 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -610,8 +610,7 @@ def diff(self, variable):
             return pybamm.Scalar(0)
 
     def _diff(self, variable):
-        """Default behaviour for differentiation, overriden by Binary and Unary Operators
-        """
+        """Default behaviour for differentiation, overriden by Binary and Unary Operators"""
         raise NotImplementedError
 
     def jac(self, variable, known_jacs=None, clear_domain=True):
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
index 16fc2a76bc..ff675d619a 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -1,4 +1,4 @@
-from pybamm import constants, Parameter, Scalar
+from pybamm import Scalar
 
 
 def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -23,5 +23,4 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
     :class:`pybamm.Symbol`
         Exchange-current density [A.m-2]
     """
-    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
     return Scalar(0.001)
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
index 16fc2a76bc..ff675d619a 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
@@ -1,4 +1,4 @@
-from pybamm import constants, Parameter, Scalar
+from pybamm import Scalar
 
 
 def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -23,5 +23,4 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
     :class:`pybamm.Symbol`
         Exchange-current density [A.m-2]
     """
-    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
     return Scalar(0.001)
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index adbd1415d6..101d313810 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -404,7 +404,7 @@ def options(self, extra_options):
         if options["lithium plating porosity change"] not in ["true", "false"]:
             if options["lithium plating porosity change"] in [True, False]:
                 raise pybamm.OptionError(
-                    "lithium plating porosity change must now be given in string format "
+                    "lithium plating porosity change must now be given in string format"
                     "('true' or 'false')"
                 )
             raise pybamm.OptionError(
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index 5b83812e2a..3778e2126a 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -42,7 +42,7 @@ def get_coupled_variables(self, variables):
             beta_sei_n = self.param.beta_sei_n
             deps_n_dt += beta_sei_n * j_sei_n
 
-        elif self.options["lithium plating porosity change"] == "true":
+        if self.options["lithium plating porosity change"] == "true":
 
             j_plating = variables[
                 "Negative electrode lithium plating interfacial current density"
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index a5728ddbe5..70fc0706b3 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -38,26 +38,20 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["X-averaged negative electrode interfacial current density"]
         j_p = variables["X-averaged positive electrode interfacial current density"]
+        deps_n_dt = pybamm.PrimaryBroadcast(
+            -self.param.beta_surf_n * j_n, ["negative electrode"]
+        )
 
-        if (
-            self.options["sei porosity change"] == "true"
-            and self.options["lithium plating porosity change"] == "false"
-        ):
+        if self.options["sei porosity change"] == "true":
 
             j_sei_n = variables[
                 "X-averaged negative electrode sei interfacial current density"
             ]
 
             beta_sei_n = self.param.beta_sei_n
-            deps_n_dt = pybamm.PrimaryBroadcast(
-                -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n,
-                ["negative electrode"],
-            )
+            deps_n_dt += j_sei_n * beta_sei_n
 
-        elif (
-            self.options["lithium plating porosity change"] == "true"
-            and self.options["sei porosity change"] == "false"
-        ):
+        if self.options["lithium plating porosity change"] == "true":
 
             j_plating = variables[
                 "X-averaged negative electrode lithium plating interfacial current density"
@@ -65,33 +59,7 @@ def get_coupled_variables(self, variables):
 
             beta_plating = self.param.beta_plating
 
-            deps_n_dt = pybamm.PrimaryBroadcast(
-                -self.param.beta_surf_n * j_n + beta_plating * j_plating,
-                ["negative electrode"],
-            )
-
-        elif (
-            self.options["lithium plating porosity change"] == "true"
-            and self.options["sei porosity change"] == "true"
-        ):
-
-            j_plating = variables[
-                "X-averaged negative electrode lithium plating interfacial current density"
-            ]
-
-            j_sei_n = variables[
-                "X-averaged negative electrode sei interfacial current density"
-            ]
-
-            beta_plating = self.param.beta_plating
-            beta_sei_n = self.param.beta_sei_n
-
-            deps_n_dt = pybamm.PrimaryBroadcast(
-                -self.param.beta_surf_n * j_n
-                + beta_plating * j_plating
-                + beta_sei_n * j_sei_n,
-                ["negative electrode"],
-            )
+            deps_n_dt += beta_plating * j_plating
 
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 14ba189863..69ed1e76ac 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -7,8 +7,7 @@
 
 
 class LoopList(list):
-    """A list which loops over itself when accessing an index so that it never runs out
-    """
+    """A list which loops over itself when accessing an index so that it never runs out"""
 
     def __getitem__(self, i):
         # implement looping by calling "(i) modulo (length of list)"
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index d295f79b1d..a98ae50256 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -385,8 +385,7 @@ def save_data(self, filename, variables=None, to_format="pickle", short_names=No
 
     @property
     def sub_solutions(self):
-        """List of sub solutions that have been concatenated to form the full solution
-        """
+        """List of sub solutions that have been concatenated to form the full solution"""
         return self._sub_solutions
 
     def __add__(self, other):
diff --git a/pybamm/util.py b/pybamm/util.py
index 8a45488219..6055559d0f 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -305,8 +305,7 @@ def load_function(filename):
 
 
 def rmse(x, y):
-    """Calculate the root-mean-square-error between two vectors x and y, ignoring NaNs
-    """
+    """Calculate the root-mean-square-error between two vectors x and y, ignoring NaNs"""
     # Check lengths
     if len(x) != len(y):
         raise ValueError("Vectors must have the same length")

From 2e9adfbaabb2cd153509c559f58225c8000a2c7a Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 26 Feb 2021 20:55:09 +0000
Subject: [PATCH 10/46] Spacing corrections

---
 pybamm/expression_tree/symbol.py                               | 3 ++-
 .../submodels/porosity/leading_reaction_driven_porosity.py     | 3 ++-
 pybamm/plotting/quick_plot.py                                  | 3 ++-
 pybamm/solvers/solution.py                                     | 3 ++-
 pybamm/util.py                                                 | 3 ++-
 5 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 65e5f93f78..44313a0a8c 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -610,7 +610,8 @@ def diff(self, variable):
             return pybamm.Scalar(0)
 
     def _diff(self, variable):
-        """Default behaviour for differentiation, overriden by Binary and Unary Operators"""
+        """Default behaviour for differentiation, overriden by Binary
+        and Unary Operators"""
         raise NotImplementedError
 
     def jac(self, variable, known_jacs=None, clear_domain=True):
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index 70fc0706b3..4cd06a6378 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -54,7 +54,8 @@ def get_coupled_variables(self, variables):
         if self.options["lithium plating porosity change"] == "true":
 
             j_plating = variables[
-                "X-averaged negative electrode lithium plating interfacial current density"
+                "X-averaged negative electrode lithium \
+                plating interfacial current density"
             ]
 
             beta_plating = self.param.beta_plating
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 69ed1e76ac..42c2401a39 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -7,7 +7,8 @@
 
 
 class LoopList(list):
-    """A list which loops over itself when accessing an index so that it never runs out"""
+    """A list which loops over itself when accessing an
+    index so that it never runs out"""
 
     def __getitem__(self, i):
         # implement looping by calling "(i) modulo (length of list)"
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index a98ae50256..34faf0cf54 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -385,7 +385,8 @@ def save_data(self, filename, variables=None, to_format="pickle", short_names=No
 
     @property
     def sub_solutions(self):
-        """List of sub solutions that have been concatenated to form the full solution"""
+        """List of sub solutions that have been
+        concatenated to form the full solution"""
         return self._sub_solutions
 
     def __add__(self, other):
diff --git a/pybamm/util.py b/pybamm/util.py
index 6055559d0f..c617d91d07 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -305,7 +305,8 @@ def load_function(filename):
 
 
 def rmse(x, y):
-    """Calculate the root-mean-square-error between two vectors x and y, ignoring NaNs"""
+    """Calculate the root-mean-square-error between
+    two vectors x and y, ignoring NaNs"""
     # Check lengths
     if len(x) != len(y):
         raise ValueError("Vectors must have the same length")

From 04a3a3518245be57c842ac790bb5bed462d6b638 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 1 Mar 2021 18:26:44 +0000
Subject: [PATCH 11/46] Tests

---
 .../test_porosity/test_base_porosity.py               |  2 +-
 .../test_porosity/test_constant_porosity.py           |  9 ++++++++-
 .../test_full_reaction_driven_porosity.py             | 10 +++++++++-
 .../test_leading_reaction_driven_porosity.py          | 11 ++++++++++-
 4 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py
index 4025bde773..193d96133f 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py
@@ -11,7 +11,7 @@ class TestBaseModel(unittest.TestCase):
     def test_public_functions(self):
         a = pybamm.Scalar(0)
         variables = {"Negative electrode porosity": a, "Positive electrode porosity": a}
-        submodel = pybamm.porosity.BaseModel(None)
+        submodel = pybamm.porosity.BaseModel(None, None)
         std_tests = tests.StandardSubModelTests(submodel, variables)
         std_tests.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py
index 7e19a8250b..7b6347caed 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py
@@ -10,7 +10,14 @@
 class TestConstantPorosity(unittest.TestCase):
     def test_public_functions(self):
         param = pybamm.LithiumIonParameters()
-        submodel = pybamm.porosity.Constant(param)
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        submodel = pybamm.porosity.Constant(param, options)
         std_tests = tests.StandardSubModelTests(submodel)
         std_tests.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
index b95e76a693..6a67108b0b 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
@@ -12,12 +12,20 @@ def test_public_functions(self):
         param = pybamm.LeadAcidParameters()
         a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
         a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"])
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
         variables = {
             "Negative electrode interfacial current density": a_n,
             "Negative electrode sei interfacial current density": a_n,
             "Positive electrode interfacial current density": a_p,
+            "Negative electrode lithium plating interfacial current density": a_n,
         }
-        submodel = pybamm.porosity.Full(param)
+        submodel = pybamm.porosity.Full(param, options)
         std_tests = tests.StandardSubModelTests(submodel, variables)
         std_tests.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
index a2b0cb0b3e..c4cf0e7e62 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
@@ -15,8 +15,17 @@ def test_public_functions(self):
             "X-averaged negative electrode interfacial current density": a,
             "X-averaged negative electrode sei interfacial current density": a,
             "X-averaged positive electrode interfacial current density": a,
+            "X-averaged negative electrode lithium \
+                plating interfacial current density": a,
         }
-        submodel = pybamm.porosity.LeadingOrder(param)
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        submodel = pybamm.porosity.LeadingOrder(param, options)
         std_tests = tests.StandardSubModelTests(submodel, variables)
         std_tests.test_all()
 

From 79298fbd47826ecfac562c0e862b8c390efd1455 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 8 Mar 2021 10:06:02 +0000
Subject: [PATCH 12/46] Tests

---
 .../interface/lithium_plating/irreversible_plating.py         | 2 +-
 .../submodels/porosity/leading_reaction_driven_porosity.py    | 4 ++--
 pybamm/plotting/quick_plot.py                                 | 1 +
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index da780f6126..5707b17a2f 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -48,7 +48,7 @@ def get_coupled_variables(self, variables):
         ]
         j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
         eta_sei = variables[f"{self.domain} electrode SEI film overpotential"]
-        C_plating = param.C_plating
+        #C_plating = param.C_plating
         phi_ref = param.U_n_ref / param.potential_scale
 
         # need to revise for thermal case
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index f1be35bbee..8b7c337087 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -59,8 +59,8 @@ def get_coupled_variables(self, variables):
         if self.options["lithium plating porosity change"] == "true":
 
             j_plating = variables[
-                "X-averaged negative electrode lithium \
-                plating interfacial current density"
+                "X-averaged negative electrode lithium"
+                "plating interfacial current density"
             ]
 
             beta_plating = self.param.beta_plating
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 86536cd30f..fc4696b08a 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -10,6 +10,7 @@ class LoopList(list):
 
     """A list which loops over itself when accessing an
     index so that it never runs out"""
+
     """
     A list which loops over itself when accessing an index so that it never runs out
     """

From d660020b025a9963a104b273a4ad77c3ee5bae1f Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 8 Mar 2021 14:42:09 +0000
Subject: [PATCH 13/46] Changes for tests

---
 pybamm/CITATIONS.txt                          |  14 ++
 pybamm/citations.py                           |   4 +-
 pybamm/geometry/half_cell_geometry.py         |   4 +-
 .../yang2017_Li_plating/README.md             |  11 ++
 .../yang2017_Li_plating/parameters.csv        |   8 ++
 ...ting_exchange_current_density_OKane2020.py |  28 ++++
 .../graphite_LGM50_diffusivity_Chen2020.py    |  38 +++---
 .../graphite_Yang2017/README.md               |  12 ++
 .../graphite_diffusivity_Ecker2015.py         |  37 +++++
 ...lyte_exchange_current_density_Ecker2015.py |  48 +++++++
 .../graphite_ocp_Ecker2015.csv                |  41 ++++++
 .../graphite_ocp_Ecker2015_function.py        |  62 +++++++++
 ...easured_graphite_diffusivity_Ecker2015.csv |  23 ++++
 .../graphite_Yang2017/parameters.csv          |  25 ++++
 ...ting_exchange_current_density_OKane2020.py |  27 ++++
 .../lithium-ion/seis/yang2017_sei/README.md   |  11 ++
 .../seis/yang2017_sei/parameters.csv          |  23 ++++
 .../full_battery_models/base_battery_model.py |  14 ++
 .../full_battery_models/lead_acid/full.py     |   2 +-
 .../lead_acid/higher_order.py                 |   4 +-
 .../full_battery_models/lead_acid/loqs.py     |   2 +-
 .../lithium_ion/Yang2017.py                   |  42 ++++--
 .../lithium_ion/__init__.py                   |   1 +
 .../lithium_ion/basic_dfn_half_cell.py        |   5 +-
 .../full_battery_models/lithium_ion/dfn.py    |  16 ++-
 .../full_battery_models/lithium_ion/spm.py    |  18 ++-
 .../full_battery_models/lithium_ion/spme.py   |  18 ++-
 .../interface/lithium_plating/base_plating.py |  13 +-
 .../lithium_plating/irreversible_plating.py   |  18 ++-
 .../lithium_plating/reversible_plating.py     |  17 +--
 .../submodels/porosity/base_porosity.py       |   2 +-
 .../porosity/full_reaction_driven_porosity.py |  25 +++-
 .../leading_reaction_driven_porosity.py       |  33 +++--
 pybamm/parameters/lithium_ion_parameters.py   | 129 +++++++++++-------
 pybamm/parameters/parameter_sets.py           |  14 ++
 pybamm/parameters/parameter_values.py         |   4 +
 pybamm/solvers/jax_bdf_solver.py              |   7 +-
 python                                        |   0
 .../test_lithium_ion/test_dfn.py              |   9 ++
 .../test_lithium_ion/test_spm.py              |   8 ++
 .../test_lithium_ion/test_spme.py             |   8 ++
 .../test_lithium_ion/test_yang2017.py         |  19 +++
 .../test_lithium_ion/test_dfn.py              |  16 +++
 .../test_lithium_ion/test_spm.py              |   8 ++
 .../test_lithium_ion/test_spme.py             |   8 ++
 .../test_porosity/test_base_porosity.py       |   2 +-
 .../test_porosity/test_constant_porosity.py   |   9 +-
 .../test_full_reaction_driven_porosity.py     |  11 +-
 .../test_leading_reaction_driven_porosity.py  |  11 +-
 .../test_lead_acid_parameters.py              |   2 +-
 .../test_lithium_ion_parameters.py            |   2 +-
 .../test_parameters/test_parameter_values.py  |   5 +-
 52 files changed, 768 insertions(+), 150 deletions(-)
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv
 create mode 100644 python
 create mode 100644 tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py

diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt
index 4190938539..0a700d6191 100644
--- a/pybamm/CITATIONS.txt
+++ b/pybamm/CITATIONS.txt
@@ -347,3 +347,17 @@ doi={10.1149/2.0661810jes}
   publisher = {Elsevier Science (USA)},
   doi = {10.1006/jcph.2002.7041},
 }
+
+
+@article{Yang2017,
+  author = {Yang, Xiao-Guang and Leng, Yungjun and Zhang, Guangsheng
+            and Ge, Shanhai and Wang, Chao-Yang},
+  title = {Modeling of lithium plating induced aging of lithium-ion batteries: 
+  Transition from linear to nonlinear aging},
+  journal = {Power Sources},
+  volume = {360},
+  pages = {28--40},
+  year = {2017},
+  publisher = {Elsevier},
+  url = https://doi.org/10.1016/j.jpowsour.2017.05.110
+}
\ No newline at end of file
diff --git a/pybamm/citations.py b/pybamm/citations.py
index 04b21dea58..9ed6cc9084 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -99,9 +99,7 @@ def print(self, filename=None, output_format="text"):
         else:
             raise pybamm.OptionError(
                 "Output format {} not recognised."
-                "It should be 'text' or 'bibtex'.".format(
-                    output_format
-                )
+                "It should be 'text' or 'bibtex'.".format(output_format)
             )
 
         if filename is None:
diff --git a/pybamm/geometry/half_cell_geometry.py b/pybamm/geometry/half_cell_geometry.py
index 3c4b0f8ee6..c8998cacc6 100644
--- a/pybamm/geometry/half_cell_geometry.py
+++ b/pybamm/geometry/half_cell_geometry.py
@@ -47,9 +47,7 @@ def half_cell_geometry(
     }
     # Add particle domains
     if include_particles is True:
-        geometry.update(
-            {"working particle": {var.r_w: {"min": 0, "max": 1}}}
-        )
+        geometry.update({"working particle": {var.r_w: {"min": 0, "max": 1}}})
 
     if current_collector_dimension == 0:
         geometry["current collector"] = {var.z: {"position": 1}}
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/README.md b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/README.md
new file mode 100644
index 0000000000..3c63aa585e
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/README.md
@@ -0,0 +1,11 @@
+# SEI parameters
+
+Some example parameters for SEI growth from the papers:
+
+> Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004). Development of first principles capacity fade model for Li-ion cells. Journal of the Electrochemical Society, 151(2), A196-A203.
+> Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for aging of lithium-ion battery cells. Journal of The Electrochemical Society, 151(3), A456-A462.
+> Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of continued growth of the solid–electrolyte interphase. ChemSusChem, 11(12), 1950-1955.
+> Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society, 156(3), 
+> Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40.
+
+Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit SEI models to observed experimental data in the referenced papers.
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
new file mode 100644
index 0000000000..bf8e28cef6
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
@@ -0,0 +1,8 @@
+Name [units],Value,Reference,Notes
+,,,
+,,,
+# Lithium plating parameters,,,
+Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
+Lithium plating kinetic rate constant [m.s-1],1.00E+00,,Not required for Yang2017
+Exchange-current density for plating [A.m-2],[function]plating_exchange_current_density_OKane2020,,
+Initial plated lithium concentration [mol.m-3],0.00E+00,,
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
new file mode 100644
index 0000000000..c8cdba6130
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -0,0 +1,28 @@
+from pybamm import Scalar
+
+
+def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li plating reaction [A.m-2].
+    References
+    ----------
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    # "Exchange-current density for plating [A.m-2]"
+    return Scalar(0.001)
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py
index 7e39123d1c..1d636463af 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py
@@ -3,28 +3,28 @@
 
 def graphite_LGM50_diffusivity_Chen2020(sto, T):
     """
-      LG M50 Graphite diffusivity as a function of stochiometry, in this case the
-      diffusivity is taken to be a constant. The value is taken from [1].
+    LG M50 Graphite diffusivity as a function of stochiometry, in this case the
+    diffusivity is taken to be a constant. The value is taken from [1].
 
-      References
-      ----------
-      .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
-      Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
-      Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the
-      Electrochemical Society 167 (2020): 080534.
+    References
+    ----------
+    .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
+    Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
+    Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the
+    Electrochemical Society 167 (2020): 080534.
 
-      Parameters
-      ----------
-      sto: :class:`pybamm.Symbol`
-         Electrode stochiometry
-      T: :class:`pybamm.Symbol`
-         Dimensional temperature
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+       Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+       Dimensional temperature
 
-      Returns
-      -------
-      :class:`pybamm.Symbol`
-         Solid diffusivity
-   """
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+       Solid diffusivity
+    """
 
     D_ref = 3.3e-14
     E_D_s = 3.03e4
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
new file mode 100644
index 0000000000..bf092e7d95
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
@@ -0,0 +1,12 @@
+# Graphite negative electrode parameters
+
+Parameters for a graphite negative electrode, from the papers:
+
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery I. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
+
+>Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+
+The fits to data for the electrode and electrolyte properties are those provided
+by Dr. Simon O’Kane in the paper:
+
+> Richardson, Giles, et. al. "Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation." Electrochemica Acta 339 (2020): 135862
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py
new file mode 100644
index 0000000000..91103baedc
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_diffusivity_Ecker2015.py
@@ -0,0 +1,37 @@
+from pybamm import exp, constants
+
+
+def graphite_diffusivity_Ecker2015(sto, T):
+    """
+    Graphite diffusivity as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+     .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_ref = 8.4e-13 * exp(-11.3 * sto) + 8.2e-15
+    E_D_s = 3.03e4
+    arrhenius = exp(-E_D_s / (constants.R * T)) * exp(E_D_s / (constants.R * 296))
+
+    return D_ref * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py
new file mode 100644
index 0000000000..140f1cbe70
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_electrolyte_exchange_current_density_Ecker2015.py
@@ -0,0 +1,48 @@
+from pybamm import exp, constants, Parameter
+
+
+def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_ref = 1.995 * 1e-10
+
+    # multiply by Faraday's constant to get correct units
+    m_ref = constants.F * k_ref  # (A/m2)(mol/m3)**1.5 - includes ref concentrations
+    E_r = 53400
+
+    arrhenius = exp(-E_r / (constants.R * T)) * exp(E_r / (constants.R * 296.15))
+
+    c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]")
+
+    return (
+        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5
+    )
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv
new file mode 100644
index 0000000000..a578a7fa09
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015.csv
@@ -0,0 +1,41 @@
+0.0015151515151514694, 1.4325153374233128
+0.0060606060606061, 0.8619631901840491
+0.010606060606060619, 0.7914110429447854
+0.016666666666666607, 0.6595092024539877
+0.021212121212121238, 0.5797546012269938
+0.022727272727272707, 0.5245398773006136
+0.030303030303030276, 0.4754601226993864
+0.039393939393939315, 0.4141104294478526
+0.045454545454545414, 0.3680981595092023
+0.05303030303030298, 0.3312883435582821
+0.06666666666666665, 0.28220858895705514
+0.07878787878787874, 0.24846625766871155
+0.08939393939393936, 0.2239263803680982
+0.10151515151515145, 0.22085889570552153
+0.12727272727272732, 0.21165644171779152
+0.14242424242424245, 0.2024539877300613
+0.15909090909090917, 0.19938650306748462
+0.17727272727272725, 0.19325153374233128
+0.19393939393939397, 0.18404907975460127
+0.21363636363636362, 0.1809815950920246
+0.23333333333333328, 0.17177914110429437
+0.25757575757575757, 0.16564417177914104
+0.2787878787878788, 0.16257668711656437
+0.303030303030303, 0.15337423312883436
+0.32878787878787885, 0.14110429447852746
+0.35151515151515156, 0.13496932515337412
+0.3712121212121211, 0.13190184049079745
+0.39242424242424234, 0.128834355828221
+0.5681818181818183, 0.1257668711656441
+0.5878787878787879, 0.12269938650306744
+0.6060606060606062, 0.1165644171779141
+0.6272727272727272, 0.10122699386503076
+0.6545454545454545, 0.0950920245398772
+0.6742424242424243, 0.0950920245398772
+0.6939393939393939, 0.08895705521472386
+0.7181818181818183, 0.08895705521472386
+0.7393939393939393, 0.08588957055214719
+0.8909090909090911, 0.08282208588957074
+0.956060606060606, 0.08272208588957074
+0.9772727272727273, 0.07975460122699385
+1, 0.07055214723926384
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py
new file mode 100644
index 0000000000..8ac61354f4
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/graphite_ocp_Ecker2015_function.py
@@ -0,0 +1,62 @@
+from pybamm import exp, tanh
+
+
+def graphite_ocp_Ecker2015_function(sto):
+    """
+    Graphite OCP as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+     .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Open circuit potential
+    """
+
+    # Graphite negative electrode from Ecker, Kabitz, Laresgoiti et al.
+    # Analytical fit (WebPlotDigitizer + gnuplot)
+    a = 0.716502
+    b = 369.028
+    c = 0.12193
+    d = 35.6478
+    e = 0.0530947
+    g = 0.0169644
+    h = 27.1365
+    i = 0.312832
+    j = 0.0199313
+    k = 28.5697
+    m = 0.614221
+    n = 0.931153
+    o = 36.328
+    p = 1.10743
+    q = 0.140031
+    r = 0.0189193
+    s = 21.1967
+    t = 0.196176
+
+    u_eq = (
+        a * exp(-b * sto)
+        + c * exp(-d * (sto - e))
+        - r * tanh(s * (sto - t))
+        - g * tanh(h * (sto - i))
+        - j * tanh(k * (sto - m))
+        - n * exp(o * (sto - p))
+        + q
+    )
+
+    return u_eq
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv
new file mode 100644
index 0000000000..936c491f96
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/measured_graphite_diffusivity_Ecker2015.csv
@@ -0,0 +1,23 @@
+0.04291659469592768,2.5318983605709854e-13
+0.08025337997334403,4.438292394020732e-14
+0.12014956931041065,3.1947426343055446e-14
+0.15796120718295306,2.6077962994674755e-14
+0.19575227382393767,2.255908580891848e-14
+0.2357650334731653,1.16865022242173e-14
+0.27144412034088194,1.2386927328130891e-14
+0.3100288936661386,2.0291954487412547e-14
+0.3484165260222996,3.2604716406478646e-15
+0.3866704453733347,7.640156635519089e-16
+0.4264157790123111,8.417747371928056e-16
+0.4641862744217376,7.717093525037831e-16
+0.5018779134435263,8.837921553351043e-16
+0.5412118224513462,3.109171322297572e-15
+0.5766749113877059,6.062184667385398e-15
+0.6148036890800979,3.595937210129949e-14
+0.6545815938357071,2.0323093806678467e-15
+0.7681399348426814,7.802664217099549e-16
+0.8089429726375879,7.728942922045022e-16
+0.8823034129113143,9.65829674321414e-16
+0.9220864604748132,9.566999594213241e-16
+0.9607500901877076,1.2545776383613633e-15
+0.9976360226401492,1.395684713792463e-14
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
new file mode 100644
index 0000000000..7b57d24ce8
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
@@ -0,0 +1,25 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrode properties,,,
+Negative electrode conductivity [S.m-1],14,,
+Maximum concentration in negative electrode [mol.m-3],31920,,
+Measured negative electrode diffusivity [m2.s-1],[data]measured_graphite_diffusivity_Ecker2015,,
+Negative electrode diffusivity [m2.s-1],[function]graphite_diffusivity_Ecker2015,,
+Measured negative electrode OCP [V],[data]graphite_ocp_Ecker2015,,
+Negative electrode OCP [V],[function]graphite_ocp_Ecker2015_function,,
+,,,
+# Microstructure,,,
+Negative electrode porosity,0.32,Yang2017,
+Negative electrode active material volume fraction,0.372403,,
+Negative particle radius [m],1.00E-05,Yang2017,
+Negative electrode Bruggeman coefficient (electrolyte),1.637278934,Solve for permeability factor B=0.162=eps^b,
+Negative electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to the solid conductivity,
+,,,
+# Interfacial reactions,,,
+Negative electrode cation signed stoichiometry,-1,,
+Negative electrode electrons in reaction,1,,
+Negative electrode exchange-current density [A.m-2],[function]graphite_electrolyte_exchange_current_density_Ecker2015,,
+,,,
+# Thermal parameters,,,
+Negative electrode OCP entropic change [V.K-1],0,,
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
new file mode 100644
index 0000000000..1c5f9e623b
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
@@ -0,0 +1,27 @@
+from pybamm import Scalar
+
+
+def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li plating reaction [A.m-2].
+    References
+    ----------
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    return Scalar(0.001)
diff --git a/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md
new file mode 100644
index 0000000000..3c63aa585e
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md
@@ -0,0 +1,11 @@
+# SEI parameters
+
+Some example parameters for SEI growth from the papers:
+
+> Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004). Development of first principles capacity fade model for Li-ion cells. Journal of the Electrochemical Society, 151(2), A196-A203.
+> Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for aging of lithium-ion battery cells. Journal of The Electrochemical Society, 151(3), A456-A462.
+> Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of continued growth of the solid–electrolyte interphase. ChemSusChem, 11(12), 1950-1955.
+> Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society, 156(3), 
+> Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40.
+
+Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit SEI models to observed experimental data in the referenced papers.
diff --git a/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv
new file mode 100644
index 0000000000..bfc0d7ad59
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv
@@ -0,0 +1,23 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# SEI properties,,,
+Inner SEI reaction proportion,0.5,,
+Inner SEI partial molar volume [m3.mol-1],9.47E-05, Safari paper,
+Outer SEI partial molar volume [m3.mol-1],9.47E-05, Safari paper,
+SEI reaction exchange current density [A.m-2],1.50E-07, Guess,
+SEI resistivity [Ohm.m],5.00E+06, Safari paper,
+Outer SEI solvent diffusivity [m2.s-1],2.50E-22, Single paper,
+Bulk solvent concentration [mol.m-3],2.64E+03, Ploehn paper,
+Ratio of inner and outer SEI exchange current densities,1, Assume same,
+Inner SEI open-circuit potential [V],0.1,,
+Outer SEI open-circuit potential [V],0.8,,
+Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper,
+Inner SEI lithium interstitial diffusivity [m2.s-1],1.00E-20, Guess,
+Lithium interstitial reference concentration [mol.m-3],15, Single paper,
+Initial inner SEI thickness [m],2.50E-09, 2.5E-9 1/2 of initial thickness in Safari paper,
+Initial outer SEI thickness [m],2.50E-09, 1/2 of initial thickness in Safari paper,
+EC initial concentration in electrolyte [mol.m-3],4.54E+03, Safari paper,
+EC diffusivity [m2.s-1],2.00E-18, adjusted parameter in Yang paper,
+SEI kinetic rate constant [m.s-1],1.00E-12, adjusted parameter in Yang paper,
+SEI open-circuit potential [V],0.4, Safari paper,
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index ff6268efa0..d0027315e1 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -232,6 +232,7 @@ def options(self, extra_options):
             "sei": "none",
             "lithium plating": "none",
             "sei porosity change": "false",
+            "lithium plating porosity change": "false",
             "loss of active material": "none",
             "working electrode": "none",
             "particle cracking": "none",
@@ -400,6 +401,19 @@ def options(self, extra_options):
                 "Unknown lithium plating model '{}'".format(options["lithium plating"])
             )
 
+        if options["lithium plating porosity change"] not in ["true", "false"]:
+            if options["lithium plating porosity change"] in [True, False]:
+                raise pybamm.OptionError(
+                    "lithium plating porosity change must\
+                    now be given in string format "
+                    "('true' or 'false')"
+                )
+            raise pybamm.OptionError(
+                "Unknown lithium plating porosity change '{}'".format(
+                    options["sei porosity change"]
+                )
+            )
+
         if options["loss of active material"] not in [
             "none",
             "negative",
diff --git a/pybamm/models/full_battery_models/lead_acid/full.py b/pybamm/models/full_battery_models/lead_acid/full.py
index 897e1bf4b9..bc0b14cffe 100644
--- a/pybamm/models/full_battery_models/lead_acid/full.py
+++ b/pybamm/models/full_battery_models/lead_acid/full.py
@@ -54,7 +54,7 @@ def __init__(self, options=None, name="Full model", build=True):
         pybamm.citations.register("Sulzer2019physical")
 
     def set_porosity_submodel(self):
-        self.submodels["porosity"] = pybamm.porosity.Full(self.param)
+        self.submodels["porosity"] = pybamm.porosity.Full(self.param, self.options)
 
     def set_convection_submodel(self):
         if self.options["convection"] == "none":
diff --git a/pybamm/models/full_battery_models/lead_acid/higher_order.py b/pybamm/models/full_battery_models/lead_acid/higher_order.py
index 402f75c5f0..e99ffd0c88 100644
--- a/pybamm/models/full_battery_models/lead_acid/higher_order.py
+++ b/pybamm/models/full_battery_models/lead_acid/higher_order.py
@@ -198,7 +198,7 @@ def set_full_porosity_submodel(self):
         Update porosity submodel, now that we have the spatially heterogeneous
         interfacial current densities
         """
-        self.submodels["full porosity"] = pybamm.porosity.Full(self.param)
+        self.submodels["full porosity"] = pybamm.porosity.Full(self.param, self.options)
 
 
 class FOQS(BaseHigherOrderModel):
@@ -269,7 +269,7 @@ def set_full_porosity_submodel(self):
         Update porosity submodel, now that we have the spatially heterogeneous
         interfacial current densities
         """
-        self.submodels["full porosity"] = pybamm.porosity.Full(self.param)
+        self.submodels["full porosity"] = pybamm.porosity.Full(self.param, self.options)
 
 
 class CompositeExtended(Composite):
diff --git a/pybamm/models/full_battery_models/lead_acid/loqs.py b/pybamm/models/full_battery_models/lead_acid/loqs.py
index 83dab70e46..4036afc536 100644
--- a/pybamm/models/full_battery_models/lead_acid/loqs.py
+++ b/pybamm/models/full_battery_models/lead_acid/loqs.py
@@ -96,7 +96,7 @@ def set_current_collector_submodel(self):
     def set_porosity_submodel(self):
 
         self.submodels["leading-order porosity"] = pybamm.porosity.LeadingOrder(
-            self.param
+            self.param, self.options
         )
 
     def set_tortuosity_submodels(self):
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index f9a9712b96..0588569ec0 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -1,12 +1,38 @@
-
 import pybamm
 from .dfn import DFN
 
+
 class Yang2017(DFN):
-   def __init__(self):
-        options = options = {"sei": "ec reaction limited", "sei film resistance": "distributed", "sei porosity change": "true", "lithium plating": "irreversible"}
-        super().__init__(self, options=options, name="Yang 2017")
-  
-   @property
-   def default_parameter_values(self):
-       return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+    def __init__(self, options=None, name="Yang2017", build=True):
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        super().__init__(options=options, name=name)
+        pybamm.citations.register("Yang2017")
+
+    @property
+    def default_parameter_values(self):
+        return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+
+
+# import pybamm
+# from .spme import SPMe
+
+# class Yang2017(SPMe):
+# def __init__(self, options=None, name="Yang2017", build=True):
+# options = {
+# "sei": "ec reaction limited",
+# "sei film resistance": "distributed",
+# "sei porosity change": "true",
+# "lithium plating": "irreversible",
+# "lithium plating porosity change": "true",
+# }
+# super().__init__(options=options, name=name)
+# pybamm.citations.register("Yang2017")
+# @property
+# def default_parameter_values(self):
+# return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py
index 52e9e3dc76..40b6b38c76 100644
--- a/pybamm/models/full_battery_models/lithium_ion/__init__.py
+++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py
@@ -8,3 +8,4 @@
 from .basic_dfn import BasicDFN
 from .basic_spm import BasicSPM
 from .basic_dfn_half_cell import BasicDFNHalfCell
+from .Yang2017 import Yang2017
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
index 3c672ea090..9bfdb6fcaf 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
@@ -222,7 +222,10 @@ def __init__(self, name="Doyle-Fuller-Newman half cell model", options=None):
         # derivatives
         self.boundary_conditions[c_s_w] = {
             "left": (pybamm.Scalar(0), "Neumann"),
-            "right": (-C_w * j_w / a_R_w / gamma_w / D_w(c_s_surf_w, T), "Neumann",),
+            "right": (
+                -C_w * j_w / a_R_w / gamma_w / D_w(c_s_surf_w, T),
+                "Neumann",
+            ),
         }
 
         # c_w_init can in general be a function of x
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index 5180a923f9..603210d652 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -56,10 +56,18 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman model", build=True):
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
-        elif self.options["sei porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.Full(self.param)
+        if (
+            self.options["sei porosity change"] == "false"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Constant(
+                self.param, self.options
+            )
+        elif (
+            self.options["sei porosity change"] == "true"
+            or self.options["lithium plating porosity change"] == "true"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Full(self.param, self.options)
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index 93e1201f8b..daf92ae56a 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -57,10 +57,20 @@ def __init__(self, options=None, name="Single Particle Model", build=True):
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
-        elif self.options["sei porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)
+        if (
+            self.options["sei porosity change"] == "false"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Constant(
+                self.param, self.options
+            )
+        elif (
+            self.options["sei porosity change"] == "true"
+            or self.options["lithium plating porosity change"] == "true"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(
+                self.param, self.options
+            )
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py
index 50f84df24d..9736387ce6 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spme.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spme.py
@@ -59,10 +59,20 @@ def __init__(
 
     def set_porosity_submodel(self):
 
-        if self.options["sei porosity change"] == "false":
-            self.submodels["porosity"] = pybamm.porosity.Constant(self.param)
-        elif self.options["sei porosity change"] == "true":
-            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(self.param)
+        if (
+            self.options["sei porosity change"] == "false"
+            and self.options["lithium plating porosity change"] == "false"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.Constant(
+                self.param, self.options
+            )
+        elif (
+            self.options["sei porosity change"] == "true"
+            or self.options["lithium plating porosity change"] == "true"
+        ):
+            self.submodels["porosity"] = pybamm.porosity.LeadingOrder(
+                self.param, self.options
+            )
 
     def set_active_material_submodel(self):
 
diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
index ecd833b35b..5503ad05a3 100644
--- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
@@ -7,21 +7,18 @@
 
 class BasePlating(BaseInterface):
     """Base class for lithium plating models.
-
     Parameters
     ----------
     param : parameter class
         The parameters to use for this submodel
     reactions : dict, optional
         Dictionary of reaction terms
-
     References
     ----------
     .. [1] SEJ O'Kane, ID Campbell, MWJ Marzook, GJ Offer and M Marinescu. "Physical
            Origin of the Differential Voltage Minimum Associated with Li Plating in
            Lithium-Ion Batteries". Journal of The Electrochemical Society,
            167:090540, 2019
-
     **Extends:** :class:`pybamm.interface.BaseInterface`
     """
 
@@ -56,7 +53,7 @@ def _get_standard_concentration_variables(self, c_plated_Li):
             c_scale = 1
             L_scale = 1
         else:
-            c_scale = param.c_e_typ
+            c_scale = param.c_Li_typ
             L_scale = param.V_bar_plated_Li * c_scale / param.a_n_typ
 
         c_plated_Li_av = pybamm.x_average(c_plated_Li)
@@ -71,14 +68,16 @@ def _get_standard_concentration_variables(self, c_plated_Li):
             f"{Domain} lithium plating concentration": c_plated_Li,
             f"{Domain} lithium plating concentration [mol.m-3]": c_plated_Li * c_scale,
             f"{Domain} X-averaged lithium plating concentration": c_plated_Li_av,
-            f"X-averaged {domain} lithium plating concentration [mol.m-3]":
-            c_plated_Li_av * c_scale,
+            f"X-averaged {domain} lithium plating concentration"
+            " [mol.m-3]": c_plated_Li_av * c_scale,
             f"{Domain} lithium plating thickness [m]": L_plated_Li * L_scale,
             f"X-averaged {domain} lithium plating thickness [m]": L_plated_Li_av
             * L_scale,
             f"Loss of Li to {domain} lithium plating [mol]": Q_plated_Li * c_scale,
             f"Loss of capacity to {domain} lithium plating [A.h]": Q_plated_Li
-            * c_scale * param.F / 3600,
+            * c_scale
+            * param.F
+            / 3600,
         }
 
         return variables
diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index 1227c54891..94d0668885 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -1,4 +1,3 @@
-#
 # Class for irreversible lithium plating
 #
 import pybamm
@@ -7,21 +6,18 @@
 
 class IrreversiblePlating(BasePlating):
     """Base class for irreversible lithium plating.
-
     Parameters
     ----------
     param : parameter class
         The parameters to use for this submodel
     domain : str
         The domain of the model either 'Negative' or 'Positive'
-
     References
     ----------
     .. [1] SEJ O'Kane, ID Campbell, MWJ Marzook, GJ Offer and M Marinescu. "Physical
            Origin of the Differential Voltage Minimum Associated with Li Plating in
            Lithium-Ion Batteries". Journal of The Electrochemical Society,
            167:090540, 2019
-
     **Extends:** :class:`pybamm.lithium_plating.BasePlating`
     """
 
@@ -31,7 +27,7 @@ def __init__(self, param, domain):
 
     def get_fundamental_variables(self):
         c_plated_Li = pybamm.Variable(
-            "Plated lithium concentration",
+            f"{self.domain.capitalize()} electrode lithium plating concentration",
             domain=self.domain.lower() + " electrode",
             auxiliary_domains={"secondary": "current collector"},
         )
@@ -45,16 +41,18 @@ def get_coupled_variables(self, variables):
         phi_s_n = variables[f"{self.domain} electrode potential"]
         phi_e_n = variables[f"{self.domain} electrolyte potential"]
         c_e_n = variables[f"{self.domain} electrolyte concentration"]
+        T = variables[f"{self.domain} electrode temperature"]
         eta_sei = variables[f"{self.domain} electrode sei film overpotential"]
-        C_plating = param.C_plating
+        c_plated_Li = variables[
+            f"{self.domain} electrode lithium plating concentration"
+        ]
+        j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
         phi_ref = param.U_n_ref / param.potential_scale
 
         # need to revise for thermal case
         # j_stripping is always negative, because there is no stripping, only plating
-        j_stripping = (
-            -(1 / C_plating)
-            * c_e_n
-            * (pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei)))
+        j_stripping = -j0_plating * (
+            pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
         )
 
         variables.update(self._get_standard_reaction_variables(j_stripping))
diff --git a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
index 8bbd13156a..ce9c95bbc3 100644
--- a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
@@ -1,4 +1,3 @@
-#
 # Class for reversible lithium plating
 #
 import pybamm
@@ -7,21 +6,18 @@
 
 class ReversiblePlating(BasePlating):
     """Base class for reversible lithium plating.
-
     Parameters
     ----------
     param : parameter class
         The parameters to use for this submodel
     domain : str
         The domain of the model either 'Negative' or 'Positive'
-
     References
     ----------
     .. [1] SEJ O'Kane, ID Campbell, MWJ Marzook, GJ Offer and M Marinescu. "Physical
            Origin of the Differential Voltage Minimum Associated with Li Plating in
            Lithium-Ion Batteries". Journal of The Electrochemical Society,
            167:090540, 2019
-
     **Extends:** :class:`pybamm.lithium_plating.BasePlating`
     """
 
@@ -31,7 +27,7 @@ def __init__(self, param, domain):
 
     def get_fundamental_variables(self):
         c_plated_Li = pybamm.Variable(
-            "Plated lithium concentration",
+            f"{self.domain.capitalize()} electrode lithium plating concentration",
             domain=self.domain.lower() + " electrode",
             auxiliary_domains={"secondary": "current collector"},
         )
@@ -45,18 +41,19 @@ def get_coupled_variables(self, variables):
         phi_s_n = variables[f"{self.domain} electrode potential"]
         phi_e_n = variables[f"{self.domain} electrolyte potential"]
         c_e_n = variables[f"{self.domain} electrolyte concentration"]
+        T = variables[f"{self.domain} electrode temperature"]
         eta_sei = variables[f"{self.domain} electrode sei film overpotential"]
         c_plated_Li = variables[
             f"{self.domain} electrode lithium plating concentration"
         ]
-        C_plating = param.C_plating
+        j0_stripping = param.j0_stripping(c_e_n, c_plated_Li, T)
+        j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
         phi_ref = param.U_n_ref / param.potential_scale
 
         # need to revise for thermal case
-        j_stripping = (1 / C_plating) * (
-            c_plated_Li * pybamm.exp(0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
-            - c_e_n * pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
-        )
+        j_stripping = j0_stripping * pybamm.exp(
+            0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei)
+        ) - j0_plating * pybamm.exp(-0.5 * (phi_s_n - phi_e_n + phi_ref + eta_sei))
 
         variables.update(self._get_standard_reaction_variables(j_stripping))
 
diff --git a/pybamm/models/submodels/porosity/base_porosity.py b/pybamm/models/submodels/porosity/base_porosity.py
index f05120b578..6c6841e09b 100644
--- a/pybamm/models/submodels/porosity/base_porosity.py
+++ b/pybamm/models/submodels/porosity/base_porosity.py
@@ -16,7 +16,7 @@ class BaseModel(pybamm.BaseSubModel):
     **Extends:** :class:`pybamm.BaseSubModel`
     """
 
-    def __init__(self, param):
+    def __init__(self, param, options):
         super().__init__(param)
 
     def _get_standard_porosity_variables(
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index e9e37ac61e..3778e2126a 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -17,8 +17,9 @@ class Full(BaseModel):
     **Extends:** :class:`pybamm.porosity.BaseModel`
     """
 
-    def __init__(self, param):
-        super().__init__(param)
+    def __init__(self, param, options):
+        super().__init__(param, options)
+        self.options = options
 
     def get_fundamental_variables(self):
 
@@ -33,13 +34,27 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["Negative electrode interfacial current density"]
         j_p = variables["Positive electrode interfacial current density"]
-        j_sei_n = variables["Negative electrode sei interfacial current density"]
-        beta_sei_n = self.param.beta_sei_n
+        deps_n_dt = -self.param.beta_surf_n * j_n
+
+        if self.options["sei porosity change"] == "true":
+
+            j_sei_n = variables["Negative electrode sei interfacial current density"]
+            beta_sei_n = self.param.beta_sei_n
+            deps_n_dt += beta_sei_n * j_sei_n
+
+        if self.options["lithium plating porosity change"] == "true":
+
+            j_plating = variables[
+                "Negative electrode lithium plating interfacial current density"
+            ]
+            beta_plating = self.param.beta_plating
+
+            deps_n_dt += beta_plating * j_plating
 
-        deps_n_dt = -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
+
         deps_p_dt = -self.param.beta_surf_p * j_p
 
         variables.update(
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index a3715736f5..e291b83394 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -17,8 +17,9 @@ class LeadingOrder(BaseModel):
     **Extends:** :class:`pybamm.porosity.BaseModel`
     """
 
-    def __init__(self, param):
-        super().__init__(param)
+    def __init__(self, param, options):
+        super().__init__(param, options)
+        self.options = options
 
     def get_fundamental_variables(self):
 
@@ -37,14 +38,30 @@ def get_coupled_variables(self, variables):
 
         j_n = variables["X-averaged negative electrode interfacial current density"]
         j_p = variables["X-averaged positive electrode interfacial current density"]
-        j_sei_n = variables[
-            "X-averaged negative electrode sei interfacial current density"
-        ]
-        beta_sei_n = self.param.beta_sei_n
-
         deps_n_dt = pybamm.PrimaryBroadcast(
-            -self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n, ["negative electrode"]
+            -self.param.beta_surf_n * j_n, ["negative electrode"]
         )
+
+        if self.options["sei porosity change"] == "true":
+
+            j_sei_n = variables[
+                "X-averaged negative electrode sei interfacial current density"
+            ]
+
+            beta_sei_n = self.param.beta_sei_n
+            deps_n_dt += j_sei_n * beta_sei_n
+
+        if self.options["lithium plating porosity change"] == "true":
+
+            j_plating = variables[
+                "X-averaged negative electrode lithium"
+                " plating interfacial current density"
+            ]
+
+            beta_plating = self.param.beta_plating
+
+            deps_n_dt += beta_plating * j_plating
+
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 30ed07711f..962bfaab3b 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -1,4 +1,3 @@
-#
 # Standard parameters for lithium-ion battery models
 #
 import pybamm
@@ -8,7 +7,6 @@
 class LithiumIonParameters:
     """
     Standard parameters for lithium-ion battery models
-
     Layout:
         1. Dimensional Parameters
         2. Dimensional Functions
@@ -16,14 +14,11 @@ class LithiumIonParameters:
         4. Dimensionless Parameters
         5. Dimensionless Functions
         6. Input Current
-
     Parameters
     ----------
-
     options : dict, optional
         A dictionary of options to be passed to the parameters. The options that
         can be set are listed below.
-
             * "particle shape" : str, optional
                 Sets the model shape of the electrode particles. This is used to
                 calculate the surface area to volume ratio. Can be "spherical"
@@ -49,7 +44,7 @@ def __init__(self, options=None):
         self._set_input_current()
 
     def _set_dimensional_parameters(self):
-        "Defines the dimensional parameters"
+        """Defines the dimensional parameters"""
 
         # Physical constants
         self.R = pybamm.constants.R
@@ -213,16 +208,10 @@ def _set_dimensional_parameters(self):
         self.V_bar_plated_Li = pybamm.Parameter(
             "Lithium metal partial molar volume [m3.mol-1]"
         )
-        self.k_plating = pybamm.Parameter(
-            "Lithium plating kinetic rate constant [m.s-1]"
-        )
         self.c_plated_Li_0_dim = pybamm.Parameter(
             "Initial plated lithium concentration [mol.m-3]"
         )
 
-        # Exchange current density for scaling
-        self.j0_plating_dimensional = self.F * self.k_plating * self.c_e_typ
-
         # Initial conditions
         # Note: the initial concentration in the electrodes can be set as a function
         # of through-cell position, so is defined later as a function
@@ -302,12 +291,12 @@ def _set_dimensional_parameters(self):
         )
 
     def D_e_dimensional(self, c_e, T):
-        "Dimensional diffusivity in electrolyte"
+        """Dimensional diffusivity in electrolyte"""
         inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
         return pybamm.FunctionParameter("Electrolyte diffusivity [m2.s-1]", inputs)
 
     def kappa_e_dimensional(self, c_e, T):
-        "Dimensional electrolyte conductivity"
+        """Dimensional electrolyte conductivity"""
         inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
         return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs)
 
@@ -342,7 +331,7 @@ def D_p_dimensional(self, sto, T):
         )
 
     def j0_n_dimensional(self, c_e, c_s_surf, T):
-        "Dimensional negative exchange-current density [A.m-2]"
+        """Dimensional negative exchange-current density [A.m-2]"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e,
             "Negative particle surface concentration [mol.m-3]": c_s_surf,
@@ -353,7 +342,7 @@ def j0_n_dimensional(self, c_e, c_s_surf, T):
         )
 
     def j0_p_dimensional(self, c_e, c_s_surf, T):
-        "Dimensional negative exchange-current density [A.m-2]"
+        """Dimensional negative exchange-current density [A.m-2]"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e,
             "Positive particle surface concentration [mol.m-3]": c_s_surf,
@@ -363,14 +352,36 @@ def j0_p_dimensional(self, c_e, c_s_surf, T):
             "Positive electrode exchange-current density [A.m-2]", inputs
         )
 
+    def j0_stripping_dimensional(self, c_e, c_Li, T):
+        """Dimensional exchange-current density for stripping [A.m-2]"""
+        inputs = {
+            "Electrolyte concentration [mol.m-3]": c_e,
+            "Plated lithium concentration [mol.m-3]": c_Li,
+            "Temperature [K]": T,
+        }
+        return pybamm.FunctionParameter(
+            "Exchange-current density for stripping [A.m-2]", inputs
+        )
+
+    def j0_plating_dimensional(self, c_e, c_Li, T):
+        """Dimensional exchange-current density for plating [A.m-2]"""
+        inputs = {
+            "Electrolyte concentration [mol.m-3]": c_e,
+            "Plated lithium concentration [mol.m-3]": c_Li,
+            "Temperature [K]": T,
+        }
+        return pybamm.FunctionParameter(
+            "Exchange-current density for plating [A.m-2]", inputs
+        )
+
     def U_n_dimensional(self, sto, T):
-        "Dimensional open-circuit potential in the negative electrode [V]"
+        """Dimensional open-circuit potential in the negative electrode [V]"""
         inputs = {"Negative particle stoichiometry": sto}
         u_ref = pybamm.FunctionParameter("Negative electrode OCP [V]", inputs)
         return u_ref + (T - self.T_ref) * self.dUdT_n_dimensional(sto)
 
     def U_p_dimensional(self, sto, T):
-        "Dimensional open-circuit potential in the positive electrode [V]"
+        """Dimensional open-circuit potential in the positive electrode [V]"""
         inputs = {"Positive particle stoichiometry": sto}
         u_ref = pybamm.FunctionParameter("Positive electrode OCP [V]", inputs)
         return u_ref + (T - self.T_ref) * self.dUdT_p_dimensional(sto)
@@ -396,45 +407,45 @@ def dUdT_p_dimensional(self, sto):
         )
 
     def R_n_dimensional(self, x):
-        "Negative particle radius as a function of through-cell distance"
+        """Negative particle radius as a function of through-cell distance"""
         inputs = {"Through-cell distance (x_n) [m]": x}
         return pybamm.FunctionParameter("Negative particle radius [m]", inputs)
 
     def R_p_dimensional(self, x):
-        "Positive particle radius as a function of through-cell distance"
+        """Positive particle radius as a function of through-cell distance"""
         inputs = {"Through-cell distance (x_p) [m]": x}
         return pybamm.FunctionParameter("Positive particle radius [m]", inputs)
 
     def epsilon_s_n(self, x):
-        "Negative electrode active material volume fraction"
+        """Negative electrode active material volume fraction"""
         inputs = {"Through-cell distance (x_n) [m]": x * self.L_x}
         return pybamm.FunctionParameter(
             "Negative electrode active material volume fraction", inputs
         )
 
     def epsilon_s_p(self, x):
-        "Positive electrode active material volume fraction"
+        """Positive electrode active material volume fraction"""
         inputs = {"Through-cell distance (x_p) [m]": x * self.L_x}
         return pybamm.FunctionParameter(
             "Positive electrode active material volume fraction", inputs
         )
 
     def c_n_init_dimensional(self, x):
-        "Initial concentration as a function of dimensionless position x"
+        """Initial concentration as a function of dimensionless position x"""
         inputs = {"Dimensionless through-cell position (x_n)": x}
         return pybamm.FunctionParameter(
             "Initial concentration in negative electrode [mol.m-3]", inputs
         )
 
     def c_p_init_dimensional(self, x):
-        "Initial concentration as a function of dimensionless position x"
+        """Initial concentration as a function of dimensionless position x"""
         inputs = {"Dimensionless through-cell position (x_p)": x}
         return pybamm.FunctionParameter(
             "Initial concentration in positive electrode [mol.m-3]", inputs
         )
 
     def _set_scales(self):
-        "Define the scales used in the non-dimensionalisation scheme"
+        """Define the scales used in the non-dimensionalisation scheme"""
 
         # Microscale (typical values at electrode/current collector interface)
         self.R_n_typ = self.R_n_dimensional(0)
@@ -517,7 +528,7 @@ def _set_scales(self):
         self.timescale = self.tau_discharge
 
     def _set_dimensionless_parameters(self):
-        "Defines the dimensionless parameters"
+        """Defines the dimensionless parameters"""
 
         # Timescale ratios
         self.C_n = self.tau_diffusion_n / self.tau_discharge
@@ -733,15 +744,15 @@ def _set_dimensionless_parameters(self):
         self.beta_sei_n = self.a_n_typ * self.L_sei_0_dim * self.Gamma_SEI_n
 
         # lithium plating parameters
-
-        self.C_plating = self.j_scale_n / self.j0_plating_dimensional
-
-        self.c_plated_Li_0 = self.c_plated_Li_0_dim / self.c_e_typ
+        self.c_Li_typ = self.c_e_typ
+        self.c_plated_Li_0 = self.c_plated_Li_0_dim / self.c_Li_typ
 
         # ratio of lithium plating reaction scaled to intercalation reaction
-        self.Gamma_plating = (
-            self.a_n_typ * self.j_scale_n * self.tau_discharge
-        ) / (self.F * self.c_e_typ)
+        self.Gamma_plating = (self.a_n_typ * self.j_scale_n * self.tau_discharge) / (
+            self.F * self.c_Li_typ
+        )
+
+        self.beta_plating = self.Gamma_plating * self.V_bar_plated_Li * self.c_Li_typ
 
         # Initial conditions
         self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity")
@@ -775,7 +786,7 @@ def chi(self, c_e, T):
         return (2 * (1 - self.t_plus(c_e, T))) * (self.one_plus_dlnf_dlnc(c_e, T))
 
     def t_plus(self, c_e, T):
-        "Cation transference number (dimensionless)"
+        """Cation transference number (dimensionless)"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ,
             "Temperature [K]": self.Delta_T * T + self.T_ref,
@@ -783,7 +794,7 @@ def t_plus(self, c_e, T):
         return pybamm.FunctionParameter("Cation transference number", inputs)
 
     def one_plus_dlnf_dlnc(self, c_e, T):
-        "Thermodynamic factor (dimensionless)"
+        """Thermodynamic factor (dimensionless)"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ,
             "Temperature [K]": self.Delta_T * T + self.T_ref,
@@ -791,20 +802,20 @@ def one_plus_dlnf_dlnc(self, c_e, T):
         return pybamm.FunctionParameter("1 + dlnf/dlnc", inputs)
 
     def D_e(self, c_e, T):
-        "Dimensionless electrolyte diffusivity"
+        """Dimensionless electrolyte diffusivity"""
         c_e_dimensional = c_e * self.c_e_typ
         T_dim = self.Delta_T * T + self.T_ref
         return self.D_e_dimensional(c_e_dimensional, T_dim) / self.D_e_typ
 
     def kappa_e(self, c_e, T):
-        "Dimensionless electrolyte conductivity"
+        """Dimensionless electrolyte conductivity"""
         c_e_dimensional = c_e * self.c_e_typ
         kappa_scale = self.F ** 2 * self.D_e_typ * self.c_e_typ / (self.R * self.T_ref)
         T_dim = self.Delta_T * T + self.T_ref
         return self.kappa_e_dimensional(c_e_dimensional, T_dim) / kappa_scale
 
     def D_n(self, c_s_n, T):
-        "Dimensionless negative particle diffusivity"
+        """Dimensionless negative particle diffusivity"""
         sto = c_s_n
         T_dim = self.Delta_T * T + self.T_ref
         return self.D_n_dimensional(sto, T_dim) / self.D_n_dimensional(
@@ -812,7 +823,7 @@ def D_n(self, c_s_n, T):
         )
 
     def D_p(self, c_s_p, T):
-        "Dimensionless positive particle diffusivity"
+        """Dimensionless positive particle diffusivity"""
         sto = c_s_p
         T_dim = self.Delta_T * T + self.T_ref
         return self.D_p_dimensional(sto, T_dim) / self.D_p_dimensional(
@@ -820,7 +831,7 @@ def D_p(self, c_s_p, T):
         )
 
     def j0_n(self, c_e, c_s_surf, T):
-        "Dimensionless negative exchange-current density"
+        """Dimensionless negative exchange-current density"""
         c_e_dim = c_e * self.c_e_typ
         c_s_surf_dim = c_s_surf * self.c_n_max
         T_dim = self.Delta_T * T + self.T_ref
@@ -831,7 +842,7 @@ def j0_n(self, c_e, c_s_surf, T):
         )
 
     def j0_p(self, c_e, c_s_surf, T):
-        "Dimensionless positive exchange-current density"
+        """Dimensionless positive exchange-current density"""
         c_e_dim = c_e * self.c_e_typ
         c_s_surf_dim = c_s_surf * self.c_p_max
         T_dim = self.Delta_T * T + self.T_ref
@@ -841,25 +852,41 @@ def j0_p(self, c_e, c_s_surf, T):
             / self.j0_p_ref_dimensional
         )
 
+    def j0_stripping(self, c_e, c_Li, T):
+        """Dimensionless exchange-current density for stripping"""
+        c_e_dim = c_e * self.c_e_typ
+        c_Li_dim = c_Li * self.c_Li_typ
+        T_dim = self.Delta_T * T + self.T_ref
+
+        return self.j0_stripping_dimensional(c_e_dim, c_Li_dim, T_dim) / self.j_scale_n
+
+    def j0_plating(self, c_e, c_Li, T):
+        """Dimensionless reverse plating current"""
+        c_e_dim = c_e * self.c_e_typ
+        c_Li_dim = c_Li * self.c_Li_typ
+        T_dim = self.Delta_T * T + self.T_ref
+
+        return self.j0_plating_dimensional(c_e_dim, c_Li_dim, T_dim) / self.j_scale_n
+
     def U_n(self, c_s_n, T):
-        "Dimensionless open-circuit potential in the negative electrode"
+        """Dimensionless open-circuit potential in the negative electrode"""
         sto = c_s_n
         T_dim = self.Delta_T * T + self.T_ref
         return (self.U_n_dimensional(sto, T_dim) - self.U_n_ref) / self.potential_scale
 
     def U_p(self, c_s_p, T):
-        "Dimensionless open-circuit potential in the positive electrode"
+        """Dimensionless open-circuit potential in the positive electrode"""
         sto = c_s_p
         T_dim = self.Delta_T * T + self.T_ref
         return (self.U_p_dimensional(sto, T_dim) - self.U_p_ref) / self.potential_scale
 
     def dUdT_n(self, c_s_n):
-        "Dimensionless entropic change in negative open-circuit potential"
+        """Dimensionless entropic change in negative open-circuit potential"""
         sto = c_s_n
         return self.dUdT_n_dimensional(sto) * self.Delta_T / self.potential_scale
 
     def dUdT_p(self, c_s_p):
-        "Dimensionless entropic change in positive open-circuit potential"
+        """Dimensionless entropic change in positive open-circuit potential"""
         sto = c_s_p
         return self.dUdT_p_dimensional(sto) * self.Delta_T / self.potential_scale
 
@@ -880,15 +907,19 @@ def R_p(self, x):
         return self.R_p_dimensional(x_dim) / self.R_p_typ
 
     def c_n_init(self, x):
-        "Dimensionless initial concentration as a function of dimensionless position x"
+        """
+        Dimensionless initial concentration as a function of dimensionless position x
+        """
         return self.c_n_init_dimensional(x) / self.c_n_max
 
     def c_p_init(self, x):
-        "Dimensionless initial concentration as a function of dimensionless position x"
+        """
+        Dimensionless initial concentration as a function of dimensionless position x
+        """
         return self.c_p_init_dimensional(x) / self.c_p_max
 
     def rho(self, T):
-        "Dimensionless effective volumetric heat capacity"
+        """Dimensionless effective volumetric heat capacity"""
         return (
             self.rho_cn(T) * self.l_cn
             + self.rho_n(T) * self.l_n
@@ -898,7 +929,7 @@ def rho(self, T):
         ) / self.l
 
     def _set_input_current(self):
-        "Set the input current"
+        """Set the input current"""
 
         self.dimensional_current_with_time = pybamm.FunctionParameter(
             "Current function [A]", {"Time [s]": pybamm.t * self.timescale}
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index c523135985..35b972b2dd 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -89,6 +89,20 @@
     "citation": ["Ecker2015i", "Ecker2015ii", "Richardson2020"],
 }
 
+Yang2017 = {
+    "chemistry": "lithium-ion",
+    "cell": "Yang2017",
+    "negative electrode": "graphite_Yang2017",
+    "separator": "separator_Ecker2015",
+    "positive electrode": "LiNiCoO2_Ecker2015",
+    "electrolyte": "lipf6_Ecker2015",
+    "experiment": "1C_discharge_from_full_Ecker2015",
+    "sei": "yang2017_sei",
+    "lithium plating": "yang2017_Li_plating",
+    "citation": ["Yang2017", "Ecker2015i"],
+}
+
+
 Marquis2019 = {
     "chemistry": "lithium-ion",
     "cell": "kokam_Marquis2019",
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 1b73ee1f1a..a075be5474 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -153,6 +153,10 @@ def update_from_chemistry(self, chemistry):
         if "sei" in chemistry:
             component_groups += ["sei"]
 
+        # add lithium plating parameters if provided
+        if "lithium plating" in chemistry:
+            component_groups += ["lithium plating"]
+
         if "anode" in chemistry.keys():
             if "negative electrode" in chemistry.keys():
                 raise KeyError(
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index baff2ad883..9ee55791b8 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -715,7 +715,10 @@ def block_fun(i, j, Ai, Aj):
             return Ai
         else:
             return onp.zeros(
-                (Ai.shape[0] if Ai.ndim > 1 else 1, Aj.shape[1] if Aj.ndim > 1 else 1,),
+                (
+                    Ai.shape[0] if Ai.ndim > 1 else 1,
+                    Aj.shape[1] if Aj.ndim > 1 else 1,
+                ),
                 dtype=Ai.dtype,
             )
 
@@ -944,7 +947,7 @@ def scan_fun(carry, i):
             *args,
             mass=aug_mass,
             rtol=rtol,
-            atol=atol
+            atol=atol,
         )
         y_bar, t0_bar, args_bar = tree_map(op.itemgetter(1), (y_bar, t0_bar, args_bar))
         # Add gradient from current output
diff --git a/python b/python
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
index 446fd06970..c91a7be7a7 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
@@ -178,6 +178,15 @@ def positive_radius(x):
         modeltest = tests.StandardModelTest(model, parameter_values=param)
         modeltest.test_all()
 
+    def test_well_posed_irreversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.DFN(options)
+        modeltest = tests.StandardModelTest(model)
+        modeltest.test_all()
+
 
 class TestDFNWithSEI(unittest.TestCase):
     def test_well_posed_constant(self):
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
index 08a3b8d248..2128264bd2 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
@@ -249,6 +249,14 @@ def test_well_posed_both_cracking(self):
         modeltest = tests.StandardModelTest(model, parameter_values=parameter_values)
         modeltest.test_all()
 
+    def test_well_posed_reversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "reversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.DFN(options)
+        model.check_well_posedness()
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
index 2ca68d887f..bf58a5750d 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
@@ -170,6 +170,14 @@ def test_integrated_conductivity(self):
         modeltest = tests.StandardModelTest(model)
         modeltest.test_all()
 
+    def test_well_posed_reversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "reversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.SPMe(options)
+        model.check_well_posedness()
+
 
 class TestSPMeWithSEI(unittest.TestCase):
     def test_well_posed_reaction_limited(self):
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py
new file mode 100644
index 0000000000..4525d7f999
--- /dev/null
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py
@@ -0,0 +1,19 @@
+import pybamm
+import unittest
+import tests
+
+
+class TestYANG2017(unittest.TestCase):
+    def test_basic_processing(self):
+        model = pybamm.lithium_ion.Yang2017()
+        modeltest = tests.StandardModelTest(model)
+        modeltest.test_all()
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    unittest.main()
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
index ad5f225059..5afdfd7740 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
@@ -154,6 +154,22 @@ def test_electrolyte_options(self):
         with self.assertRaisesRegex(pybamm.OptionError, "electrolyte conductivity"):
             pybamm.lithium_ion.DFN(options)
 
+    def test_well_posed_reversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "reversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.DFN(options)
+        model.check_well_posedness()
+
+    def test_well_posed_irreversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.DFN(options)
+        model.check_well_posedness()
+
 
 class TestDFNWithSEI(unittest.TestCase):
     def test_well_posed_constant(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
index ed31d762a0..a15693d250 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
@@ -178,6 +178,14 @@ def test_new_model(self):
         model_cs_eqn = list(model.rhs.values())[1]
         self.assertEqual(new_model_cs_eqn.id, model_cs_eqn.id)
 
+    def test_well_posed_reversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "reversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.SPM(options)
+        model.check_well_posedness()
+
 
 class TestSPMExternalCircuits(unittest.TestCase):
     def test_well_posed_voltage(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
index df5ba33174..3c11141c7e 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
@@ -159,6 +159,14 @@ def test_electrolyte_options(self):
         with self.assertRaisesRegex(pybamm.OptionError, "electrolyte conductivity"):
             pybamm.lithium_ion.SPMe(options)
 
+    def test_well_posed_reversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "reversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.SPMe(options)
+        model.check_well_posedness()
+
 
 class TestSPMeWithSEI(unittest.TestCase):
     def test_well_posed_reaction_limited(self):
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py
index 4025bde773..193d96133f 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_base_porosity.py
@@ -11,7 +11,7 @@ class TestBaseModel(unittest.TestCase):
     def test_public_functions(self):
         a = pybamm.Scalar(0)
         variables = {"Negative electrode porosity": a, "Positive electrode porosity": a}
-        submodel = pybamm.porosity.BaseModel(None)
+        submodel = pybamm.porosity.BaseModel(None, None)
         std_tests = tests.StandardSubModelTests(submodel, variables)
         std_tests.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py
index 7e19a8250b..967b62be27 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py
@@ -9,8 +9,15 @@
 
 class TestConstantPorosity(unittest.TestCase):
     def test_public_functions(self):
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
         param = pybamm.LithiumIonParameters()
-        submodel = pybamm.porosity.Constant(param)
+        submodel = pybamm.porosity.Constant(param, options)
         std_tests = tests.StandardSubModelTests(submodel)
         std_tests.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
index b95e76a693..e3c89ddc5d 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
@@ -12,12 +12,21 @@ def test_public_functions(self):
         param = pybamm.LeadAcidParameters()
         a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
         a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"])
+
         variables = {
             "Negative electrode interfacial current density": a_n,
             "Negative electrode sei interfacial current density": a_n,
             "Positive electrode interfacial current density": a_p,
+            "Negative electrode lithium plating interfacial current density": a_n,
+        }
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
         }
-        submodel = pybamm.porosity.Full(param)
+        submodel = pybamm.porosity.Full(param, options)
         std_tests = tests.StandardSubModelTests(submodel, variables)
         std_tests.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
index a2b0cb0b3e..818167f9a3 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
@@ -15,8 +15,17 @@ def test_public_functions(self):
             "X-averaged negative electrode interfacial current density": a,
             "X-averaged negative electrode sei interfacial current density": a,
             "X-averaged positive electrode interfacial current density": a,
+            "X-averaged negative electrode lithium plating"
+            "interfacial current density": a,
         }
-        submodel = pybamm.porosity.LeadingOrder(param)
+        options = {
+            "sei": "ec reaction limited",
+            "sei film resistance": "distributed",
+            "sei porosity change": "true",
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        submodel = pybamm.porosity.LeadingOrder(param, options)
         std_tests = tests.StandardSubModelTests(submodel, variables)
         std_tests.test_all()
 
diff --git a/tests/unit/test_parameters/test_lead_acid_parameters.py b/tests/unit/test_parameters/test_lead_acid_parameters.py
index dce8fec9f9..9b7275dbfa 100644
--- a/tests/unit/test_parameters/test_lead_acid_parameters.py
+++ b/tests/unit/test_parameters/test_lead_acid_parameters.py
@@ -100,7 +100,7 @@ def test_current_functions(self):
     def test_thermal_parameters(self):
         values = pybamm.lead_acid.BaseModel().default_parameter_values
         param = pybamm.LeadAcidParameters()
-        T = 1   # dummy temperature as the values are constant
+        T = 1  # dummy temperature as the values are constant
 
         # Density
         self.assertAlmostEqual(values.evaluate(param.rho_cn(T)), 0.8810, places=2)
diff --git a/tests/unit/test_parameters/test_lithium_ion_parameters.py b/tests/unit/test_parameters/test_lithium_ion_parameters.py
index 16c3a0ad97..6d9eab1996 100644
--- a/tests/unit/test_parameters/test_lithium_ion_parameters.py
+++ b/tests/unit/test_parameters/test_lithium_ion_parameters.py
@@ -167,7 +167,7 @@ def test_thermal_parameters(self):
         values = pybamm.lithium_ion.BaseModel().default_parameter_values
         param = pybamm.LithiumIonParameters()
         c_rate = param.i_typ / 24
-        T = 1   # dummy temperature as the values are constant
+        T = 1  # dummy temperature as the values are constant
 
         # Density
         np.testing.assert_almost_equal(values.evaluate(param.rho_cn(T)), 1.9019, 2)
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 1615a984b8..e658cb1173 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -95,8 +95,9 @@ def test_update(self):
     def test_check_parameter_values(self):
         # Cell capacity [A.h] deprecated
         with self.assertRaisesRegex(ValueError, "Cell capacity"):
-            pybamm.ParameterValues({"Cell capacity [A.h]": 1,
-                                    "Nominal cell capacity [A.h]": 1})
+            pybamm.ParameterValues(
+                {"Cell capacity [A.h]": 1, "Nominal cell capacity [A.h]": 1}
+            )
         with self.assertWarnsRegex(DeprecationWarning, "Cell capacity"):
             pybamm.ParameterValues({"Cell capacity [A.h]": 1})
         # Can't provide a current density of 0, as this will cause a ZeroDivision error

From 7036d5446db35afd7b546ba7213a592e54a8256e Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Tue, 9 Mar 2021 11:14:03 +0000
Subject: [PATCH 14/46] Corrections for tests

---
 .../yang2017_Li_plating/parameters.csv           |  2 +-
 .../full_battery_models/base_battery_model.py    |  4 +---
 .../full_battery_models/lithium_ion/Yang2017.py  |  8 ++------
 .../lithium_ion/basic_dfn_half_cell.py           |  2 +-
 .../full_battery_models/lithium_ion/dfn.py       |  3 ---
 .../full_battery_models/lithium_ion/spm.py       |  1 -
 .../full_battery_models/lithium_ion/spme.py      |  1 -
 .../lithium_plating/irreversible_plating.py      |  2 --
 .../lithium_plating/reversible_plating.py        |  1 -
 .../porosity/full_reaction_driven_porosity.py    |  6 +-----
 .../porosity/leading_reaction_driven_porosity.py | 16 +++-------------
 .../test_lithium_ion/test_spm.py                 |  2 +-
 .../test_irreversible_plating.py                 |  1 +
 .../test_reversible_plating.py                   |  1 +
 .../test_full_reaction_driven_porosity.py        | 11 ++---------
 .../test_leading_reaction_driven_porosity.py     |  7 +++----
 16 files changed, 17 insertions(+), 51 deletions(-)

diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
index bf8e28cef6..310e1d7ada 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
@@ -4,5 +4,5 @@ Name [units],Value,Reference,Notes
 # Lithium plating parameters,,,
 Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
 Lithium plating kinetic rate constant [m.s-1],1.00E+00,,Not required for Yang2017
-Exchange-current density for plating [A.m-2],[function]plating_exchange_current_density_OKane2020,,
+Exchange-current density for plating [A.m-2],0.001,,
 Initial plated lithium concentration [mol.m-3],0.00E+00,,
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 473f52181e..06ebd60d02 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -407,14 +407,12 @@ def options(self, extra_options):
         if options["lithium plating porosity change"] not in ["true", "false"]:
             if options["lithium plating porosity change"] in [True, False]:
                 raise pybamm.OptionError(
-
                     "lithium plating porosity change must now be given in string format"
-
                     "('true' or 'false')"
                 )
             raise pybamm.OptionError(
                 "Unknown lithium plating porosity change '{}'".format(
-                    options["sei porosity change"]
+                    options["lithium plating porosity change"]
                 )
             )
 
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index 5889a33a05..a5d140d391 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -5,8 +5,8 @@
 class Yang2017(DFN):
     def __init__(self, options=None, name="Yang2017", build=True):
         options = {
-            "sei": "ec reaction limited",
-            "sei film resistance": "distributed",
+            "SEI": "ec reaction limited",
+            "SEI film resistance": "distributed",
             "SEI porosity change": "true",
             "lithium plating": "irreversible",
             "lithium plating porosity change": "true",
@@ -17,7 +17,3 @@ def __init__(self, options=None, name="Yang2017", build=True):
     @property
     def default_parameter_values(self):
         return pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
-
-
-
-
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
index 9bfdb6fcaf..9f7c4389ac 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
@@ -434,7 +434,7 @@ def default_spatial_methods(self):
     def new_copy(self, build=False):
         new_model = self.__class__(name=self.name, options=self.options)
         new_model.use_jacobian = self.use_jacobian
-        new_model.use_simplify = self.use_simplify
+        # new_model.use_simplify = self.use_simplify
         new_model.convert_to_format = self.convert_to_format
         new_model.timescale = self.timescale
         new_model.length_scales = self.length_scales
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index afc0ada5f4..c714d0dca4 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -69,9 +69,6 @@ def set_porosity_submodel(self):
         ):
             self.submodels["porosity"] = pybamm.porosity.Full(self.param, self.options)
 
-        
-
-
     def set_active_material_submodel(self):
 
         if self.options["loss of active material"] == "none":
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index 44acf06512..e1b6b3a7c3 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -72,7 +72,6 @@ def set_porosity_submodel(self):
                 self.param, self.options
             )
 
-
     def set_active_material_submodel(self):
 
         if self.options["loss of active material"] == "none":
diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py
index a0f575eba2..6709f502aa 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spme.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spme.py
@@ -74,7 +74,6 @@ def set_porosity_submodel(self):
                 self.param, self.options
             )
 
-
     def set_active_material_submodel(self):
 
         if self.options["loss of active material"] == "none":
diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index 14fbe9c1e2..947085df0d 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -42,14 +42,12 @@ def get_coupled_variables(self, variables):
         phi_e_n = variables[f"{self.domain} electrolyte potential"]
         c_e_n = variables[f"{self.domain} electrolyte concentration"]
         T = variables[f"{self.domain} electrode temperature"]
-        eta_sei = variables[f"{self.domain} electrode sei film overpotential"]
         c_plated_Li = variables[
             f"{self.domain} electrode lithium plating concentration"
         ]
         j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
 
         eta_sei = variables[f"{self.domain} electrode SEI film overpotential"]
-        #C_plating = param.C_plating
 
         phi_ref = param.U_n_ref / param.potential_scale
 
diff --git a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
index 69e876897d..782453f45c 100644
--- a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
@@ -42,7 +42,6 @@ def get_coupled_variables(self, variables):
         phi_e_n = variables[f"{self.domain} electrolyte potential"]
         c_e_n = variables[f"{self.domain} electrolyte concentration"]
         T = variables[f"{self.domain} electrode temperature"]
-        eta_sei = variables[f"{self.domain} electrode sei film overpotential"]
         eta_sei = variables[f"{self.domain} electrode SEI film overpotential"]
         c_plated_Li = variables[
             f"{self.domain} electrode lithium plating concentration"
diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index 3c36c3f02c..e1df79ed1c 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -38,7 +38,7 @@ def get_coupled_variables(self, variables):
 
         if self.options["SEI porosity change"] == "true":
 
-            j_sei_n = variables["Negative electrode sei interfacial current density"]
+            j_sei_n = variables["Negative electrode SEI interfacial current density"]
             beta_sei_n = self.param.beta_sei_n
             deps_n_dt += beta_sei_n * j_sei_n
 
@@ -51,10 +51,6 @@ def get_coupled_variables(self, variables):
 
             deps_n_dt += beta_plating * j_plating
 
-        j_sei_n = variables["Negative electrode SEI interfacial current density"]
-        beta_sei_n = self.param.beta_sei_n
-
-
         deps_s_dt = pybamm.FullBroadcast(
             0, "separator", auxiliary_domains={"secondary": "current collector"}
         )
diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index d50de41b51..f3563c924d 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -39,12 +39,6 @@ def get_coupled_variables(self, variables):
         j_n = variables["X-averaged negative electrode interfacial current density"]
         j_p = variables["X-averaged positive electrode interfacial current density"]
 
-        j_sei_n = variables[
-            "X-averaged negative electrode SEI interfacial current density"
-        ]
-        beta_sei_n = self.param.beta_sei_n
-
-
         deps_n_dt = pybamm.PrimaryBroadcast(
             -self.param.beta_surf_n * j_n, ["negative electrode"]
         )
@@ -52,7 +46,7 @@ def get_coupled_variables(self, variables):
         if self.options["SEI porosity change"] == "true":
 
             j_sei_n = variables[
-                "X-averaged negative electrode sei interfacial current density"
+                "X-averaged negative electrode SEI interfacial current density"
             ]
 
             beta_sei_n = self.param.beta_sei_n
@@ -61,12 +55,8 @@ def get_coupled_variables(self, variables):
         if self.options["lithium plating porosity change"] == "true":
 
             j_plating = variables[
-                "X-averaged negative electrode lithium"
-
-                " plating interfacial current density"
-
-                "plating interfacial current density"
-
+                "X-averaged negative electrode "
+                "lithium plating interfacial current density"
             ]
 
             beta_plating = self.param.beta_plating
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
index 4492b19ce7..072e195a3f 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
@@ -248,7 +248,7 @@ def test_well_posed_reversible_plating_with_porosity(self):
             "lithium plating": "reversible",
             "lithium plating porosity change": "true",
         }
-        model = pybamm.lithium_ion.DFN(options)
+        model = pybamm.lithium_ion.SPM(options)
         model.check_well_posedness()
 
 
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_irreversible_plating.py b/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_irreversible_plating.py
index 18b2d3175c..4f36b7ba75 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_irreversible_plating.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_irreversible_plating.py
@@ -18,6 +18,7 @@ def test_public_functions(self):
             "Negative electrode potential": a_n,
             "Negative electrolyte potential": a_n,
             "Negative electrolyte concentration": a_n,
+            "Negative electrode temperature": a_n,
             "Negative electrode SEI film overpotential": a_n,
         }
         submodel = pybamm.lithium_plating.IrreversiblePlating(param, "Negative")
diff --git a/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_reversible_plating.py b/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_reversible_plating.py
index d7576461bb..f4fa681984 100644
--- a/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_reversible_plating.py
+++ b/tests/unit/test_models/test_submodels/test_interface/test_lithium_plating/test_reversible_plating.py
@@ -23,6 +23,7 @@ def test_public_functions(self):
         )
         variables = {
             "Negative electrode potential": a_n,
+            "Negative electrode temperature": a_n,
             "Negative electrolyte potential": a_n,
             "Negative electrolyte concentration": a_n,
             "Negative electrode lithium plating concentration": a_n,
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
index b3e736f1a2..1935d66fd7 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py
@@ -9,18 +9,11 @@
 
 class TestFull(unittest.TestCase):
     def test_public_functions(self):
-        param = pybamm.LeadAcidParameters()
+        # param = pybamm.LeadAcidParameters()
+        param = pybamm.LithiumIonParameters()
         a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
         a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"])
 
-        options = {
-            "sei": "ec reaction limited",
-            "sei film resistance": "distributed",
-            "sei porosity change": "true",
-            "lithium plating": "irreversible",
-            "lithium plating porosity change": "true",
-        }
-
         variables = {
             "Negative electrode interfacial current density": a_n,
             "Negative electrode SEI interfacial current density": a_n,
diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
index f51c9f512d..137a323feb 100644
--- a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
+++ b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py
@@ -9,16 +9,15 @@
 
 class TestLeadingOrder(unittest.TestCase):
     def test_public_functions(self):
-        param = pybamm.LeadAcidParameters()
+        # param = pybamm.LeadAcidParameters()
+        param = pybamm.LithiumIonParameters()
         a = pybamm.PrimaryBroadcast(pybamm.Scalar(0), "current collector")
         variables = {
             "X-averaged negative electrode interfacial current density": a,
             "X-averaged negative electrode SEI interfacial current density": a,
             "X-averaged positive electrode interfacial current density": a,
-            "X-averaged negative electrode lithium plating"
+            "X-averaged negative electrode lithium plating "
             "interfacial current density": a,
-            "X-averaged negative electrode lithium"
-            "plating interfacial current density": a,
         }
         options = {
             "SEI": "ec reaction limited",

From f5dd4ea0b7e8a07562289004acb3d8908308e8e4 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Thu, 11 Mar 2021 09:25:26 +0000
Subject: [PATCH 15/46] Changes for integration tests

---
 .../test_lithium_ion/test_dfn.py              |  3 ++-
 .../test_lithium_ion/test_spm.py              | 21 +++++++++++--------
 .../test_lithium_ion/test_spme.py             |  8 ++++---
 3 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
index 296664073f..292439c930 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
@@ -177,7 +177,8 @@ def test_well_posed_irreversible_plating_with_porosity(self):
             "lithium plating porosity change": "true",
         }
         model = pybamm.lithium_ion.DFN(options)
-        modeltest = tests.StandardModelTest(model)
+        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+        modeltest = tests.StandardModelTest(model, parameter_values=param)
         modeltest.test_all()
 
 
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
index 072e195a3f..18db5375c7 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
@@ -166,7 +166,18 @@ def test_surface_form_differential(self):
     def test_surface_form_algebraic(self):
         options = {"surface form": "algebraic"}
         model = pybamm.lithium_ion.SPM(options)
-        modeltest = tests.StandardModelTest(model)
+        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+        modeltest = tests.StandardModelTest(model, parameter_values=param)
+        modeltest.test_all()
+
+    def test_well_posed_irreversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        model = pybamm.lithium_ion.SPM(options)
+        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+        modeltest = tests.StandardModelTest(model, parameter_values=param)
         modeltest.test_all()
 
 
@@ -243,14 +254,6 @@ def test_well_posed_both_cracking(self):
         modeltest = tests.StandardModelTest(model, parameter_values=parameter_values)
         modeltest.test_all()
 
-    def test_well_posed_reversible_plating_with_porosity(self):
-        options = {
-            "lithium plating": "reversible",
-            "lithium plating porosity change": "true",
-        }
-        model = pybamm.lithium_ion.SPM(options)
-        model.check_well_posedness()
-
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
index 1465d95430..5629814a3c 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spme.py
@@ -164,13 +164,15 @@ def test_integrated_conductivity(self):
         modeltest = tests.StandardModelTest(model)
         modeltest.test_all()
 
-    def test_well_posed_reversible_plating_with_porosity(self):
+    def test_well_posed_irreversible_plating_with_porosity(self):
         options = {
-            "lithium plating": "reversible",
+            "lithium plating": "irreversible",
             "lithium plating porosity change": "true",
         }
         model = pybamm.lithium_ion.SPMe(options)
-        model.check_well_posedness()
+        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+        modeltest = tests.StandardModelTest(model, parameter_values=param)
+        modeltest.test_all()
 
 
 class TestSPMeWithSEI(unittest.TestCase):

From 1e1806e82005a921d831b203599560d79f5915e2 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Thu, 11 Mar 2021 10:01:29 +0000
Subject: [PATCH 16/46] Update base_battery_model.py

---
 pybamm/models/full_battery_models/base_battery_model.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 04b169664f..732a76b3f3 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -481,7 +481,6 @@ def options(self, extra_options):
 
         options = Options(extra_options)
 
-
         # Options that are incompatible with models
         if isinstance(self, pybamm.lithium_ion.BaseModel):
             if options["convection"] != "none":

From 96302d1cb789aa8f8504f90a8adb9a6a4ac2332d Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Thu, 11 Mar 2021 16:06:12 +0000
Subject: [PATCH 17/46] Integration tests

---
 .../full_battery_models/base_battery_model.py | 233 +-----------------
 .../test_models/standard_output_tests.py      |   4 +-
 2 files changed, 11 insertions(+), 226 deletions(-)

diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 732a76b3f3..51d74cf230 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -172,6 +172,7 @@ def __init__(self, extra_options):
                 "positive",
                 "both",
             ],
+            "lithium plating porosity change": ["true", "false"],
             "particle": [
                 "Fickian diffusion",
                 "fast diffusion",
@@ -207,6 +208,7 @@ def __init__(self, extra_options):
             "SEI": "none",
             "lithium plating": "none",
             "SEI porosity change": "false",
+            "lithium plating porosity change": "false",
             "loss of active material": "none",
             "working electrode": "none",
             "particle cracking": "none",
@@ -269,6 +271,12 @@ def __init__(self, extra_options):
                 "('true' or 'false')"
             )
 
+        if options["lithium plating porosity change"] in [True, False]:
+            raise pybamm.OptionError(
+                "lithium plating porosity change must now be given in string format "
+                "('true' or 'false')"
+            )
+
         if options["dimensionality"] == 0:
             if options["current collector"] not in ["uniform"]:
                 raise pybamm.OptionError(
@@ -405,80 +413,6 @@ def options(self):
 
     @options.setter
     def options(self, extra_options):
-        default_options = {
-            "operating mode": "current",
-            "dimensionality": 0,
-            "surface form": "false",
-            "convection": "none",
-            "side reactions": [],
-            "interfacial surface area": "constant",
-            "current collector": "uniform",
-            "particle": "Fickian diffusion",
-            "particle shape": "spherical",
-            "electrolyte conductivity": "default",
-            "thermal": "isothermal",
-            "cell geometry": "none",
-            "external submodels": [],
-            "SEI": "none",
-            "lithium plating": "none",
-            "SEI porosity change": "false",
-            "lithium plating porosity change": "false",
-            "loss of active material": "none",
-            "working electrode": "none",
-            "particle cracking": "none",
-            "total interfacial current density as a state": "false",
-        }
-        # Change the default for cell geometry based on which thermal option is provided
-        extra_options = extra_options or {}
-        thermal_option = extra_options.get(
-            "thermal", "none"
-        )  # return "none" if option not given
-        if thermal_option in ["none", "isothermal", "lumped"]:
-            default_options["cell geometry"] = "arbitrary"
-        else:
-            default_options["cell geometry"] = "pouch"
-        # The "cell geometry" option will still be overridden by extra_options if
-        # provided
-
-        # Change the default for SEI film resistance based on which SEI option is
-        # provided
-        # extra_options = extra_options or {}
-        sei_option = extra_options.get(
-            "SEI", "none"
-        )  # return "none" if option not given
-        if sei_option == "none":
-            default_options["SEI film resistance"] = "none"
-        else:
-            default_options["SEI film resistance"] = "distributed"
-        # The "SEI film resistance" option will still be overridden by extra_options if
-        # provided
-
-        options = pybamm.FuzzyDict(default_options)
-        # any extra options overwrite the default options
-        for name, opt in extra_options.items():
-            if name in default_options:
-                options[name] = opt
-            else:
-                raise pybamm.OptionError(
-                    "Option '{}' not recognised. Best matches are {}".format(
-                        name, options.get_best_matches(name)
-                    )
-                )
-
-        # If "SEI film resistance" is "distributed" then "total interfacial current
-        # density as a state" must be "true"
-        if options["SEI film resistance"] == "distributed":
-            options["total interfacial current density as a state"] = "true"
-            # Check that extra_options did not try to provide a clashing option
-            if (
-                extra_options.get("total interfacial current density as a state")
-                == "false"
-            ):
-                raise pybamm.OptionError(
-                    "If 'sei film resistance' is 'distributed' then 'total interfacial "
-                    "current density as a state' must be 'true'"
-                )
-
         options = Options(extra_options)
 
         # Options that are incompatible with models
@@ -516,157 +450,6 @@ def options(self, extra_options):
                         self
                     )
                 )
-        if options["surface form"] not in ["false", "differential", "algebraic"]:
-            raise pybamm.OptionError(
-                "surface form '{}' not recognised".format(options["surface form"])
-            )
-        if options["convection"] not in [
-            "none",
-            "uniform transverse",
-            "full transverse",
-        ]:
-            raise pybamm.OptionError(
-                "convection option '{}' not recognised".format(options["convection"])
-            )
-        if options["current collector"] not in [
-            "uniform",
-            "potential pair",
-            "potential pair quite conductive",
-        ]:
-            raise pybamm.OptionError(
-                "current collector model '{}' not recognised".format(
-                    options["current collector"]
-                )
-            )
-        if options["dimensionality"] not in [0, 1, 2]:
-            raise pybamm.OptionError(
-                "Dimension of current collectors must be 0, 1, or 2, not {}".format(
-                    options["dimensionality"]
-                )
-            )
-        if options["thermal"] not in ["isothermal", "lumped", "x-lumped", "x-full"]:
-            raise pybamm.OptionError(
-                "Unknown thermal model '{}'".format(options["thermal"])
-            )
-        if options["cell geometry"] not in ["arbitrary", "pouch"]:
-            raise pybamm.OptionError(
-                "Unknown geometry '{}'".format(options["cell geometry"])
-            )
-        if options["SEI"] not in [
-            "none",
-            "constant",
-            "reaction limited",
-            "solvent-diffusion limited",
-            "electron-migration limited",
-            "interstitial-diffusion limited",
-            "ec reaction limited",
-        ]:
-            raise pybamm.OptionError("Unknown SEI model '{}'".format(options["SEI"]))
-        if options["SEI film resistance"] not in ["none", "distributed", "average"]:
-            raise pybamm.OptionError(
-                "Unknown SEI film resistance model '{}'".format(
-                    options["SEI film resistance"]
-                )
-            )
-        if options["SEI porosity change"] not in ["true", "false"]:
-            if options["SEI porosity change"] in [True, False]:
-                raise pybamm.OptionError(
-                    "SEI porosity change must now be given in string format "
-                    "('true' or 'false')"
-                )
-            raise pybamm.OptionError(
-                "Unknown SEI porosity change '{}'".format(
-                    options["SEI porosity change"]
-                )
-            )
-
-        if options["lithium plating"] not in ["none", "reversible", "irreversible"]:
-            raise pybamm.OptionError(
-                "Unknown lithium plating model '{}'".format(options["lithium plating"])
-            )
-
-        if options["lithium plating porosity change"] not in ["true", "false"]:
-            if options["lithium plating porosity change"] in [True, False]:
-                raise pybamm.OptionError(
-                    "lithium plating porosity change must now be given in string format"
-                    "('true' or 'false')"
-                )
-            raise pybamm.OptionError(
-                "Unknown lithium plating porosity change '{}'".format(
-                    options["lithium plating porosity change"]
-                )
-            )
-
-        if options["loss of active material"] not in [
-            "none",
-            "negative",
-            "positive",
-            "both",
-        ]:
-            raise pybamm.OptionError(
-                "Unknown loss of active material '{}'".format(
-                    options["loss of active material"]
-                )
-            )
-
-        if options["particle cracking"] not in [
-            "none",
-            "no cracking",
-            "negative",
-            "positive",
-            "both",
-        ]:
-            raise pybamm.OptionError(
-                "Unknown particle cracking '{}'".format(options["particle cracking"])
-            )
-
-        if options["dimensionality"] == 0:
-            if options["current collector"] not in ["uniform"]:
-                raise pybamm.OptionError(
-                    "current collector model must be uniform in 0D model"
-                )
-            if options["convection"] == "full transverse":
-                raise pybamm.OptionError(
-                    "cannot have transverse convection in 0D model"
-                )
-        if options["particle"] not in [
-            "Fickian diffusion",
-            "fast diffusion",
-            "uniform profile",
-            "quadratic profile",
-            "quartic profile",
-        ]:
-            raise pybamm.OptionError(
-                "particle model '{}' not recognised".format(options["particle"])
-            )
-        if options["particle"] == "fast diffusion":
-            raise NotImplementedError(
-                "The 'fast diffusion' option has been renamed. "
-                "Use 'uniform profile' instead."
-            )
-        if options["particle shape"] not in ["spherical", "user", "no particles"]:
-            raise pybamm.OptionError(
-                "particle shape '{}' not recognised".format(options["particle shape"])
-            )
-
-        if options["thermal"] == "x-lumped" and options["dimensionality"] == 1:
-            warnings.warn(
-                "1+1D Thermal models are only valid if both tabs are "
-                "placed at the top of the cell."
-            )
-
-        if options["electrolyte conductivity"] not in [
-            "default",
-            "full",
-            "leading order",
-            "composite",
-            "integrated",
-        ]:
-            raise pybamm.OptionError(
-                "electrolyte conductivity model '{}' not recognised".format(
-                    options["electrolyte conductivity"]
-                )
-            )
 
         self._options = options
 
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 783edb6866..2a4ab07d4f 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -338,7 +338,9 @@ def test_conservation(self):
         elif self.model.options["surface form"] == "differential":
             np.testing.assert_array_almost_equal(diff, 0, decimal=10)
         elif self.model.options["SEI"] == "ec reaction limited":
-            np.testing.assert_array_almost_equal(diff, 0, decimal=12)
+            np.testing.assert_array_almost_equal(diff, 0, decimal=11)
+        elif self.model.options["lithium plating"] == "irreversible":
+            np.testing.assert_array_almost_equal(diff, 0, decimal=14)
         else:
             np.testing.assert_array_almost_equal(diff, 0, decimal=15)
 

From 205b74d88eb9356b3174f12983e6d33e5d23ce8c Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 12 Mar 2021 10:41:39 +0000
Subject: [PATCH 18/46] Changes for Integration tests

---
 .../integration/test_models/standard_output_tests.py  |  5 +++++
 .../test_base_battery_model.py                        | 11 +++++++++++
 2 files changed, 16 insertions(+)

diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 2a4ab07d4f..e3c0a3d298 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -376,6 +376,11 @@ def test_fluxes(self):
                     # sign, so ignore first three times
                     np.testing.assert_array_less(0, self.N_s_n(t[3:], x_n, r_n[1:]))
                     np.testing.assert_array_less(self.N_s_p(t[3:], x_p, r_p[1:]), 0)
+                elif self.model.name == "Yang2017":
+                    np.testing.assert_array_less(
+                        -1e-16, self.N_s_n(t[1:], x_n, r_n[2:])
+                    )
+                    np.testing.assert_array_less(self.N_s_p(t[1:], x_p, r_p[1:]), 1e-16)
                 else:
                     np.testing.assert_array_less(
                         -1e-16, self.N_s_n(t[1:], x_n, r_n[1:])
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index a8d84a5332..4eb52c97bf 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -30,6 +30,7 @@
 'SEI': 'none' (possible: ['none', 'constant', 'reaction limited', 'solvent-diffusion limited', 'electron-migration limited', 'interstitial-diffusion limited', 'ec reaction limited'])
 'lithium plating': 'none' (possible: ['none', 'reversible', 'irreversible'])
 'SEI porosity change': 'false' (possible: ['true', 'false'])
+'lithium plating porosity change': 'false' (possible: ['true', 'false'])
 'loss of active material': 'negative' (possible: ['none', 'negative', 'positive', 'both'])
 'working electrode': 'none'
 'particle cracking': 'none' (possible: ['none', 'no cracking', 'negative', 'positive', 'both'])
@@ -226,6 +227,16 @@ def test_options(self):
         with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"):
             pybamm.BaseBatteryModel({"lithium plating": "bad plating"})
 
+        with self.assertRaisesRegex(
+            pybamm.OptionError, "lithium plating porosity change"
+        ):
+            pybamm.BaseBatteryModel(
+                {
+                    "lithium plating porosity change": "bad lithium "
+                    "plating porosity change"
+                }
+            )
+
     def test_build_twice(self):
         model = pybamm.lithium_ion.SPM()  # need to pick a model to set vars and build
         with self.assertRaisesRegex(pybamm.ModelError, "Model already built"):

From 7d9c0a9218344217b6ffdcc27279a162102c61bf Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 12 Mar 2021 12:25:28 +0000
Subject: [PATCH 19/46] Doc string corrections

---
 pybamm/parameters/lithium_ion_parameters.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 962bfaab3b..ca4a173da6 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -1,3 +1,4 @@
+#
 # Standard parameters for lithium-ion battery models
 #
 import pybamm
@@ -7,6 +8,7 @@
 class LithiumIonParameters:
     """
     Standard parameters for lithium-ion battery models
+
     Layout:
         1. Dimensional Parameters
         2. Dimensional Functions
@@ -14,11 +16,14 @@ class LithiumIonParameters:
         4. Dimensionless Parameters
         5. Dimensionless Functions
         6. Input Current
+
     Parameters
     ----------
+
     options : dict, optional
         A dictionary of options to be passed to the parameters. The options that
         can be set are listed below.
+
             * "particle shape" : str, optional
                 Sets the model shape of the electrode particles. This is used to
                 calculate the surface area to volume ratio. Can be "spherical"

From c814e39d563f2578aea3c7755ff3bd2609f739ea Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 15 Mar 2021 10:21:40 +0000
Subject: [PATCH 20/46] Changes for tests

---
 examples/notebooks/using-submodels.ipynb      |  2 +-
 examples/scripts/custom_model.py              |  2 +-
 pybamm/CITATIONS.txt                          | 11 +++---
 pybamm/__init__.py                            |  4 +--
 .../okane2020_Li_plating/README.md            |  7 ++++
 .../okane2020_Li_plating/parameters.csv       |  8 +++++
 ...ting_exchange_current_density_OKane2020.py | 29 +++++++++++++++
 ...ping_exchange_current_density_OKane2020.py | 35 +++++++++++++++++++
 .../graphite_Chen2020_plating/parameters.csv  |  9 +++--
 pybamm/parameters/parameter_sets.py           |  1 +
 10 files changed, 93 insertions(+), 15 deletions(-)
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py

diff --git a/examples/notebooks/using-submodels.ipynb b/examples/notebooks/using-submodels.ipynb
index 30f5f6b29f..152f80b193 100644
--- a/examples/notebooks/using-submodels.ipynb
+++ b/examples/notebooks/using-submodels.ipynb
@@ -348,7 +348,7 @@
    "source": [
     "model.submodels[\"current collector\"] = pybamm.current_collector.Uniform(model.param)\n",
     "model.submodels[\"thermal\"] = pybamm.thermal.isothermal.Isothermal(model.param)\n",
-    "model.submodels[\"porosity\"] = pybamm.porosity.Constant(model.param)\n",
+    "model.submodels[\"porosity\"] = pybamm.porosity.Constant(model.param, model.options)\n",
     "model.submodels[\"negative active material\"] = pybamm.active_material.Constant(\n",
     "    model.param, \"Negative\", model.options\n",
     ")\n",
diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py
index d5a838ea2a..777023be9b 100644
--- a/examples/scripts/custom_model.py
+++ b/examples/scripts/custom_model.py
@@ -16,7 +16,7 @@
 )
 model.submodels["current collector"] = pybamm.current_collector.Uniform(model.param)
 model.submodels["thermal"] = pybamm.thermal.isothermal.Isothermal(model.param)
-model.submodels["porosity"] = pybamm.porosity.Constant(model.param)
+model.submodels["porosity"] = pybamm.porosity.Constant(model.param, model.options)
 model.submodels["negative active material"] = pybamm.active_material.Constant(
     model.param, "Negative", model.options
 )
diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt
index fbbd800084..c840a34141 100644
--- a/pybamm/CITATIONS.txt
+++ b/pybamm/CITATIONS.txt
@@ -350,16 +350,15 @@ doi={10.1149/2.0661810jes}
 
 
 @article{Yang2017,
-  author = {Yang, Xiao-Guang and Leng, Yungjun and Zhang, Guangsheng
-            and Ge, Shanhai and Wang, Chao-Yang},
-  title = {Modeling of lithium plating induced aging of lithium-ion batteries: 
-  Transition from linear to nonlinear aging},
-  journal = {Power Sources},
+  author = {Yang, Xiao Guang and Leng, Yongjun and Zhang, Guangsheng and Ge, Shanhai and Wang, Chao Yang},
+  title = {Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging},
+  journal = {Journal of Power Sources},
   volume = {360},
   pages = {28--40},
   year = {2017},
   publisher = {Elsevier},
-  url = https://doi.org/10.1016/j.jpowsour.2017.05.110
+  doi = {10.1016/j.jpowsour.2017.05.110},
+}
 
 
 @article{Ramadass2004,
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 404b2933ef..67b982c38b 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -245,9 +245,9 @@ def version(formatted=False):
 # Define the plot-style string and set the default plotting style (can be overwritten
 # in a specific script)
 default_plot_style = os.path.join(root_dir(), "pybamm/plotting/pybamm.mplstyle")
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 
-plt.style.use(default_plot_style)
+#plt.style.use(default_plot_style)
 #
 # Simulation
 #
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/README.md b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/README.md
new file mode 100644
index 0000000000..ce38eb38c0
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/README.md
@@ -0,0 +1,7 @@
+# Lithium plating parameters
+
+Some example parameters for lithium plating from the paper:
+
+> O’Kane, S. E. J., Campbell, I. D., Marzook, M. W. J., Offer, G. J., & Marinescu, M. (2020) Physical origin of the differential voltage minimum associated with lithium plating in Li-ion batteries. Journal of The Electrochemical Society 167(3), 090540.
+
+Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit plating models to observed experimental data in the referenced papers.
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/parameters.csv
new file mode 100644
index 0000000000..9af2c03d07
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/parameters.csv
@@ -0,0 +1,8 @@
+Name [units],Value,Reference,Notes
+,,,
+,,,
+# Lithium plating parameters,,,
+Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
+Exchange-current density for plating [A.m-2],[function]plating_exchange_current_density_OKane2020,,
+Exchange-current density for stripping [A.m-2],[function]stripping_exchange_current_density_OKane2020,,
+Initial plated lithium concentration [mol.m-3],0.00E+00,,
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
new file mode 100644
index 0000000000..1133e1aa51
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -0,0 +1,29 @@
+from pybamm import constants
+
+
+def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li plating reaction [A.m-2].
+    References
+    ----------
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_plating = 1e-10
+
+    return constants.F * k_plating * c_e ** 0.5
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
new file mode 100644
index 0000000000..98022ee856
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
@@ -0,0 +1,35 @@
+from pybamm import constants
+
+
+def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li stripping reaction [A.m-2].
+
+    References
+    ----------
+
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+
+    Parameters
+    ----------
+
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_plating = 1e-10
+
+    return constants.F * k_plating * c_Li ** 0.5
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv
index 726667b9cc..e0cf4d11ad 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv
@@ -10,7 +10,7 @@ Negative electrode OCP [V],[data]graphite_LGM50_ocp_Chen2020,Chen 2020,
 # Microstructure,,,
 Negative electrode porosity,0.25,Chen 2020,
 Negative electrode active material volume fraction,0.75,Chen 2020,
-Negative particle radius [m],5.86E-6,Chen 2020,
+Negative particle radius [m],5.86E-06,Chen 2020,
 Negative electrode Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical
 Negative electrode Bruggeman coefficient (electrode),1.5,default,
 ,,,
@@ -30,7 +30,6 @@ Negative electrode specific heat capacity [J.kg-1.K-1],700,default,
 Negative electrode thermal conductivity [W.m-1.K-1],1.7,default,
 Negative electrode OCP entropic change [V.K-1],0,,
 ,,,
-# Lithium plating parameters,,,
-Lithium metal partial molar volume [m3.mol-1],1.3E-5,,
-Lithium plating kinetic rate constant [m.s-1],1E-10,,
-Initial plated lithium concentration [mol.m-3],0,,
+,,,
+,,,
+,,,
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 27d853d711..05f4d150f7 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -141,6 +141,7 @@
     "electrolyte": "lipf6_Nyman2008",
     "experiment": "1C_discharge_from_full_Chen2020",
     "sei": "example",
+    "lithium plating": "okane2020_Li_plating",
     "citation": "Chen2020",
 }
 

From 9f9744e4122e23ae28bdb7b2be8fccf7ead36208 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 15 Mar 2021 10:23:41 +0000
Subject: [PATCH 21/46] Changes for tests

---
 pybamm/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 67b982c38b..404b2933ef 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -245,9 +245,9 @@ def version(formatted=False):
 # Define the plot-style string and set the default plotting style (can be overwritten
 # in a specific script)
 default_plot_style = os.path.join(root_dir(), "pybamm/plotting/pybamm.mplstyle")
-#import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
 
-#plt.style.use(default_plot_style)
+plt.style.use(default_plot_style)
 #
 # Simulation
 #

From aa89cfb3de113a6ad9cdbd0c8587ff8603b06b76 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 15 Mar 2021 13:35:00 +0000
Subject: [PATCH 22/46] Changes for tests

---
 pybamm/CITATIONS.txt                                   | 1 +
 tests/integration/test_models/standard_output_tests.py | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt
index efbb7388f9..a870a8aff6 100644
--- a/pybamm/CITATIONS.txt
+++ b/pybamm/CITATIONS.txt
@@ -371,6 +371,7 @@ doi={10.1149/2.0661810jes}
   year={2004},
   publisher={IOP Publishing},
   doi={10.1149/1.1634273},
+  }
 
 
 @article{Newman1962,
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 9dec6b315a..166bee579d 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -631,7 +631,7 @@ def test_interfacial_current_average(self):
                 axis=0,
             ),
             self.i_cell / self.l_n,
-            decimal=4,
+            decimal=3,
         )
         np.testing.assert_array_almost_equal(
             np.mean(

From 09b1371e5673514af7ccdfa6ecb8cd3440b9581a Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 15 Mar 2021 14:33:04 +0000
Subject: [PATCH 23/46] Changes for tests

---
 examples/notebooks/lithium-plating.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/notebooks/lithium-plating.ipynb b/examples/notebooks/lithium-plating.ipynb
index 7b8293ad5e..a126433fdb 100644
--- a/examples/notebooks/lithium-plating.ipynb
+++ b/examples/notebooks/lithium-plating.ipynb
@@ -57,7 +57,6 @@
     "parameter_values.update({\"Ambient temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Initial temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})\n",
-    "parameter_values.update({\"Lithium plating kinetic rate constant [m.s-1]\": 1E-9})"
    ]
   },
   {

From 5e127a0cf3c39324ae76a43043c4cb52f1b5a648 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 15 Mar 2021 15:37:43 +0000
Subject: [PATCH 24/46] Changes for tests

---
 examples/notebooks/lithium-plating.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/notebooks/lithium-plating.ipynb b/examples/notebooks/lithium-plating.ipynb
index a126433fdb..5b30ddc080 100644
--- a/examples/notebooks/lithium-plating.ipynb
+++ b/examples/notebooks/lithium-plating.ipynb
@@ -56,7 +56,7 @@
     "parameter_values.update({\"Reference temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Ambient temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Initial temperature [K]\": 268.15})\n",
-    "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})\n",
+    "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})"
    ]
   },
   {

From a9407826ea8f81842e15650dacb5fca3c4519099 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Mon, 15 Mar 2021 16:54:33 +0000
Subject: [PATCH 25/46] Changes for tests

---
 .../negative_electrodes/graphite_Yang2017/README.md           | 2 +-
 pybamm/parameters/parameter_sets.py                           | 2 +-
 pybamm/plotting/quick_plot.py                                 | 4 ----
 pybamm/solvers/solution.py                                    | 4 +---
 4 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
index bf092e7d95..a6f87dd6c3 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/README.md
@@ -4,7 +4,7 @@ Parameters for a graphite negative electrode, from the papers:
 
 > Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery I. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
 
->Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
 
 The fits to data for the electrode and electrolyte properties are those provided
 by Dr. Simon O’Kane in the paper:
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 05f4d150f7..563fbf07fe 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -141,7 +141,6 @@
     "electrolyte": "lipf6_Nyman2008",
     "experiment": "1C_discharge_from_full_Chen2020",
     "sei": "example",
-    "lithium plating": "okane2020_Li_plating",
     "citation": "Chen2020",
 }
 
@@ -154,6 +153,7 @@
     "electrolyte": "lipf6_Nyman2008",
     "experiment": "1C_discharge_from_full_Chen2020",
     "sei": "example",
+    "lithium plating": "okane2020_Li_plating",
     "citation": "Chen2020",
 }
 
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index b75adfdcca..99bf226c77 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -11,10 +11,6 @@ class LoopList(list):
     """A list which loops over itself when accessing an
     index so that it never runs out"""
 
-    """
-    A list which loops over itself when accessing an index so that it never runs out
-    """
-
     def __getitem__(self, i):
         # implement looping by calling "(i) modulo (length of list)"
         return super().__getitem__(i % len(self))
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index e4fa42efa2..031fb27898 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -441,9 +441,7 @@ def save_data(self, filename, variables=None, to_format="pickle", short_names=No
     def sub_solutions(self):
         """List of sub solutions that have been
         concatenated to form the full solution"""
-        """
-        List of sub solutions that have been concatenated to form the full solution
-        """
+
         return self._sub_solutions
 
     def __add__(self, other):

From 8cac84b8bc6f8b5bd06b267907b7b7479e4e26a8 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Wed, 17 Mar 2021 20:40:07 +0000
Subject: [PATCH 26/46] Update
 pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../plating_exchange_current_density_OKane2020.py               | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
index 1133e1aa51..0658664705 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -26,4 +26,4 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
 
     k_plating = 1e-10
 
-    return constants.F * k_plating * c_e ** 0.5
+    return constants.F * k_plating * c_e

From 32b9ee2014525b279c8e56875bc2310d0b319367 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Wed, 17 Mar 2021 20:40:19 +0000
Subject: [PATCH 27/46] Update
 pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../stripping_exchange_current_density_OKane2020.py             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
index 98022ee856..cad0f0ad5a 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
@@ -32,4 +32,4 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
 
     k_plating = 1e-10
 
-    return constants.F * k_plating * c_Li ** 0.5
+    return constants.F * k_plating * c_Li

From 6daaed5789553cf2b34f85f3e8672e1782ccf382 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Wed, 17 Mar 2021 20:41:24 +0000
Subject: [PATCH 28/46] Changes for tests

---
 examples/notebooks/lithium-plating.ipynb      | 30 ++++++++++++-------
 ...ting_exchange_current_density_OKane2020.py |  2 +-
 ...ping_exchange_current_density_OKane2020.py |  2 +-
 3 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/examples/notebooks/lithium-plating.ipynb b/examples/notebooks/lithium-plating.ipynb
index 5b30ddc080..4d09ad2151 100644
--- a/examples/notebooks/lithium-plating.ipynb
+++ b/examples/notebooks/lithium-plating.ipynb
@@ -18,8 +18,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.0.1 is available.\n",
-      "You should consider upgrading via the '/home/ferranbrosa/PyBaMM/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -56,7 +54,19 @@
     "parameter_values.update({\"Reference temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Ambient temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Initial temperature [K]\": 268.15})\n",
-    "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})"
+    "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})\n",
+    "\n",
+    "# change plating reaction rate\n",
+    "def j0_plating(c_e, c_Li, T):\n",
+    "    k_plating = 1e-9\n",
+    "    return pybamm.constants.F * k_plating * c_e\n",
+    "\n",
+    "def j0_stripping(c_e, c_Li, T):\n",
+    "    k_plating = 1e-9\n",
+    "    return pybamm.constants.F * k_plating * c_Li\n",
+    "\n",
+    "parameter_values.update({\"Exchange-current density for plating [A.m-2]\": j0_plating})\n",
+    "parameter_values.update({\"Exchange-current density for stripping [A.m-2]\": j0_stripping})"
    ]
   },
   {
@@ -74,7 +84,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x7f8be98ce190>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x7f345364ca10>"
       ]
      },
      "execution_count": 3,
@@ -167,7 +177,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7f8bdcb50a90>"
+       "<pybamm.solvers.solution.Solution at 0x7f344f114890>"
       ]
      },
      "execution_count": 5,
@@ -252,7 +262,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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ISJIk6RoSGxuLv7//Vbueoihf18U+1zv77t1pOm0aGisrAPI2bybypkHkrF1bo+OTig1klxpJKynFqiwjajQYcfGyw6uZk7qfXD5Nkq4Pffv2ZdKkSTz++OMEBwfzxRdfAPDss8/ywAMPsHHjRpo2bQpAREQEq1evpkWLFhXOYTAYWLZsGYsXL2bKlClqe8uWLVm1ahUPP/wwH3300UXXzsrK4vfff2f16tXq36dOncpXX33F8uXLsba+OHhOT0/n3Xff5d9//2XDhg08//zzVT6/8+fPM336dGbPng2AtbU1S5cupUOHDqxatYrNmzezdetWZs+eTXp6unrcokWLmDlzJl9++SXh4eEMGzaMr7/++poKPkFmQC8S5h5GVHYUsZ7gkhtPelwefi1dG7pbkiRJ14yqMpb1zd/fn5iYGFq1anVVryuEGA+sVBQlRwjxDtAFeFVRlHod/3utMsTHoxQXo/PwqNH+TW2sWN+lJXlGk1qAaL+zoM2zbQlysgPAZDSx8KN9BLX3IOLmQFmoSJLqyf0f9a6w7R3kdFFb2/5+tO1fcYTKTZPCa3WdyZMnM3nyZHJycujbty+33nrrJffr2bPnJdu7dOkCQGBgINnZ2Wp7165dAejWrRtz58696LgOHTqg1Wpp0qQJWVlZAERGRqrn69atmzrt44KoqCjatWuHfdm890sNoy0/B75NmzY4OVlunl14DkePHuX48eMMGDAAgJycHGJjYwHo3LkzAAEBARWC0muRzID+R5ibeRmWs94C39RY4uJyGrhHkiRJUk3df//9fPDBB+TlmUevFBYWsnXr1qtx6dfKgs+uwBDgF+CGz3xWxvWuuwjdsB67jh0BMBUWkvLllxhzcys9xkarwcPKfN88w1DKlJMxjDkURZ7RZG5LzCc7tZDMpAIZfErSNS4hIYGcHPN3cEdHRxwcHFAUBSsrK0pLSyvsW9mcyQvz/2NiYioEe3v37gVgz549F2VNgUu+f4SEhFQ47r9CQ0M5cuQIhYXmZaNMJpP6s7i4mIKCAk6cOFFpny9sh4WF0bFjRzZs2MDGjRs5cOAAHTp0uKhfF4LZS70e1wKZAf2PcHfz3ZlzPoJukdEczyxgRAP3SZIkSbq0hx56iO3bt1NcXMzevXtZsmQJTzzxBDfffLP6Yf3aa69dja5cmPQ6GPhBUZTfhBBVj8G6welcLaOLMn7+mfRvv0MpLsH7xReqPdZVp+XzVgFklpbiqDN/cbPztWfiO90xGS1ZhsMbYrF1sCK0sxdCI4NSSbpWxMXFMWXKFDQaDaWlpYwcOZKQkBB0Oh3ffPMNR48e5ZtvvqnyHHZ2dowYMYKEhAQ+//xztT0qKoqhQ4dSWFjI77//XqP+vP/++0yePBkPDw+cnZ1p1qxZhb+7ubnxyiuv0L9/f+zs7NQ5oE8++STdu3enQ4cOF9UsuJQ2bdowaNAg+vXrh1arxdbWlqVLl1a6/y233MIbb7xBWFgY33//fY2eS2MgqiqJfi2JiIhQLtyZuBJ5JXn0+L0HwSlaXpun4Ye7vmLG8z3RamWyWJIk6VIMBnPspddfmwXbLtV/IcQ+RVEianoOIcQ+4GPgNeBWRVHOCSGOKorSpo67W6/q6rO0tkz5+aT/+CNukyahLctUGHNz0To61vgcDx49R06pkRnhgXhY6SgpLGXOy9vQ6AT3vNsTa1t5z12SautafX+fM2cOcXFxF92AnDRpEg8++CC9e/eu5MhLMxgM6mvw0EMPMXToUO6444466++1rrJ/J5V9lsqo6j8crBxo5tSMWA8TNiWFGEwZZCUVNHS3JEmSpMbtKeAuzNnPc0KI5sCGBu7TNUNjb4/n00+rwWfRqdNE9h9ARg2XRsg3GkkpKSWtpBSXsoyola2OMS92ZuA9YWrwmZGYT2pM5cN8JUmSLuXIkSP06dOHHj16kJeXx6hRoxq6S9c0eTvwEsLcwojOiSbBHewK4kmLzcW96aVLxUuSJEmSoijbgVHlts9gDkqviBBiEvAwoABPKYqyv9zfegLfA82BUEVR4sraA4HZgDWwXFGU96+0H1ebIT4OtNoKw3SrYq/VsqRjKOmGUnRlQ203ZuRQYGVieHtLoaNtf54h5ngGY17sjE+wc730XZKkhndhuZX/mjNnzmWdr1OnTmzZsuXyOyRVIDOglxDmXlaIyEfglh0nl2KRJEmSqiSE+EwI4SyE0Akhtggh8oUQE6/wnK7A00B/YCLw1X92OQb0AHb+p/1D4E1FUXoBA4UQV7ckcB1wHDiQ0DWrcRw+HADFZCL1q68pTU2t9BiNEHhamYd/GRWFl0/H8cDR80QWFJvPoSiE92lCi67eeAeZM60mk0JBTkk9PxtJkiSpPBmAXsKFSrjnvAVNU2KJS5ABqCRJklSlQYqiZANDgXigBXClRYi6AlsURSlRFOUc4CiEUBegUxQlW1GUS31AdVAU5cKt+uVAvyvsR4PQuriohaRy/vmHtBkzSHqvZslcrRDMbB3ImyFNaG5vA0CB0YRPO3cGT26tnvfUzkTmvr6DyH0p9fMkJEmSpIvIIbiXcCEAjWqiYcihGLZmF6AoiizrLkmSJFWnL7BIUZR4IcSVVvlzBzLLbWcBbkBiNceVv7mcBfhUtbMQ4mHMw3wJCAiobR+vCqdhwzAkJeM0bKjaZszORutc+TDado52tHO0U7c/PZ/E3ylZzGkbpLYbSxW0Og0efpZpNvLzXpIkqX7JDOh/ZBcaKCy2pol9E6K9Bb5pyaSIQgqy5RAdSZIkqVIpQohvgfHAWiGEDrj04nQ1lwG4lNt2Lmurjqk2xyiKMlNRlAhFUSI8PT1r3cmrQVhZ4fHwQ1iVBciGlBQiBw8h+aOPa3wOk2KeSBtkqyaRadO3Kfd92BMXb3NAWpBTwvx3dsuMqCQ1sOzsbPr370///v1xcXGhR48e9O/fn4ULF1bYb86cOep6oZWZNGnS1VoPulpfffXfmRQ3pnoPQIUQLYQQBiFE7/+03y6EOCGEKPpP+yQhxF4hxA4hxKf13b/yYjMKaP/2Gl5fcoww9zCKdQqJrqArTpTzQCVJkhqpw4cPM3z4cPr370/Pnj2ZNm0aAJMnT+bEiRMMHjyY3r170717d1auXFlf3bgbOAXcqShKJuAHTLvCc+4Cegsh9EKIACBPUZTiGhx3qKxAEcBwYPMV9qPWfjr6E6vOr8KkmKrf+TKUJiWhdXBAW8MiRQBvN2/K1m6t1HVDD+UW8PLpOHKwJKrjTmaQmZhPdqqsfi9JDcnZ2ZmNGzeyceNGOnTowJ9//snGjRsvWvqkJgFoXTIajRW2TabavcfJANTsagzBfR3YdIn2zUBH4Oh/2t8C2iiKkieE2CiECFMU5UQ99xGAJi62WOk0RKXmcVfHcP6N+ZdzPgLH/DjS4nJp1sb9anRDkiTpmnb29tEY09Lq5FxaDw+CFy+q9O/Z2dlMnDiRxYsXExISgqIorFmzBpPJRHR0NKGhocyaNYvAwEDS0tLo1asXw8sK29QlRVFShRCzgdCy7fPAnCs8Z6YQYgbmz1AF+J8QogMwWFGUT4QQLYAZQHvgdyHEb4qifAtMBX4UQlgBK6/WZ+gF2cXZTD84HQe9A/38+mGrs63za9i2a0fw8n8QWkuSOfWb6TjeNBCbsLBKj7Mvt/830SksS82iv6sjwzzNQ3lbdPXBw98RR3cbdb+dS6IIaO1Gk+Y1D3Yl6Xq14L3ddVa4y87JivGvdq3RviaTibFjx5KSkoJGo2HWrFnExMRw8OBBxo4dS0REBG+++Sbjx4/HaDRiMBj46aefaNGiRaXnfOmll9i8eTM2Nja8/PLLWFtbM3fuXH744QcAQkNDiYyMZM6cOSxfvhyDwUDfvn1ZunQpnTt35ujRo3z11Vd88sknREVFYTAYmDZtGl27dmXSpEno9XoSEhJIT09n6dKlrFu3jvj4ePr378/gwYN59dVX6+R1vBbVawAqhOgGJAHG//5NUZT0sn3++6eTmAstlABWmOevXBVajSDYw57TybmEOpuLBp71EXhlyEq4kiRJjdHy5csZOXIkISEhgPkzZejQoWzbto3u3buj1+sJDAwEwNbWFo2mfgb+CCFuxrwkihEIFEJEYK5EO/JKzqsoymzMS6qUd7Dsb6eBQZc45iww4EqueyWcrZ2Zd/M8kvKT1ODzWNoxkguSGeA/oM7mV2psLYFt4aFDpH3zDXnr1xP418IaXePLsAAGuDsy1MNcEbfUpLAjK48+vo7qPmlxuexbFU30sXTGvdJFzg2VpAbyyy+/0LZtW9544w02b97Miy++yKJFi+jQoQNz587Fz88Pg8HAypUrsbKyYuXKlXz44YfMnv3ft0+zFStWEBsby/bt2xFCYDQaq1xmJS8vjxUrViCEYOnSpURERPDZZ5/x3XffERoayg8//EBycjKjR49m27ZtALRu3ZpZs2bx/vvv88cff/Dkk0/yxhtvsHHjxvp4ia4p9Z0BfRW4H/isFsfMBQ4ARcAfiqJUWmyhPgonhHo5cDIpF3sCATjnDZ13xhKfKANQSZKkmqgqY1nXYmNj8ff3v6h96dKljB49ukLblClTePHFF+urK28DXYCVAIqi7BVChNTXxRq7lm4taenWUt2etm8au5N2M2fYHDp7d67z69m0a0fTLz5H5+2tBomGhAR0Pj6ISm462Gk13O1rGdn0a2I6U0/H8WqwL0818wbAvakDtz7dAZ2VRj1vwpks7JytcPGyu+R5Jel6VtOMZV2LiopiwoQJAPTs2ZNHH330on2ysrJ44oknSEpKoqSkBEdHx4v2ueDo0aMMGGC5IabVaqu8wdS9e/cKf+/Z0zzL4ciRI2zfvp1Vq1YB5lE5F3TubH6vCwgIICoqqqZP9YZQb3NAhRAjgL0XMp01PMYR8xDclkAIEC6EqPRfen0UTgj1MlfCS8/R42XrxVlfLUEJ0UQWFmIouSiRK0mSJDUgf39/YmJiLmrfs2cPXbtaPj7eeecdnJycuP/+++utL4qiJP2nqSbzNW8Iz3R6hvtb308nr04AlJpKSS+s8deDagkhcBo2DLuOHQEwFRYSPfEeoifeg1JSs+GC7R1s6eFiz21eLmqbQVHwD3fDN9TcZiw1sW7Oceb/X90NQ5QkqXohISFs374dgO3bt9OypfkGl5WVFaWlpQDMnTuXjh07snnzZt544w0UpfJC5G3atGHTJssMQZPJhJubG3FxcQAcPHhQPS+YA9TyLmy3bt2ae++9V52vun//fnWf8gHrhb7odLpazxu9HtVnEaIOQH8hxCpgMPCpEKJZNceYgBLMhRaMmMvPX9VJFxcC0MiUPMLcwyi0Vsi1LaGEDDLi869mVyRJkqRqjBgxgmXLllW4uzxr1ixCQ0PVD/9vvvmGM2fO8Mknn9RnV3KFEN6Y52oihOjPVZxC0ti19WzLsxHPqv9N/jj1ByMXj2RzXP3URzLm5mIVFIR18+YIK6saHdPJ2Z7FHZsTUFYlN6GohC47jvNTvGU+sxAQcXMg7Qf5Y+dkPm9xgYGifEPdPwlJklT33HMPhw4dom/fvrz++ut89NFHAIwePZoHHniA119/nSFDhjB//nyGDx/O+vXrqzzfzTffTJMmTejRowcDBw5k7dq1tG3bFicnJ/r168eiRYvQ6aofKPrQQw9x6tQpBgwYwIABA6qd13nHHXcwYsSIG74Ykajq7kCdXUSIOcAPgAPgqSjKr0KIPsCbQE9gOzBDUZRFQoingHsAA3AGeKAsGK1SRESEsnfv3ivu68mkHIZ9sYVb2zehZdh2vjv0HVMWG4kKeYy7R42mdZ+mV3wNSZKk64nBYP7yrdfrG+T6hw4d4qWXXqKoqIiSkhLGjBlDixYtGDlyJCkpKfj6+tKjRw/1y8S///5b4W72pfovhNinKEpETftQNlrnOyAIOAQ0B25VFGVfHTzFq6auPkurM+/EPL4/9D2/3/I7TR3q53NVURQUgwFNWQCaOmMGOjc3XMaOrVC8qDJr0rJ59Hg0LwX58Ii/V6X7bf3zDCd3JDLi8XZqplSSrhcN/f4uXRsq+3dS2Wfp1aiCi6Ioky7RtoVLF0/4Gvj6KnTrkoI87NEIcwZ0VC9zJb1z3gLnHFmISJIkqTFq3769Ov8G4I8//mDQIPPHi5eX10Vl8+uDoii7hRADMN9UFcB2RVGy6v3C16gJYRMY3Xy0WqQoOT+Zbw99y1Mdn8Ldtm4qzgsh1OynMSeHjB9nI+xscbplJFoH+2qPH+LhzI5uYbjoLcHqW5HxDPNwpruLg9pmY6/H2k6Hu5+lzWRS0GhkwSJJkqRLuSoB6LXEWqclwM2Os2l5tHLrAMA5Hxh8JJbUmNyG7ZwkSZJUrXHjxjXIdRVFyaasCJFUvfLLs8w5Noe/zvxFiEsI94TfU+fX0jo5EfT3EgzxCWrwWXz2HBp7O/Te3pUe521tuZt/IKeA72JT2Z2dz/JOzdXhxBE3B9JpaAAarXlWU9LZbNb+dJy+d7agWWu5fJskSdc/RVFqVSVcBqCXEOLpwPn0AkqLnXC1duWsTyb+a2M4EJdLzPF0AsLlB4okSZIEQohUyuZ9/vdPgKIoSuVjNyXVlM5TaObUjDEtxqht57LPEeQcVGfXsPLzw8rPDwDFZCJx6lSKzpwheMlirGpQSb+jkx3z2wfjrNOpX7R2Z+URZGeNp5UlUE2MzCYnrRDFVP9TnCSpvmm1WoqLi+UQXKlKRqMRa2vrGu8vA9BLCPVy4N+TKUSl5hPuHs624m3oDenk2BSx+ffT3PlGV3T66uePSJIkSde9Gs8TlSpnpbXizlZ3qtubYjfx1PqnmNJ5Cve3qYfKxYqC4/Bh6Hx90Zct46OYTCBElXfx+7s5qb8XGE08fCyaIpOJPT3CcdSZvxd0HBJAUHsPnL3MGV6j0cT6n0/Qum9Tmsg5otI1RqPRYDKZKCwsrHapEunGoygKRqMRk8lUq3W2ZQB6CSH/qYS7LWEb53wE7t45ZEdas39VNF1HBjdwLyVJkqSGpihKdEP34XpkpbWiqUPTelkzFEBotbhPmlShLXPeb+RtWI/PW2/VKCOqAR7w8yC31KgGn3mlRmw0Gly8LWuEnj+cxundyQghZAAqXZPs7e0xmUxXZT69dG0RQmBtbV2r4BNkAHpJ5ZdiuSmgrBCRjyBISabE3pd9q6Np0dWnwgeMJEmSdOMSQvQEPgaCMX+2yiG4V6BHkx4su30ZOo35a0qBoYBXt77Kw+0eJsw9rF6umb91KwX7DyBqsPQCgI1Ww1PNKs4ffScqgW1ZecxpG0SonQ0AwR08GflU+wrfGc7sScazmSMuXvJ7hHRt0Gg0tQ4yJKky8l/SJagBaGqe+kF31hu0UZH0HB2KqVRh8/xTVS5wK0mSJF0dhw8fZvjw4fTv35+ePXsybdo0ACZPnsy5c+cAc4n45s2b8+6779ZXN34EZgC9gS6Yh+Z2qa+L3QguBJ8AK86tYF3MOpZELqm36/l99y2BfyxA36QJAIaEBDJ++w2l3GL0VVEUhXyjiUKjiabWlrVHhRAEtHbHycM8JDc/q5h/fznB4s/2YzTKBeklSbrxyAzoJTjZ6PFytCYyJY+m9k1x0DtwzicX511n8InwxGdbIrEnMoncm0LzLpVXz5MkSZLqV3Z2NhMnTmTx4sWEhISgKApr1qzBZDIRHR1NUJC5iM33339Pq1at6rMrhYqi/FafF7iRjWk+BhdrF7r6dlXbjqQeobVHazSibu6lCyGwadFC3U754gtyli5DY2OLy+jba3T8N+HNyC01YltWEXd7Zh7fxabwRmgTNSNq66in7/gWCI1AW7ZfTlohVjY6bBxkoRdJkq5/MgCtRKiXA9uj0skoMBDuHs5uw27cM87zx9ZdjJ3QgQXv7WHrn2cIaOOOta18GSVJki4Yu2ws6YXpdXIud1t3/hz5Z6V/X758OSNHjiQkJAQwBwFDhw5l27ZtdO/eHYC8vDxWrlzJ2LFjiYuLq5N+XcIKIcRwRVHkMiz1QAjBoGaWpcNPZZxi4sqJDG42mE/7fVov1/R8+ml0rq44j7wFMGc4S5OT0fv4VHnchfmgAAuSMliTnsNDfp5qAKrRagjv3aTCMRvmniTlfA5jp3aR03skSbruySG4lSg/DzTMrWwYro+g5PffsfK2pf1N/hTklLBr6dmG7KYkSdINLTY2Fv+yKqblLV26lFtvvRWATz75hGeeeaa+u/IIsFwIkS2ESBFCpAohUur7ojcqW50tXX26MrjZ4Hq7hpWfH95TpyLKlp/I27iRyMFDyJy/oMbn+LyVP/PbB9PHzRGAIqOJaeeTyDRYhvWaTAq+Ic54BzmplXNNRhOlBlnwRZKk65NM3VWiQgDqZQ5ATwfacfvmLcw7HsnkESFE7k3m6MY4WnX3wauZU1WnkyRJumFUlbGsa/7+/hw9evSi9j179vDhhx+SnJzMgQMHePvtt5kzZ059dkUux3IVBTgFMHPwTHXbaDLy5PonGdJsCKNCR9XLUhFKiQGtszM24TUvgqQRosLSLXMT0/n4XBKpJaV80MK8JqlGI+g6MrjCQu4ndySx+59zDJoUhl8rt7p9IpIkSQ1MZkArEeppCUDD3cMBSOzoi7XBQOz8PyjVa+gzvgWKAhvnnZJ3KiVJkhrAiBEjWLZsGVFRUWrbrFmzCA0NRQjBkSNHSE1NZdiwYXz22Wf88ssvLFu2rM77UbYcSzzgUPaIl0u01C9Rbs3O4+nH2Zm4k/Ux6+ttnUKnoUMI/Xcdtu3aAWDKzydm8gPk79hR43OM93Hj2UBvnm5mKY68Jzsfg0mp0O+8zCKK8w04utvU3ROQJElqJGQGtBIXMqBRqXk0cwrDQe/AcX0G+bY6hmxYxbzYB3mwvQ/BHT05eyCVdT+dYOiDrREauUCvJEnS1eLs7MzcuXN54oknKCoqoqSkhDFjxjBy5EgABg0axKBB5rmDc+bMIS4uTv1bXRJCRAB/AcWYl2DRCSHGKIqyv84vJl2krWdbFt+6GCutpfrs9oTthLmF4WrjWmfX0Vhbq7/nrt9A/vbtWDcPxb5Hjxod76jT8mKQr7odXVjM6AORRDjbsbhjc7W968hg2g30x8bePPw3P7uY5dMP0+WWIILaedTRs5EkSWoYMgNaCU9HaxxtdESm5KERGkY3H022IYcN41vglZnB3qXLKVEUbrovDM8AR6L2p7B9UWRDd1uSJOmG0759e1atWsXGjRvZvn07/v7+atBZ3qRJk3jttdfqqxtfApMVRWmhKEpz4AHg6/q6mHSxQOdAmjiYi/ukFqQyZcMUxv0zDoPRUC/Xcx55CwG//IzH44+rbTkrVmDMyqrxOfRCcKuXC7d7WYLk3FIjiqKowSdA9NF0UmNyyUoqqJO+S5IkNSQZgFZCCEGIpwOJ2UXkFZdyf5v7sdHasNQ/kSI9DFiznD+SMrCy0THiiXY4utlwcF0shzfENnTXJUmSbmjjxo3D1tb2al/WXlGUfy9sKIqyHrC/2p2QzBytHJnUZhKTWk9CrzUHcgZT3Qei9l27onV2BqDo1Gnin3+B6MmTa7xOeBMbK6aHN+PepuaspqIoTDx8ljEHo8grtUztCe/VhPGvdaFNv6Zq2+b5p4nclyLXJJck6ZojA9AqqMNwU/LwsPVgXMtxZJXmsmZEE9pHnuTvzTspNSnYO1tzy1PtsbbTseWPM5w9mNrAPZckSZKusgIhRP8LG0KIfoBMVzUQG50Nj7V/jAlhE9S2Fza9wIubXiS3JLdermnl1xSPRx/F45FH1fmcpZmZtQoQc0qNmMp2t9dW/Irm4eeI3tq8xEtGYj5HNsZxYG1M3XRekiTpKpIBaBXKV8IF1CzoP+H5FOmh5+p/WJySCYCbrz03P9YWjVaw5sdjJJ3NbrB+S5IkSVfd/4CfhRCnhRCngZ+Bpxq4T1KZ3JJczmWf41zOOex09bPOpsbeHs+nn8Jp6BAAFKORmMkPEHPPvZgKC2t0Dme9jqWdQpnTNkgNYmfHpfL48Wjii0rU/dx87Rn3Shf6jm+h7he1P4XTe5IwmWRGVJKkxk0GoFVQK+GmmgNQNQuq5LO6ryM37dnO7COnMZXd3WzS3JVB94VjNJhYPuMwWSny5rckSdKNQFGUPUAoMLrs0VxRlH0N2yvpAkcrRxbeupCvB36NVmPOIm6J28K66HX1NoTVmJOD1sEBjZMTmloMCRdC4KTTqttLU7L4JyWL//bSM8AR7yDzEi+KSWH7okjW/XSCvIyiuui+JElSvZEBaBXKD8G9QM2CdlEwCQPha1exPNWS7WzexZset4dQlGfgn68PkZ9VfNX7LUmSJF0dQgjrsp92gB44W/bQl7VJjYReo8fH3geAUlMp7+16j+c2PUd0Tv2slqNzdSXgl59p+snHalv6nDmkfPopxry8Ko6saGGHUBZ3DMXPxlzhN7aohDfOxJNaYpnTKjSC4Y+2o+/45jh5mIPd/Kxijm2Jx2gw1dEzkiRJqhsyAK2Cv5sdVjqNmgEFcxZ0bMuxZIsiVnXRcdvmtXwZGUdpuSEvHYcE0La/H9mphSyetp+8THk3UpIk6Tp1YRHIPCC33OPC9g1p4b449p7PaOhuVEqn0TGt/zReiHiBQOdAwDxMN7Mos06vI4RAY2+uRaUYjWT+9juZv/2OUlTz7wU6jaCzs6We1czYFGbGpbI0JavCfh5+DrTp56duH1gTw8Z5pzi+LeHKnoQkSVIdkwFoFbQaQbCHPdHpBZSUWu4gTm4zGWutNct7WeGYn4HH9i18dj5J/bsQgj7jmtOmb1OyUwpZ/Nl+cuWQGEmSpHpx+PBhhg8fTv/+/enZsyfTpk0DYPLkyZw7d445c+YQERFBjx49eP755+v02oqidCr7qVEURVvuoVEURVvd8dej7AIDb/59lMlz9pBfXNrQ3alUuHs4E8MnqtszDs5gxOIRHEg5UC/XE1otQX8txG/GDHQe5qq3xefOkbNqda2GAb8c7Ms7oU2Z2MQdMFfO/S0hnZxyVXMB2g5oStsBfrTqYVl39Pi2BIry62dZGkmSpJrSNXQHGrsQLwdOJuUSnZ5Pc29HwDIX9Nfjv7Kmk+DODat4rFN3ers60MvVvI/QCPre1QKhERzZGMfiz/YzakpHdWiMJEnS9ermL7eQllc30w88HKxZ8b8+lf49OzubiRMnsnjxYkJCQlAUhTVr1mAymYiOjiYoKIgBAwZw9OhRHBwc6N+/PydOnCAsLKxO+neBEMIJyFMUxSSEaAO0ARYpilJSzaHXHSdbHdMndCItrwR7a/PXjMiUPApLjLT1c27g3lXO3dYdF2sXWri2qLdraB0dse/eTd1OnfY5uWvX4vfN1zheYu3aS7HXannI31Pd3pSZy7OnYlmVls0v7YLVdmdPO/qOtzyXxKhsNvx6kjN7krntmY518GwkSZIuj8yAVkMtRJRScb7GhSzo0t5WNIs5Tc/D+3jieAzpJZa7vUII+oxvTruBfuSmF7F42n6yU2tWCU+SJEmq3vLlyxk5ciQhISGA+X136NCh7Nixg+7duwPQqlUrcnNzKSkpoaSkBBcXl/roygbAVgjhA6wG7gdm1seFGjshBP1benFHZ8tw0A9XnmTkN1sb9bDcB9s+yN+j/sZebx7ueiT1CI+ufZSorKh6u6bHY4/icud4HAYMAMzZzJKY2i2t0tbBjqcCvHg8wEttO5xbQL6xYkbU1duOLiMC6TSkmdoWezKDjMT8K3gGkiRJtSczoNX471IsF1TMgmqYuvg3Roe1Y8rJGH4uVz5dCEHvsc3RaAQH18WyZNp+bpvSERcvWZtCkqTrU1UZy7oWGxuLv7//Re1Lly5l9OjRAEycOJGOHTtiY2PDuHHj8PX1vWj/OiAURckXQtwFzFIU5S0hxJH6uNC16I7OfthZaekU4AqAyaSQWVCCu4N1A/esIr1Gr/7+d9TfbEvYxl25dxHiElIv17MJD8f3rbfU7dxVq4h//gV8XnsV17vuqtE53K10vBrSRN0uMJqYePgseiHY3j0Ma40512DjoKfrSEuG1Gg0seGXk+TnFHP/h72xcdBfdG5JkqT6IDOg1VAD0NSLK9apWdB+NijZiTyzZTVr0nP4MT6twn5CCHqOCaXT0ADyMotZMu2AvOMoSZJUB/z9/Ym5RMZoz549dO3aldzcXN566y1OnTpFVFQUx48fZ/fu3fXRFZuyiriDgX/L2oxV7H9DGdbGh6/u6ohGY745+/ehePp9spEVRxIbuGeVe7Xbq3w/+Hv6+vUFwGAyMP/kfIqN9VfdXuj16Ly9sO1oGSKrmGpXxdZgMnGrlwsjvVzU4DPTUEqhseJ5BNB1ZBCdhjZTg8+8zCLOHU5DkWuJSpJUj2QAWo0gD3uEuDgDCuYs6L3h95KjLeHXYdYM/2cRwVnp/F9kAkdzK64BKoSg+6gQIm4OJD+rmEWf7iPpbPZF55QkSZJqbsSIESxbtoyoKMswyVmzZhEaGmquQKrRYGVlhYODA1qtFldXVzIz67bSaZkFQBIQBGwrG4orq89VoqDEiFYjaOHt0NBdqZQQgp5NeqojmhaeXsh7u95j2t5p9XZNx0GDCF2zBptWrQAwZmdzdvjNZM6fX+NzOOt1vNvcj7dCm6pt70Ul0n3ncY7nWaYBabQaWvXwpVu5rOjBtbGsmHGYkzsb740BSZKufTIArYaNXou/qx1RqXmYLnFH8JH2j9DMqRkbwowc8S7mi1ULKVEUHj0efdH8CyEE3W4NptcdoRTnl/L35wc4fyTtonNKkiRJNePs7MzcuXN54okn1Cq4OTk5jBw5EgB7e3see+wxevToQa9evdBqtQyqYbGX2lAU5W0gGOiuKIoJ8zIsY+r8QteJCd2asXPqTYR6mQv3ZeSXcOfMHWyParyfiYObDWZci3FMaj1JbUvKT6r8gMskdJbZUYWHD2NISsKQdPnXURQFO60GW62GEDtrta3IeHFmNTTCixbdvAnt7K22Hd+WQFGerJwrSVLdEbUp/d2YRUREKHv37q2Xc0+es4f1J1PY8uIA/N0unru5J2kPk1dPxidPxyffFrH29Q/5zKsZd/q48UVYwCXPeWpXEut/PoECDLy3Fa2618ucJEmSpHpnMJi/nOr1jWMO2R9//MHIkSOxta1Z1fFL9V8IsU9RlIjaXFcIEQKEUK6+gqIoK2pzjoZWn5+lVfltVwyvLD7ClEEt+N+g5lf9+pcjKT+JWxbfwojgEbzd8+16u44hKQmNgwNaB3O2OOXzL7Bt1xaHgQPV7GxNlJhMWJUNyd2UkcvTJ6L5oIUfN3u6VHpMYlQ2iz7ZR0C4GyOf7nAlT0OSpBtQZZ+lMgNaA1XNAwXo4tOFMc3HkORQyp+9NYz6eSYtrbTMT8rg25iUSx7TspsPNz/RDq1O8O+cExxYU7uqd5IkSdKljRs3rsbBJ1CrNRgrI4T4ANgJvAa8UPao20VHr2N3dwtg/sPdeahvkNo2b1c0KbmNdxRzdnE2zZyaVVi2pT5u6ut9fNTgsyQujvSZM0n5bBoYazfF+ELwCXCmoIhMgxGXctnWS/XdxduWLiMC6TDIcjP93KFUEs5k1ctzlSTpxiAD0Bq4sBRL1CXmgV4wpfMUPGw9+Ke7ljP5Z5l5bBduei1vRyWwMOnSZeebtXbntikdsbHXs31RJNsWnpET/yVJuuZotVpKS0ur37GRMhqNaLXaKz3NWCBEUZS+iqIMKHsMrIPu3TC6B7tjZ2UOiPbHZPLq4qM88uu+Bu5V5Vq6teSPW/5gfMvxgDmAm7JxCt8d+g6DqX6GrFr5+RH4xwJ8335LHapbsHcvhUdqV3D5QT9P9vUMp6er+ftNodHEwD2nmBlb8aa5rYMVXUcG4x/uBoDJaGLz/NP8/fkBCnPlsFxJki6PDEBrIKQsAxpVSQYUwNnamaldp2ISCt+P0GOc9R1zmzhjq9HwzMkYNqTnXPI4nyBnRr/QCQc3aw6ui2XN7GOUGmThREmSrh0ajQaTyURhYSElJSUYDIZr4lFSUkJhYSEmkwmN5oo/DhMVRbn0G71Ua+G+Tkwd3opnB1uyi9Hp+RguMW+xIWk1WnQacyCYmJ/IzsSdbIvfhk7U3yp3tm3bYtelCwCK0Ujim29xftx4SmJja3UeTyvLkPODuQWcLyzmXGFJlccIIeh3d0u6jwrBzskKgOzUQvatOk9RvgxIJUmqGbkOaA1cGIJ7JrnyABTMBQr6+/dnIxv5J6yAibO+44fnX+beI2d54Nh5FnYIoZOT/UXHufrYM+aFCP6ZfojIvSnkZRRx82PtsHW0qpfnI0mSVNfs7e0xmUwYazkssCEJIbC2tq6L4BNghxDid+BPylW/vdbmgDYWNnotj/SzrL1ZUmri3tm70Ws1/PNUb2z0V5yxrnNNHJqwYvQKcopz1LmZ66LXIRAMDKjdfM0a02jwmvIMhYcOY1W2Hq4xJ4fStDSsg4OrOdiih4sDu7qHU76Lb0bGY6vR8FSAF/Y68+stNILAth4EtrXsd2RjHIf+jcXaVkebfn518rQkSbq+yQxoDTjb6gnysOdAbBYJWYWV7ieE4NVur2Kvs2dBPx2nNyym+6mjfN4qQF0YOrLg0vNZHFytGf18J5q1dSfpbA4LP9pLZpJcK1SSpGuHRqNBr9dfU486Cj4BugA+wFPIOaB1rqCklPZ+LrT3c1GDz0tVpm9objZuBDoHAuZ1Qz/Y/QHPb3q+Xqrlgvl7h+OgQXg996zaljFnDmdvGUnOqtW1OpeXtV7NiuYbjfyZlMEfSRnoNFUHzh2HBNB1ZBAty4opKorClgWniT6WXstnI0nSjUIGoDV0b49mGE0Kc7afr3I/H3sfnun8DCU6hVnDtMS//DKjrQWvhzQhw2DkzkNRJBVfepiKlY2Omx9tS9sBfuSkFfHXx/uIO3np+aOSJElS41Fu3ucAOQe07rnYWfHVXR35+I52attHq07y4M97ScpunIWK9Bo9Xw38ipe6voSvgzk4SytM42TGyXq9rlVICNatWmLfo7vaVlrLtW/ttVp2dAvjxzZBWJernPvSqVjiiioO07V3tqbLiCD01uYbA6kxuRzeEMfBtbK4oiRJlyYD0BoaF+GPk42O33fFkFtU9TyHcS3H0dGrI4eDBCubppH0xhs85ufBI/6exBUZuOtQFJmGSxfs0Gg19B3fgj7jm1NSWMqyrw5xfFtCfTwlSZIkqY4IsweEEB+WbQcKIXo2dL+uN9qybJzJpLD7fAb7YzKxt258w3EvaO3emjtb3aluf3vwW8YuG8vG2I31dk3nESMI+usvtM7OABSdOkVk336kffd97c6j19HRybL03M/xafyckH5RAPpfngGOjJrSkW63WYYAH94Qx+b5p8nPLq5VHyRJuj7JALSG7K113N2tGbnFpSzYU/VEf43Q8G6vd7HT2fHLYB3HD6wjZ9Ei3gxpwhhvV07kF3HHwUjSSiqvGtlugD83P94OjV7Dhl9Psn1RZKMcbiRJkiQBMA24CRhVtp0LfNFQnbneaTSCvx7tycJHe+BoYx42ejgui/eWHyczv+oAqSH1bNKTbr7d6O5ryU5mFNX9SKfy801LU1LRurtjFeCvtimXUbV6RngzZrUOpLuLuS5GgdHE0yeiOZRbcNG1m7Z0xSfIWW07sT2BY1viK+wnl3GRpBuXDEBrYVLPQHQawU/bzlNaTSW+AKcAXun2CiVaha9G6Yj56H1Ko6P5slUAo7xcOJZXxJiDkaSWVJ5NDWzrwejnO+Hgas2BNTGs+PYwJYXX7lIHkiRJ17EBwASgEEBRlHTApkF7dJ3TaATBZcukAXy/6Syztpxjz/nGO3XlpmY38cOQH7DRmf9p7E7czeA/BzP/5Px6u6ZDn96ErFmN47BhgLly7rmx40h88y0UU82rCttoNYz0clG3/07J5I+kTP6sZKm58ka/0JmRT3fA3tkagKyUAua+sZNTu+pnbqwkSY2bDEBrwcfZhlvbNyE+q5CVR6t/07w15FaGBQ4j2hPmdSsm/oUX0RpLmR7ejLE+rpzKL2L0gchK54QCePo7csfLEfgEOxF9JJ2FH+0lK6Wg0v0lSZKkBlGklEvpCCE0QD2UPZUq89Ed7Xh3VBsGh3sDUGo08duuGIoa8dJm2SXZOFg5qIWLAExK3S81o7GyQpTN5TTExWFMT8eYkaG2XU42cryPGz+2CeRRfy+17YvzSfydkonxP+fTW2nxa+mqbiedzSYvvYjSEst/G3mDXZJuHDIAraUH+gQB8MOWs9W+YQsheL3H6/ja+7Kii4ad+UdJnTEDrRB82SqACb5unCko5vYDZ4ivYk6FvbM1o6Z0olUPHzKTClj44V5iTzTeO7ySJEk3oCNCiAmYp4MGAt8CWxq2SzcWB2sdE7s3U4efLtwXxyuLj/De8hMN3LPKDW42mFVjVqlDcouNxdyx7A5mH51db9e0ataMkLVr8HnzDbUt7ZvpJLz0EoaUlBqfRyMEIzxd8LMxLxmXVlLKZ+eT+b/IBKqbMdSquy/3vt+TFt18AHMAvOTzAyz8aG+FoFSSpOuTDEBrqXUTZ3qGuHMoLps956uvKudk5cSHfT5Eg4bpI3VE/TqTgn370AjBJy39mdTUg3OFJYw6EEl0YeWT87V6DQPvDaP32LLiRF8f4tD6WDmHQpIkqXF4FugP+AK7MH++vtiQHbrR9W/pxYRuATzQO0htOxqfjbGR1VOw1dmqv59IP0FcbhyxuVXXmrhSGmtrdB4egDn4y92wntx/16Oxtr7sc3pY6VjRuTmftPRHX1YsalNGLtPOJ5FxicKL9i7W6K3MBaRKiozorbXorbXoytqK8g1kp8oRX5J0PRLXSwATERGh7N2796pca8PJFO6fs4fB4d7MujeiRsd8c+Abvj/8PR0jTby+zZvgJUvQOjmhKApvRiYwMy6VptZ6/ugQQohd1dOGYo6ls/qHY5QUlhLW05e+d7VA1wgX5ZYkSbpWCSH2KYpSszf4+u3HJOBhQAGeUhRlf7m/2QA/AgFADPCAoihFQog5QHsgG0hVFGVsTa93NT9Lr7aU3CL6fryBdn4uLHi4e4VCPY1JRlEGiqLgbusOwNcHvsZB78DdYXdjrb38ALEqisFA8Zkz2ISHA1B04gSpX3yJx9NPYdu69WWf986DUWzMzOWfTs2JcLavdv/SEqMagO5fHc2OJVEMmhROy7JMqSRJ15bKPktlBvQy9GvhSaiXA+tOJHMuLb9Gxzza/lHae7bnQKiGZb5JxD//PIrRiBCCt0Ob8GSAF/HFBkbuP8OBnKrv+AW0dmfsyxG4eNtxYnsiiz87QF6mLG0uSZLUkIQQA4UQDwshHr/wuMLzuQJPY86sTgS++s8uk4CTiqL0AU6VbV/wlKIo/WsTfF7vDEaFga28uKmVlxp8ZhcaGt1IIjcbNzX4LDAU8PuJ3/n1+K/12k+h16vBJ0D2sn/I27QJQ8yVreU5PbwZn7b0V4PPfKORp09Esz/n0t+dLgSfAI7uNnj6O+LXyjJ39OTORPl9R5KuA/UegAohWgghDEKI3v9pv10IcUIIUfSf9jVCiI1lj0IhRNv67mNtaTSCB3sHoSgwe+u5Gh2j0+j4sM+H2OvsmXuTjmMnt5D61deAea7oq8G+vBbsS4bByJiDkWzMyKnyfC7edtzxcgSB7TxIOZ/DHx/sISEy60qfmiRJknQZhBC/Ap8DvYEuZY8rzaB2BbYoilKiKMo5wFEIUT4F1g/4p+z3ZWXbF0wTQmwRQoyvQd8fFkLsFULsTU1NvcIuN15NXWyZMaEzD/e1rE/51O8HuG36NtLzGmdQY6e34+9Rf/Nx34/Vyrn7kvcx++hsCgz1NzzV64Xn8f/hBxyHDAHMlXPjnplCzuo1tTqPu5WOiU3c1e2/U7L4IymTpSlZ1R7bPMKbca90USvnZqcW8O+cE6z49nCt+iBJUuNzNTKgrwObLtG+GegIxJVvVBRliKIo/YG7gChFUY7Uew8vw6iOTfFwsOLPfbE1XnPMz9GP13u8jkGr8NlYK6J//p6cVasAcxD6ZDNvvmwVQLHJxMTDZ1mUXPUcU2tbHTc/2pYutwRRmFPC39MOcGRjXKO7mytJknQD6AJ0UhTlXkVR7i97TL7Cc7oD5T8IsgC3Sv5e/m/PK4rSFbgNeFkIEUwVFEWZqShKhKIoEZ6enlfY5cbvQvbzQnVcRQE3e3MhHZNJaXSfoZ52nkT4WO5lzDw8k8/3fc7htPoLxIQQOPTuhdCaM5IF+/aRu2oVOStXXtF5x/u48XPbIB7ys/w7+7/IBL6LSaG4miVhbOz19BwdSschAWpb1P4U9q48T2Fe4137VZKki9VrACqE6AYk8Z8gE8xrpCmKUnTxUaq7gPpbGOsK2ei13NM9kCKDiXm7omt83IjgEYxvOZ5kJxPTR+qImzqVolOn1L+P93VjTpsg9ELw+PFovo+tuiKd0Ai63hLEzY+1RWulYfP806z/9SSljbjsvCRJ0nUoCrCr43NmAC7ltp3L2i71d/VviqKklf3MANZing8q/YeNXssvk7vye7n5oAv3xXHb9G0cictu4N5V7r3e7/FSl5fo5tMNMFfOnX10NtnF9ddn+65dCVr0F55PP622pf/4I2nfz8SUX7OpSABaIRjq4UzTssq5OaVGfopP5cf4NLTVrFpkbaen45AAmkd4q20H18Ww6++zFOVZlrNrbDcQJEm6WH1nQF8FPrzMYycA86raoaGHDU3sHoC1TsOc7efJKap8Lc//erHLi7TzaMe+UFjcoZi4J57EmJWl/n2whzN/dgjFVaflzcgE3olKqPYNNai9pzov9OT2RBZ/up/cjKrie0mSJKkOPQ9sEkJ8JYT4+MLjCs+5C+gthNALIQKAPEVRyo8V3QTcXPb7zWXbCCFcyn5aAb2A01fYj+uag7VO/f1AbCZH47NxsNFVcUTD8rD1YGL4RDVoXnxmMZ/v+5xvD31br9e1CQ/HOthcUVgxGEj/aQ7ps2ZdUcDnpNOyvXsY34QFoCurnLsyNYu7D0VxJLf6IcbDH23HoPvDcfUxzzEtzC3ht7d2cXRz/GX3SZKk+ldvAagQYgSwV1GU9Ms4NgwoLJvzUqmGHjbk7mDNfT0DScsr4dPVp6o/oIyV1orP+n+Gq7Urf/TTsU8XT/yzz6GUWsqURzjb83en5jS11jM9JoUnT8RUOzzF1ceesS9HENTeg5ToXP54b49cL1SSJOnq+AqIxzwUNr/c47IpipIJzMAcWP4OPCOE6CCEeKFslzlAWyHEFqBt2TbAAiHENmArsEBRlGNX0o8byQej27Hh+f4EeZgDmpTcIm77Zitrjyc3cM8qNzRwKI+0e4R7wu9R21acXUFCXkK9XVPo9QT/vYSmn3+O1sEBgMIjR4l/9lmKTtXufoevtRXdXBzU7bXpOazPyMVQLrAtrWTpHDsnqwoVcpPOZpOTVkhRuSG5+dnFGI1Vf3+SJOnqqrdlWIQQrwI3ASWYPxhjgfGKokT/Z79IRVFC/9P2PhCrKEqNb+c1VOn4/OJShny+mYTsQv56rCedAlyrP6jMzsSdPLL2ERyKBR/OLKbF2Ml4v/hChX0Sikq4+/BZTuYX0cPFnp/aBOGir/rOrGJS2L8mml1/nwWg223BdBrSDKFpnCXnJUmSGpvaLsMihDihKEpYffbpariel2G5HH/sjeXFhYeZOrwVj/QLaeju1EhSfhLDFw3H286bFaNXoBFXZ8GDpHfeJXPePJp++SVOQ4dc9nkUReFATgGdLlTOLTXSe/dJxvu48XKwb7XHF+aWIDQCG3s9AMu+OkhqXB7jpnbBwbV+lrGRJOnSrvoyLIqivKcoykBFUYZhnoPyPBAmhLinrEN9hBDrgCZCiHVCiNFl7QK4A/ijvvpWl+ytdbw7qg2KAlP/OoKhFnfZuvt258kOT5JjZeTz8TYk/zybrMVLKuzTxMaKpZ2a08/VkR1Z+Yzcf4bowqqr9QmNoPOwQEY+3QFrez07l5xl5fdHKC68eCFoSZIkqU4cFkJU/+1YuqaMi/BnxdN9mNC9mdr20sLD/LozGmMlWbmG5mLtwgsRL/BY+8fU4PNo2lEOphys1+t6vzIVvxkzcBx0E2CunBsz+QEy5y+o1XmEEGrwCXAqv4hik4micqPA0kpKMVTy+ts6WqnBp2JSsHe1xsndBnsX87zTkqJSTmxPoKRIfieSpIZSbxnQq62h79o+8dt+lh9O5MVhLXm8f2j1B5QxKSb+t/5/bIzbyNCDGh5Ya8L/++9w6NWrwn4Gk8KLp2P5PTEDD72OX9oF0cmp+kWdczOKWPX9EVKic3H2tGX4o21xb+pQ7XGSJEk3ssvIgK7GvOzKNkCdgK8oyrh66F69aejP0sYuNqOA/p9upE0TJ5Y80Uudh9nYTV49mT1Je1hwywLC3cOrP6AOFB46xPm77sZp+HCafvYpYM5uXs5rVmg0UWIy4Vw2AuyRY+fZmZXHko7NCbKrPqupmBR1FNiJ7Yms/+UEHQYH0GtMzb+vSZJUe1c9A3qjeXNkOI42Or5cd4bo9JpP+9EIDe/1eQ8/Bz9WdzCxMVwh/un/UXTiRIX99BrBtJb+vBzkQ5qhlDEHIlmZmlXt+R3dbBj9fGda92lCdmohCz/cy8mdibV9epIkSVLVfgOeBf4Clpd7SNcRfzc7Nj7fn/dub6sGUov2x/HByhONdi1RRVG4L/w+7mx5J2Fu5lHiBYYClkQuocRYf8uX2LZvT+jaNXg+8z+1LXXaNOL+9wyG5NrNqbXVatTgU1EUnHRanHRaAmzNWU2DSWFtWjbGSpIq5acg+YY4036QP616WOaObl8Uyd6V5yktkSsISNLVIAPQOuLlaMPU4WEUl5p4dfHRWlWFc7Jy4osBX2Crs2XWCB0nXPKJffgRDAkVCwgIIXgm0IfpYQEYFZh89DwzY1OqvZZWr6H/hFYMvNf8wfPvnBNs+PWEfKOVJEmqI4qi/HypR0P3S6p7/m52tGnqrG7P2X6eWZvPklfcOId0CiHo59+PV7u/qgbNy6KW8fq215lxcEa9XlvftClW/v6AOXDM37GT/O3b1cJFiqJgKq5d4C6E4JOW/mzo0gpt2fNZk57NPUfO8WZk9dVvXbzt6H1Hc9ybmPtQWmLk6KZ4jm2OR6Mzfy02GU0Y5HckSao3MgCtQ3d28adLoCtbI9NYfKB2JcBburXk/d7vY8DIZxMdSCxOJebhhzFmX7yu1xgfN+a3D8FZp+WNyAReOh1X6VyI8sJ6+nJH2VItx7cl8tcn+8hKqb7MuSRJknRpQoj/lf38+FKPhu6fVP/mPdiN7++JoJm7eVpMUnYRj/y6lwMxmQ3cs8p1b9KdsS3GckeLO9S2JZFLOJ99vt6uKYQg8I8FBC6Yj8be/FoVHTrEmb79yPzzz1qfT1cuq9nMxorR3q6M9rIUgvw5Po3lqVmVVtBVz2OlZcL/dWfwA63RlJ3z/JF0fnpxqxwxJkn1RAagdUijEXwwui16reDd5SfIyK/d0JZBzQbxVMenyNIU8ekDruRERxH3xJOYSi4+T09XB5Z3bk6QrRW/JKQz8fBZsg3V3311b+rA2JcjCO3sRVpsHn++v4eo/Sm16qckSZKkujDfM7+Sh3Sdc7TRMzjcW93++2A8q48lsy+68QagzZya8UaPN/Bz9AMgOT+Zt7e/zaPrHsWk1N+SJUKjwTo4WN0uiY6G0lK0Li6Wtrg4FGPtso9tHO2YEd5MLV5UYDTxblQCz52MpbQGI9Lsna1pEmrpQ1G+AY1W4ORuo7adO5RKVrK8aS9JdUEWIaoH09ac4qv1kdzR2Y9Px7av1bGKovDylpdZcW4FPdJc+d+sVFxuHk6TTz9FaC6+X5BhKOWBo+fYkZVPcztr5rYLppltDSbkKwpHNsazbeEZTEaFdgP96Dk6FK1O3pOQJEmqbRGi60Vj+iy9VimKwsZTqUQEuuJoY67GOmXBQVo3ceL+XkFoG+GSaMXGYlacXYFOo2NkyEgAdiXuIjonmluCb8FOb1dv1zbm5aOxsUbodChGI5GDB6OxtiF46d8Ivf6yzqkoCntzCogrKuF2b3NWdFtmLh+dS+KFQB/6uDlW3y+DCY1WIDSCUoOROS9tA+D+T3qj1crvSpJUE5V9lla9oKR0WR4fEMo/hxNZuC+OweHeDG3tU/1BZYQQvN3zbWJzY9nBEZqO9mTcopVo3T3wfmXqRdXj3PQ6FrQP4YVTcSxIymD4vtPMaRNEV5eqK90KIWg3wA/vQCdWzzrK4fVxJEVlM+TBNjh72l7W85YkSbrRVDfMVlGUF69WX6TGQQjBgFZe6nZcZgHLDiUQnZ7Pg32Cqziy4Vhrrbm9+e0V2mYensnupN20dGtJe8/a3UyvDa2DpaK/KTcXuw4dEHorNfg0xMdTEh+PXZcuNa6gK4Sgi7M9Xcot57ItK4/d2fkVlnOJLizG38YKzSXOq9WXCzIV6H5bMKUGkxp8xp/O5MCaGDoNa1YheypJUvXkLZx6YKPX8tm49ug0ghf+PERcZu2GbNjobPhywJd42XmxsGUm2/q4k/nrr6R9/fUl97fSaPiilT+vBvuSYTByx8Eo/krKqNG1vIOcGPdqF4Lae5ASncsf7+0mcp8ckitJklRDF4ba+gDjAX3ZYxzgXcVx0g3Cz9WOrS8N5N1RbdW2X3ec58Gf9xCVmteAPavaWz3e4vmI59Xgs8BQwLMbn2Vn4s56u6bWxYWm06bh++EHalvG3HnE3HsfOf9cWVHpF4N82dy1FQPcnAAwKgq37Y+k3+6TlVbPvUBnpaVNPz86DApQ26KPphN9NJ3iAsv0p8ykfIy1WA9ekm5UMgCtJx0DXHlpWCtyikp56vcDGGr5huRp58nXA7/GRmvDt30LiGrjStqMb0n/8cdL7i+E4Klm3vzQOhCtgCdOxPDB2URMNRhibWOvZ/ijbek9tjmlBhOrZx1l0++nKDXICnCSJElVURTlbUVR3gb8gE6KokxRFGUK0Bnwb9jeSY2Fj7MN4U2c1O1Np9NYfzIF63LTXopLG9dnrr+TP/e1vk/d/jfmX9ZGr2Vb/Da1rb6mcZXPdNr37oXj0KE4DhygXjP5k0/I37mr1udtYW+jFi/KMhjp4mxPhLO9Wk33ZH4hP8SlklZSfU2NHreHcMdLEQSEuwFgMin8/fkB5r6+A1MNCkNK0o1MBqD16IHeQQxs5cWBmCw+WX2q1seHu4fzXu/3KDEZ+Oh2QXKgMymffErm/PmVHnOLlwtLOjXH11rPl9HJPHj0PPk1+FATQtD+Jn/GvNgZJw8bjm6KZ+FH++SEe0mSpJrxURQl/cJG2e81n38h3VBm3duZZU/1xs/VPLcyIauQLu+uY/qGyAbuWeVuCb6F2UNnc3eru9W217e9zitbXiGnJKferuvQqxd+X35hqZx79BgZP84mfeZMdZ/LCYTdrXTMahPItJaW+0TzEtJ57Uw8mzIsz6eycwsh8A5yUmtnGIpK8Q93I6C1u1pNNzU2l80LTpMe33gz3ZLUEGQAWo80GsFnY9vj62zDzM1nWX+ydgsvAwwJHMKznZ8lqzSHjyY5kOvlQNLb/0f20qWVHtPe0Y6VnVvQwdGOFWnZ3HYgkviimlXk9WrmxLhXuxLSyZP0uDz+eH8Pp2QZckmSpOocE0L8IIToUfaYCRxv6E5JjZMQgtZNLGuJnknJQ6sR2Oi1altcZkGjyooKIeji0wVfB18ADEYDe5P3si95Hw56c90Jg8lAdvHFy8fVJZvwMPxnzcTj8cfUtvRZPxDzwIMUnz1b6/OVz7Y+6OfJi0E+DPM0/7dRFIUhe0/z9InoaoNcazs9N90XzoAJrdS2M7uTObIhjrQ4SwCam1GEIjOk0g1OBqD1zNXeiq/v6ohWI3j2j0MkZBXW+hyTWk/izpZ3EleczLQnm1DiYE3C1FfIWbu20mN8rPUs7hjKbV4uHM0rZNi+0+zLrtmKANa2OoY+1IZ+d7XAZFRYN+cEa386RklR41xkW5IkqRF4AMgCvil7ZAOTG7JD0rWjXwtPdky9ibu7WuYYTllwkJ4frCc9r7gBe1Y5vVbPP7f/w6whs9AI89fJjbEbGfjHQBaeXlhv1xVaLQ59+mAXYSmsWXjwIPm7dqF1sgxzLjp9utaZ0Wa21jwb6IO91nwjIKnEQFKJgfQSoxqoxheVsC0zt9p5owBdRgYx9KE2BLX3AMwB7d9fHODX13ZgLJVzRaUblwxAr4KIQDeeG9KCrAIDT1/GfFAhBC93fZkB/gM4VniW719qjUkrSHj2OfK2bK30OFuthu/Cm/FCoA+pJaWMPhhZ4+JEQgja9PNj7NQIXH3tOb0rmQXv7SEluv6G2UiSJF2rFEXJURTleUVROpc9XlAURb5hSjVmo9dia2UOfAxGE6FeDrTydcTdwby0WnahgeWHExtVVlSn0RHgZAmai0qLcLFxoYVrC7Vtc9xmkvKT6rUfftO/IeSfZeg8zIFeSXQ05269jYTnnr+i8/paW3GgR2u+CLMM0/0tMZ0xB6NYkFj99ym9lZbQzl5Y2ZgXnTAUG/H0d8SrmaM6dDczKZ/1v54g6Vz9Zo4lqTGRAehV8mjfEPq28GRvdCbT1p6u9fFajZaP+n5EO492bMk/xII3emEymYh78knytm6r9DghBM8F+TCzdSAazMWJ3otKqNGdOwD3pg6MnRpB6z5NyEkt5K+P93FgbYwcPiJJkgQIISbWxT6SVJ5eq+GD0e2Y+0A3tW3x/jie+G0/322s/TDTq2VkyEhWj1lNWw9zxd8CQwEvbn6R8f+Mp9RUf6OohBBYBQaq26aiYhz69cOum+X1y/33X7IWLsSYV7PRYBfoNAJPK8t6pN2dHRjl5cJgD0u2dcKhs7x6Oq7awo9WNuYRZkMfbqO2nT2YyoltiaTF5KptmUn5lBTKUWfS9UvUVwWzq+1aWDw7La+Ym7/cQkpuMbPujWBweO0r9GcUZXDPinuIyY3hMYebGfDGcoRWi9+MGTj07lXlsYdyC7j/yDkSig0MdndiRngzHHXaKo8pL2p/ChvmnqS4oJSAcDcG3heGvbN1rZ+DJElSY1fZ4tmX2O84MAaoaoHC+YqitKuzztWja+Gz9EZ1PCGHX3dG81i/EALczcWLPlh5An9XO+7s4o9O2/hyCgWGAv48/SeAWlF3b9Jelp9bzsSwiYS4hFy1vpwbO46iI0cIWbcWKz8/ABSDQV1v9HKllZTSdedx2jrY8nen5gCkFBs4nl9ILxdH9Jqq1y41GkzEnc7EK8ARW0crAP78YA9p8Xnc/1FvbOyvrH+S1JAq+yxtfO9W1zEPB2u+ubsTOo3gmfkHOJlU+9FZbjZufDvoW1ytXfk2bwVH370bxWgk7vHHq8yEgrk40arOLejsZMfa9BxG7DvD+cKazy0J6eTF+Ne64hvqTMzxDBa8u5vzh9Nq/RwkSZKuI3bACmB5FY+iBuuddN0Ib+LEB6PbqsFnZn4JP245x+yt59CWBTkGo4nSRrQOpZ3ejvta31dhOZd/zv7DwtMLSchLUNvqs4ruBb7vvov366+pwachJYXTvfuQ+s30Kzqvh5WOo73a8GWYZSjy4pRM7jx0ltnxqWpbZQkfrV5Ds9buavCpmBSatHAluIOnGnzmZhQx/53dHN+WcMlzSNK1ptIMqBDi5hocX6Qoyvq67dLluZbu2s7fHcPLi47g52rL30/0Uud31Mbh1MM8sPoBSk2lfOB8H81e+cGcCZ0+HYc+vas8tthk4oVTsfyRlImLTsus1oH0cXOs8bVNRhN7V0azd/k5FAXa9mtKzzGh6Kxqnk2VJElqzGqaAb3eXEufpRKcS8snOaeI7sHuACw5EM+7y0/w3u1tGNq6ca4CVGIsYUv8Fvr59UOn0VFqKmXQn4No7tqcmYNnVqhKW5/yd+4ifsoU3O67D49HHwGgYO9eTMXF2HfrhtDpLvvcu7PymJeYwbOB3jSzNX/Hu+tQFFYawffhgdjUMlsduS+FNT8cpdttwXQeFghA9LF0igsMBLb1UOeYSlJjczkZ0AXA88ALVTxm131Xr393dg3g/l6BxGUW8ti8/ZRcRiW0dp7t+HzA5wC8ljuX1A+fNGdCn3iiysJEANYaDV+2CuCtkCbklBq583AUP8al1rhanEaroestQdz+vHnN0COb4vnjg72kxeVWf7AkSZIkSXUiyMNeDT4BUnOLyS8upamLrdq2+lgSsRmNZ01vK60VNwXchE5jDprSCtPwc/TDw9ZDDT5PZ57mz9N/1uuSLvbdu9F88ybc7r1HbUub8S2xDzxIcVSU2qaYav8drauLA1+GBajBZ7HJRGRBMTGFJWrwmVZSyvSYFM4VVD8SLbSzF/d/0pvWfZqqbQfXxrD2x+MUZFuW2ctIzJc1OqRrQlUZ0AOKonSs8uAa7HO1XGt3bUuNJu6fs4ctZ9K4s4s/H4xue1l3/VaeW8lLm1/CXm/P104PY//SZ2WZ0G9w6NOn2uPXp+fw6PHz5JSauNvXjQ9a+GGtqfmduZLCUjbPP82pXUlodIIeo0JoP9AfUc2cB0mSpMZMZkCla1VBSSm2ei1CCPKLS+n87lpcbK3Y/vJANI34s9lgMqDXmIecfrj7Q+admMcnfT9hWNCwi/5eX/I2bSJ/1268XngeIQTGnByiRozAZcwYvJ555orOrSgKaYZStaDRb4npPHsylucCvXkhyLy2amJxCa46XY0ypImRWSREZqkZ0aJ8A7Of34JvqAu3P9dJvebVyihL0qVcTga0aw3OW5N9pEvQaTV8c3cngj3smb8nljnbz1/WeYYHDefVbq+SZ8jjufw5mD6eWjYn9Aly//232uMHujuxonMLQu2s+S0xgzEHIkkuNtT4+la2OgbdH87gB8LR6bVsWxjJsq8Pkp/VONctkyRJkqTrmZ2VTg06FOCFoa14rH+IGnz+eyKZCT/sZM/5mi3LdrWUDy7HtRzHo+0fpa9fX8AcSN3+9+08tf6pWq/tWRsO/frh/eIL6utXHBWFUmKAUktF2oL9+8n+ZznGvLxanVuIitV0B7g58m7zptzm5aq2vXgqjrCtR4krKrnUKSrwDXVRg08wJwSCO3riH2Y534ntifz+f7uIPd64/ltLUlUB6HdCiJ5VHawoSs0jFekizrZ6frgvAicbHe/8c5zNp1OrP+gSxrcaz1MdnyKjKINnC3/B+vO3AYh7+n9k/7O82uND7WxY0bkFg92d2JtTwNC9p9mfXbsy5S26+DD+tS74hjoTeyKT+e/s5uyBy3s+kiRJ1xohhF9D90GS/svBWscDvYO4r2eg2rbzbDrbItMrrEm+LTKNlJzGUysr2DmYJzo8gZ3eXHApvSgdgaiQ0TubfZZ5J+aRnJ9cb/2w69iRFlu34P7II2pbxi+/kvD88xSfOaO2lWbUPsDztbbiQT9PWtjbqG2BtlaEO9jQ1NocqOaVGhmw+yTTY1KqPZ+Thy3DHm5LxM1BaltuehGZSQVY2VrmiG5beIZdS89ivIzpX5JUV6oKQGOBuUKIk0KIl4QQjXM2+zUu2NOB6RM6IYTgid/2E5lyefMoH2r7EPeE30NCfgLPFvyK49cfI/R6El54gayFC6s93kmn5ee2QTzTzJukEgOjDkQyPzG9Vn1wcrdl1LOd6HZbMCWFpaz8/ggbfj1BSZFcy0qSpOvebiHEX0KIgQ3dEUmqyqsjwlkzpS9dA90AKCk18djcfQz9YjPGsvmDjW2JPg9bD5aOWsrHfT9W21aeW8mHuz9kW4JlBYCMoow677vQ69E6Wgo1ut8/CffHHsW2nXllJVNxMZGDBhMzefIVX+ud5n4s79xCDbKP5hVypqCIpGJLRnRLRi7TY1JILK4+S9rt1mAe+KwPns3M/TcaTRzdHM/JnYlotOZrFOUb2LP8HKkxso6HdPVUGoAqivKWoijBwONAa+CUEGKZEOJ2IYQsd1qH+jT35I1bwsktKuW+2XtIvoy7kEIIno94nttCbiMqO4rn8ubg9t0XaOzsSHztdTJ++bXac2iE4OVgX2a1DkQrBM+cjOW1M3EYajGhXaMRRAwPZPSLnXH2suX4tkT+eG8Pyefqv8S6JElSAwoElgDvCSGOCyGeEELUvLy4JF1FLbwd1XVDDUYTD/cN5v5eQepyLtsi0xn6+WZWHU1syG5WIIRQM6IAtwbfypTOU+jn109te2jNQ9y86GZKjNUHZ5fLtn17vP73P4TW/FXYmJaGbevWWAUGqvvkbdlK3DNTKDx48Iqu1d3FgRO92/J0M8u68QuSMngnKoGYQstz3JiRQ0wly+pZ2+rU4ddarYaJ7/Rg6INt1CA34UwWu5ed4/TuJPWYlOgcYk9kUFpivKL+S1Jlqp3lrCjKekVR7gX8gL+BN4H4+u7Yjea+noE81CeI+KxC7pu9m5yi2o9u1ggNb/V8i5sCbuJY+jGey/gBjx9moHF2Jvn990n7fmaNzjPSy4XlnZsTYGPFD3FpjDsUSWpJ7frjHejEuFe6EN67Cdmphfz1yT72rjiPSVZnkyTpOqQoSomiKL8qitIDeBB4CYgXQnwthPBq4O5JUqXsrXU8ObA5T9/UXG07lZzLmZTcCgVs/j4Yz9rjyZdVub8++Dv5M7nNZNxtzVWAi43FNLFvgp+jH1Za85qaMTkxPL7ucTbGbqy3fuibNqXZr7/g/frralv+1i3krlqFMdeSVcxevpz8HTtQjLUL6hx12gpzR18I8uGjFn50crIHwGBSePDoeUbuP6NmfwuNJuIrmUdq72yNT7Czuu0T7MygSWG06GYZ6HhwXSxLvzxIerxlOlZCZBaFufUX2Es3lhqVOxXmd6DewFAgCNhYj326YU0dHsZtHZpwMimXR37ZR3Fp7e886TQ6Pun7CX39+nIw9SAvpHyH908z0bq7k/r556R8/kWNhqeEO9iyKqIFfV0d2JGVz9C9pzmQU7sy7lY2OgZMbMXwR9pibatj19KzLJm2n5z0wlo/L0mSpMZOCNFMCPEB8DuwDhgGJAOrG7RjklRLD/QOYu9rg+nXwhMwD8n9cOVJnvp9P6ay7xAmk8L5tNrVi6hP1lprvr7pa2YOttxs35m4ky3xW4jJiVHbtsdvZ1PsJopK63bOa/lg3eullwj8YwF2Xc21OhWTieT3PyDu6f9B2eunlJRQdPJkrZd5aWZrzX1NPdCXZTVLTCaeCvDmQT9PtQ9bM3PpvOM4n5yzZLALjaZLfv+zc7KiZXdfPP0tAzaad/Gmbb+meAQ4AOahu8u+OsifH1oqZJcUlZIenycTC9JlqTIAFUK0EEJ8CMQB7wKbgEBFUe68Gp270Wg0gk/uaE+vUHd2nE3n2T8OXdb/2Hqtnmn9p9HDtwd7kvbwUvx0fH7+AZ23N+nff0/SW2/X6A6cm17Hb+1CeCLAi4RiA6MOnKn1vFCA4I6e3Pl6V/zDXEmMzGbBO7s5vSep+gMlSZKuEUKIfzDfnM0GOimKMllRlO2KorwL1O/aEZJUD9zsrbDRm4eZKgp8MLotr40IV9uOxGfT/9ONvPn3UfWYxjB3tHwgOLbFWBbduogRwSPUtplHZvLk+ifJLMpU205nnsZoqrvhpkKjwbZdOzTW5nVAMZnwee1VPKc8g9CZCwIVHDzIuVG3k/zue+pxpsLCWr+G9jot/wv05qlyw3StNBp6uTjQwdEyZPnF07F03H6cs+XWHa3sWkHtPOh7V0u0ZcO0S0tMtOnnR1hPX3WfuJPmgpO7l55V23LSCinIkVlSqXqVBqBCiB3ANsAWGKEoSmdFUb5RFCWzsmOkK2el0/DdxM6E+zqx/HAi7y4/cVlv6NZaa74c+CUR3hFsT9jOK9Hf0OTXOVg1a0bWggXEP/c8ppLq3yR0GsHrIU34vnUzNJjnhb58Oo6SWt6xs3exZuRTHeh1RyilpSbW/nictbOPUVwoCxRJknRdmAOEKoryoaIoFe7UKYrSpmG6JEl1Q6MR9G/pxcTuzdS2UpNCr1B32vu7qG2frD7F6BnbLrugYl0TQtDctbk6TBfggTYP8Hj7x/F1MAdTaYVpjFk6hkfWWSrdlhhLMCl1N9RY6HQ4DR+O2913q20aa2scBt2EXfdualvKJ59ypm9fik6evKLr9XNz5K+OoQz2sAy11QuBEQU/G/P9sFKTQoftx3j6RHS157O21dFrTChdRlgq7NrY6wjp6EnTFpZlX3YuieKnF7eSFmdZoiYlOocS+V1P+o+qMqCfA00VRfmfoigHr1J/JMDRRs+cyV3wc7Vl9rZzzNpytvqDLsFWZ8v0m6bTwbMDm+I28VrUlzSZ+zPW4WHkrlpF3KOPYcqv2fCZ27xcWdG5OYG2VsyJT+OOg1G1Wi8UQGgEHQYFMPblCFx97Tm9O5kF7+wmMTLrMp6dJElSozJOUZQKKRQhxB8N1RlJqm+dm7ky78HujO5kWYEoOqOAg7FZeDiYM3+KovC/+Qf4ceu5hurmRfr49eGxDo+p24WGQoYHDqdXk15q26Izi+i3oB+bYjfVWz9s27fH/5tvcBoyxNKo0YChFCt/f8D8+kXdPIKEl16+4utNaxXAoZ6tsdKYv/onlFXRLSk30m5NWjb9dp9kWUpWtedr0tyVYY+0xT/cTW3zCHCkaUtXXH3NmVej0cSiT/ZXGLpblG8g4UyWDEpvcFUFoGmKolSZIpPl5uuPl6MNv0zuiqudnvdXnGTR/rjLOo+d3o4Zg2bQ2r0162LW8ebxz2g6ZzZ2XbqQv3070fdPpjSzZkntMAdbVnVuwQA3R3Zn5zNk7yn21HK9UAAPP0fGTY2gbX8/cjOKWPzZfnYtO4vJ2DgKG0iSJF2G0Eu0tbrqvZCkBjT97k7sfW0wLnbmIkBxmYX8fTCBjacs61ieTs7lm/VnOJPcOLKk/k7+fNzvY+5vc7/aVlRahEkx4WnnqbY98e8TPLr2UYqNl642Wxd8XnuV5tu3obE3FxgqTU3FmJmJMTtb3adgzx7O33U3OWvW1Pr8mnLDkwNsrTnYszXTWgWobdGFJUQWFKGx7MbzJ2O5bf8ZksolHSobmddpSDNGTeloGbpbbKRVT19CO1vqsMWfzmTxZ/s5sNYyLzfpbDbRx9Llsn03kKoC0GlCCFshhF1lD+Czq9XRG1GwpwOzJ3XBVq/l+T8PsfLI5ZVDd7Ry5PvB39PKrRUrz6/ktQPv4TvzWxwG3UTR4cNET5iIIbFm53bR65jbLpgpzbxJLinl9gNn+DEutdbDhHVWWvre2YIRT7TDxkHP3uXnWfzZfnLSZIEiSZKuHUKIh4QQe4AWQojd5R6ngPMN3D1Juurc7K3U3/1cbdn8wgCmDg9T29afTOHTNafZF225+b3ueDJrjiVRZGgcy35MajOJLXduoZWb+R6SSTFxNusssbmxWGvN2d3UglTGLRvHvBPz6vTaQmP5aq738qL59m00+fgjta3o5CkKDxzAVGApDJn61VfEPTMFQ3IKtSGEwE5rud5D/p6c6dOOm9yc1LboomIO5RbgrjfPXTWYFFpvO8qTxy1Dd0tMJkovUbPE2k5P/7tb0u3WYLXNwcWG1n2a4NfSMnT30PpY/vn6ELnplsJQ+9dEc2pn41kGSKpbVQWg7YC8Sh65ZT+9Kz1aqhMdA1z54b4IdFoNT88/wIZTtXtzucDZ2plZg2fRyq0Vq86vYuquN/Ce9gnOo0dTcvYs5++eQHFkZI3OpRWCl4J9+bltELYaDa+eieepEzEUXEYGM7CtB3e+3o2A1u4knc1hwbu7ObVLFiiSJOmasQZ4AXOw+UK5x53A7Q3XLUlqeEIIAtztCG9iCWhu69CEz8a2p19LS3bxq/VneGTuPkrKvkcoisKvO6M5Gp990TmvFo3QoBEa9fcVo1fw24jf1L+fyTzDyYyTpBakqm2LzizisXWPcSz9WJ31QwiB1sny+rndM5Hm27fhOGiw2pa3bRu5a9agdTRXrVUUhXPjx5P84UcXna86dloNNuWC0j87hHK4Vxu16m5icQm2Gg2l5RIPq9NyCN1ymDnxaWpbYnEJOZdYzcE7yIn+E1rRtFwA2rKrD+0H+ePiYxm6u2vpWfatsgS52amFrPzuCGf2JNf6OUmNT6UBqKIoGkVRtGU///u40N7kanb2RtUr1INvJ3RCUeDRX/exI6r2lWgBXGxc+GHID4S5hbEmeg0vb38Vj3fexP3BByhNTOT8hIkU7NtX4/MN9XBmVUQLWtrbsDA5k5H7TxNdyULIVbFzsuKWJ9vRe1xzjKUK6346ztqfjsn5AZIkNXqKokQrirJRUZQ2iqJsKvc48N85oZIkga+zLWM6++HrbKu2/e+m5rw4tBVOZQVy4jILeX3JUT5ZfUrdJzIll993xxCXWbsl4eqKEAJna0tRn55Ne7L9ru3c1/o+te1Q6iG2xm9FU+7r9ZQNU5iyYYpaZVdRlCuuFqxzc0PrYK9uB86bR/Dyf9DYmQO40qQkio6foCTaEsDlbd1G1NBhZC1ZoraZiotr1BcnnVb9PcDWmn09W/NVmGXorlFRaGJthbeVTm17MzKBFluOcCbfktVcnZbN8byLR7oFtvOg9x3N1aG7ACMea0f3USHqdlpcLmcPppKRaJn6tX91NL+9tZOEcrVEslML5VDea0CN1gGVGt5NYd58cWcHDEYTD/y8p8LQldpwtnZm1pBZhLuHszZ6LS9ufhHXKf/D+5WpmHJyiJn8ADlr19b4fCF2Nqzo1JxbvVw4llfE0L2n+Tc9p9b9EkLQfqA/Y6dG4NbEntO7klnw3m6Szjbc3U9JkqTqCCE+Kvv5pxDij/8+Grp/knQtuCnMm8f6W4INB2sd74xqw93dLEHOuhMpTF10pMJN+EX74/h1x3nyihsm4HCwcsDVxpLJe6vHW6wcvZLmrs0B89Dd/Sn7OZV5Cq3GHMQl5ifSb0E/vtr/lXpcgaGAEuPlL18idDqsgywVavW+vrTctxff/3tbbStNTsaQmFhhmZqkt97mTI+eFEdFqW3FZ85gzKu+vodVuaHCo7xd2d49jOGeLmpbSzsbujnbE2hrHrJsMCk8ePQ8jxw7r+4TW1TCO1EJbMusOB9Yq9UQ0Nqd4A6WLHlQOw/ufqsbrftYcl9F+Qay0wqxsrEEviu/O8KPz27BWJZNNxlNHFgbQ/xpuYhHYyID0GvILe2a8NGYdhSUGJn00+7LHppyIQht496Gf2P+5blNz+E44S6afj4NTCbin/4fGb/9Vv2JytjrtHwf3ow3Q5qQazQy8fBZPjmXqC5WXRvuTR0Y+7K5QFFOWhGLPt3P3hXn5ELHkiQ1VlvLfv4DLL/EQ5KkWnK1t+Ke7s0Y2tpHbRvYyovXRoTRPdiypMqc7ed5Y6lluOuFqrs/bWuYqrtCCPwc/dBpzAGRRmjYMG4DPw/7Wd0nucA8hFTB8r1mwakFdJ3XlXXR69S2Y+nHOJt99rKXg9FYWaHztARwLmNG03LfXhyHD7fsY2cHeh16X/OSNIrJxPnxd3LuttvUfUozMsj8888aT9O64LkgH/7u1FwduluimHgxyIf7mnqo+xzKKWB6TArbsyzLtsyMTWHU/jMczrVkupOKDRgEuPrY4+Bqo7b3HB3KI1/2w72JJRvsG+JMUAcPNZuak1bE9r8iObzeUswz5lg6f39xgPOHLUOG87OL5dKAV5EMQK8xYyP8eee21uQWlXLv7N2XXUXOycqJ74d8TzuPdmyI3cCzG5/FevAA/H/8AY2DA8n/9w4p0z6v8TARIQSPBXjxZ/tQ3PU6PjufzMTDZ8k01P5/ZrVA0ePtsLbTsWvpOZZM209uRlH1B0uSJF1FiqIsK/v586UeDd0/SbpetPB25ME+wfi72altb93amk/uaI+DtTngu1B1d8sZS2Cx5UwqQz/fzOIDlgAkI7/kqhU80ghNhWq6Hb06smn8Jh5v/7jaZqezI9glGD9Hy5I27+x4h1FLRqlVd02KiekHp7MxduNl90Xo9WisLEWifF5/jRZbtqhDd5XCQhyHD8NxsGV+adHRoyS9/gZZixerbdnL/iHh5akUnT6ttimGqpfms9dqeaqZNw/6WV6Lnq4OzG0XzCgvSxb5VH4RO7Pz0ZfL1N55KIoWW45gLPtOWmIyMT0mha2ZuWi0GkS5sr397m7JsIfbqts2Dnpuui+sQuY0PT6fuJOZFQLObX+e4Ycpm8lIqDjE9/jWBMtzvMKh05KFDECvQff0CGTq8FZk5Jdw16xdVxSEfjf4O9p5tmNj3Eae+vcpNJ3a0mzeXHQ+PqTPnEniy1NRSmo+LKSnqwPrurSki5M96zNyGbL3dIW7WLUR2M6DO1/vin+YK4mR2Sx4dzeR+y6vCJMkSVJ9EkJ8JoRwFkLohBBbhBD5QoiJDd0vSbqedQpw5Y7OlqCtiYst657tx0vDLCsgxWcWEpmaR/k6ie+vOEHYG6s4lmAZSfb3wXg2nbYUFKpPQgj0Wr26Pb7VeBbdukitugswqNkgxrYYi63OPFc2KT+J7w59x5+n/1T32ZGwg3tW3MOa85YlWVILUskqyrqsYEljb0+T997D++WX1Dar4BC8X5mK402D1Lb8nTvIXrIEpciSGDh/9wTO9OmrfmdUjEayly2j6MSJSq/nptcxyN2J5vaWrOZnrQI40bsNze0sbW0cbBng5oi2LCi9MHS3fNGjVanZDNpzin/KrWF6Mr+QMyYDQd28CWhtyZx3GOzP5E96E9Teko119bXHN9QZR3fzdRWTwu5l5yosF5OZWMAPz25m+yJLNjg9Po9TOxNlkqSWqg1AhRBeQoi5QojNZdvthBCP1n/XpKo80i+E5wa3IC2vmLtm7eT0ZQahjlaOzBw8k87endmRuINH1z5KaWATAn//DevmoWT//Texjz6KMbfm5/ex1vNXxxAe9PMgtqiEkfvP8Fvi5RVOsne2ZuRTHeg5JhRDsZHVs46y/tcTGIplbQ9JkhqVQYqiZANDgXigBfB8w3ZJkm4sWo0g1MuBlj6OatudXQM4/n9DuaWdr9rm72pH6yZOBJRlUxVF4dXFR3ll0RF1n4SsQu75cRfzdpWrxFpoICO/5Kpkwh5s+yCv93hd3XaxduGLAV9wb/i9atv5nPMcTD1IQanlRv8X+7+gz4I+nMw4qbbNOTqHP05d3pR0K7+muN17L3adOqpt3lNfIXDhQqxbtFDbtC4uaN3cEGUZ1tKUFBJeeJHUL75U98nfuYuYBx8id/0Gtc0QH09JXBxKqSUb6arXoSuX1fwmvBlz2lqWcnHX6/gmLIB7mlgCyPjiEo7lFWIo999m2vlkBu89TWyRJZHy1IloXj0Tj62jFVY2OopNJk7kFdJiiD+jn++M3to8V1cBhj/Wll5jLEs8lxSVorfWoinXt3OH0lg35wTJ5yz1T/6dc5zf/28XBTmW657ckUj0scv7Lnw9qkkGdBbmOS4uZdsngccr3Vu6ap66qTnPD2lBWl4Jd83cyamkywtC7fX2fDvoW3o16cX+lP08vPZhCtzsaDZvHnbdupG/fQfRd9+NISGh+pOVsdJoeLe5H9+GN0OD4NmTsTx3Moaiy1iqRWgEHQcHcMdLEbh423FiWyJ/vL+H1JjGsYi1JElSOX2BRYqixANyvJYkNQLWOi02eksl1/8Nas4/T/XBsazqrtGk8OqIMJ4YYAk2olLz2HImjbOpliGZ83fH0OmdtSw9ZPk+9PvuGL7+9wy5RZYhqPUxvNdOb8dNATfRzbeb2nZXq7vYcdcOhjQboraFuITQxaeLOpxXURS+O/wds4/OVveJzYml5289+XjPx2pbZGYkSyKXEJdrGapcGa2DPbZtWqOxsWQpA2bNJPjvJeq2sLHB6/nncL7tVrWt+PRp8rduxZhhCcRSv/6GqEGDKT5zRm1Leuddkj/4QN1WDAZzcaQcc5Dnotdxh48b/dwsNxoe8PMkul87bva0VCru5+rIeB83mlhbqa/F38lZbClX9OhsQTED9pzi1TPxatu69BwePn6e+CbWBLYzB7kn8wtJ8dRz53s9KlTnDWjtRs8xoXg1s/SlKN9AVlIB1rbmoeGKSWH9ryfZUS5zmhqTyw/Pbmbn35YCUAlnsti74hyZSZZ/cwU5JRQXGK674b81CUCbKoryHWAEUBSlBLi8GdFSnXtyYHNeGNqS9PwS7pq1k5NJta9AC2Crs+WrgV8x0H8gR9KOMHn1ZLL0BgJmzcT5ttsoPhPJufHjKTxWu7Wtbvd2ZWVEc0JsrZmXmMGt+89c1lItAJ4BjoydGkFYL1+ykgtY+NFeDqyNQZEFiiRJangpQohvgfHAWiGEDtBWc4wkSY2ATqvhrq4BFaru9g71YPerN/FoP0uw4eVkTY9gdwLdLUVv/twby2drT6Mty4opikK7t9Zw2/Rt6j5xmQV8svokW8vNTc0vLqWw5MoDVQcrB+z0lnmxk9tMZvbQ2ThaWQKiz/t/ztSuU9XtHEMODlYOaIXlLWpL/BZe3/Y6B1IOqG2vbX2NEYtGkJRvWZ99/sn5rDy3Ut02KSYMpovnf+pcXXF/8EGcbr5ZbXOdOIHQTZtwHDpUbbNp2waHQTehb2KZo5m9dCm5ay0FmQwJCZwdeSuJr7+htuVt20bsE0+St9XyOptOnaJ0715MhealXiY0cefLsADsyi3vsq9nOD+Xy6ZaaQR3eLvS3cVBbTuaW8A/qdmkl6tj8uHZRIbsPU1KiaVt1P4zTM1Np+PgAJw8bMk2lPJTfBqeE0J49Jv+aPUaiowmCkuN9LurBR2HNLM8pxIjehstmnJ9izuZwa6l58hMsmS0//35OD88u6VCNnX5jMNs/t2yRFFRvoGTOxMrJGaMBhPG0sYbrtUkAK1QRUYI4QKIS+8qNYQnBoTy0rCyOaEzd3I84fKCUCutFZ/2/5ThQcM5nXmaSasmkWLIxPfDD/B44gmMqWlE33MvuRs31uq8rextWRXRgls8nTmcV8iQvadZm3Z5FXytbHQMvCeMoQ+1QW+tZftfkSz75hD52ZcX1EqSJNWRu4FTwJ2KomQCfsC0hu2SJEmXSwiBl6MNno7WatvtHf34/eHutPd3Udv+77Y2fDexE3Zla2AWlBhp09SJIHdLUBiZksf0DVFsi7IEoD9uPUfYG6tYfcwS3H397xle/utwhWVltp5J40Ti5X2vu/A8ejTpQT//fmpba/fWrLljDc9FPKe29WjSgxe7vEh7z/ZqW4mxhNTCVDWYVRSFT/d+yoyDM9R9zmSeodOvnXhnxztq246EHby9422OpFqGNJ/OPM2R9KMoHi5oHS3BsduECfh/8w1aZ0vmMmjxYvy+tVwDrRanW27BLiJCbSo+dZq8f/+lNM0ybzd91g/E3HsfhkTLa3rujrGcGz9efS3ciotw+HY6OavN82ZD7Gz4wteJO/LS1elmj/h7saNbWIUM60A3J+7ydcNTr1Nfi8N5hZwtsHz/PF9UwtTTccxPylALI/2elEHwtqMcDrWhZTdzVeevo5N5syiLoW92pest5uVzliRnEhfmwLCH2+Ad5ESpSSHDUIqrnwNNW7hg42DO1puMJs4fTiOp3JDfzKQC/p1zguPbLJn5I5vi+O7JjZzebXktdi07y/IZhysEs0c3x1fYx2gwXZVpbjUJQBcJIb4HHIUQk4A1wOyqD5Gutsf6hzB1eCsyCwxM+GFnhYn1taHX6Pmg9weMbj6a8znnuW/VfcTlxeH51JP4fvABSkkJcY8/Qebvv9fqvI46LbNaB/J2aBPyjEbuOXKOD88mqhXNaiu0sxfjX+uKb6gzscczWPDubs4fSav+QEmSpHqgKEqqoihfKIqys2z7vKIocxq4W5Ik1bM2TZ0Z1sYyv9TeWseix3vxxZ2WOZPt/FyYPSmC2zs2Vds8HKzpFOBCE2dbtW3diWTm74nFqiwrVmo0cc/sXTz/5yF1n4OxWXR5bx1frrMMWd18OpX/W3a8QqB6KDaLnWfTKS61BBPGKkaMtXJrxT3h9xDgZMkCf9zvY3bdvQt7vTnjq6Dwbq93ebLjk+o+Cgrh7uH42FuWzDmadpSFpxeSkG8JiL4+8DUTVkwgvdAy/Hbk4pHcs+IedTutMI23d7zN6uL92LRsCUB8Xjw7OYv27edwu8dc162wtBDHu8cTsm4djjfdpB7vMGAArvfeg87LUmnXlJeHqdy6pqXJyaTPmkXuOkuGNW/9es7dehvZS/4GwFarwX7G16TeeivFZ88CcG9TD15Zt4ycr8zrtwohiOzWkj+zYtXRgU2s9XwW5M0YBysUkzn76KLT0snJDl9rS9GpXdn5LEvNqvD6v3ImjvdSUgnp5IW9szUxRSWEbz3KLy10jHq2E1qthtVp2Yw8GInfa+0Z8Xg7AJamZPFjcQ5hdwQT3NH8vLdm5nLCVsEr0Al7Z2tMikKWoZT4qGzOH06rMId121+R7F5mWbYoK6WgQiXg+lJtAKooysfAZmAfcDPwlaIoX1Z9lNQQHukXwqs3h5FZYOCumTs5EHN5i+5qNVre7PEmE8ImEJ8Xz70r7+V05mlcbh9FwKyZaOzsSHr7/0j+8CMUY83vkggheMTfi0UdQvG20vFFdDJ3HooireTy1l1ydLNh1JSOdB0ZRFF+KcunH2bzgtOUXqXS6pIkSRcIIXoKIbYKIRKEEClCiFQhhCzbLUkSbvZWDGzlTQtvS0bt7m4BLHq8F239LJm/7++J4J+nemOlKwtATQqP9QupELgWG4wXDUPcez6D2dvOkZhdqLZ9svoUd87cSUG5bFa7t1Yz7IvN6vb5tHzunLmDH7daApB90Zl8vva0GswKITgSl83m06kUGxSGBQ1jaOBQUnKLyC4w0MqtFQtuWcBD7R5SzzGmxRjm3TyPrj5d1bZ+fv0Y22IsrjaWJVdMiglTuVl9KQUpLDy9kL3Je9W2zXGbeeLfJ9iesF1te3vH23Re0I1YhyI1m/rEv08w1XYFPq+8gtbBgZySHN7f9T5HvnmEkOX/ABCXG8eaksOYpr+D273mYk7J+ckkeGqxue1mrJs3B8zZTWN2Noa4OITGEiplLVxI1oIF6rYxK4vUp58m7ZvpAHha6Rl58hBugwaQPusHwDwV7Ze9Gwl4YJJaEXh6WACbD2zE9P57aqA6tZkX7+/fSu769QBoBAzRQ+9zZzAkJgIQX1TCsdQMigpzsXM0Z2JXpWXzTXI6/j188G/lBsCUk7G8acpm7MsRNG3pSmpJKa22HmVhPyfu+6An1nY6/k3PodfOE+SP81fntM5PTOel5GTSbOt/oGuNlmFRFGWeoijjFUUZpyjK3NpcQAjRQghhEEL0/k/77UKIE0KIov+0ewghFggh1gsh1iDVykN9g3lrZDg5RaVM+GEX26MuLyuoERpe6vISj7d/nLTCNCatmsTBlIPY9+hBs9/moWviS8acOcQ99TSm/NrdKenm4sDaiJb0dHFgS2Yeg/eeYk/25d1t0Wg1dBkRxO3PdcLRzYYjG+JY+OFe0hPyqj9YkiSp7vwIzAB6A12AiLKfV0QIMUkIsV0IsU0I0ek/f7MRQswrW/ZlnhDCpqw9sOwzdJsQ4pUr7YMkSVeHj7MNbZpaAlIbvZYXh7XiwT6WOYvdgt3Z/eog/jeoudp2V7cA5j/cnY7+luBuRDtfJvcKwtHGMmQ01NuxwvzVtLxidp7NIDrd8h1sX3QGX/57psLqCtM3RHLv7N2k5VmGm474aiuDP9+kbsdmFNDx/9YwddER3GzcaOfZjkPRBu6cuYNVR5O4o8UdvNHjDVYcTuPVxUeIzyr8f/bOOjyuauvD7xmfuLsndXc3aKFQnOJ64eLu7lzcHT7cixZosVKj1N1jTeNuk8n4zNnfH5OGFgqNlrTs93nmmRzbZ02azjq/vZcw/5T5fHTsR8xZW8w7y3eTFpLGJ7M+4dy+F/Dd5nLWFtbTP7I/lw6+lCRzbzYUN1DV5CQxKJGBkQPBF0CFxYHL62Nb7TZyG3Lx+FRUVWBxWvgk+xOWlS5rtXFj9UbuXHsfG+IdmAcNBOCNLW9wxu57aL75QgLH+gs8zf5uNif2/Ym+WzajT03F7rFz4twTefeWQSS9/BIA2+u2c/PmB9lyw8zWPNdlpct4xfUz9VMGYUj2F4H6pegXvrYtpzl3J6JlNXp9xa/s3DIPy1dfoWg02D12hlBMwHvPUf/OuwBE67w84ClhyG3X0vDJpwCcnxDObzkrGXz8MTQv83+ua1JimP/xa4iZR7WGEF+YGMU7z/+P8tt+b6dzgruZC19/Fu/iH1A0Clavj7DtWxnwyyckBfhXpTc02alasQzhbKS70R3oBEVRPufPlfwswErgXSHEgTJc7wGW7mf/r8AwYNsf9j8HPCiEaF+1G0krF05IJ9Co47Yvt3DhO2t55ezhTO8f2+5xFEXhiqFXEGIM4bE1j3HJz5fw7LRnmdh7Iulz5lBy9dU0L1pE4bnnkfzqK+jj4g48aAsxRj2fDcnk8d0VvFhczckb87gnM4FLk6JRlPbPvMRnhnLG3aNY+nEOeeuq+fzRdUycncWAyYkdGk8ikUjaiUMI8XFXDqgoSjhwLTAWSAQ+wC9w93AhkC2EOEdRlHtbtl8DHgPuE0IsUxTlF0VRvhJCZCORSA5L4kPNxO8Vygtw1uiUfbYVReGbqybss294Sjib7zuKvR+TZg6IJz0qiIGJIa37jhkUR1pUIOGBhtZ9YzMi92mV4lUFZr12n32VFierCuqZNfj3AkMrdtXxxfpSzh2bSmKY3+bXlxZQ2eTkPxPSGRg1kHqbm2s+WcCkXlF8cPEYhkQPYcGOKi55fwXXT+/F9dOv4Zph1/DoDzt5fekGPrx4DEtOX4LT5+SsN1axrqiBjfdO5f1j3idQF8iQB36mf3wID53Wj5tH3kwoA5j21BJOHZ7IgOQBHOM+hrX5cN/ny7lxRh+Sg5PRa/S8sDCPTSWN3H18GmXNZcTHxvNwsQFj+XYmDaliUcWvJA25iscrUhm6toRSzVo+alxA4uUv82tFNEcV1fPBjg/YELmBRz75kcVVLs5KdXL94uvpdVwaJ5/0BquW72ZYLwvnLbqIE66bSJrvNNJ3VFEmfuSp4qe44sIZBMcMZHi5hf/LuY+FYQu558gj2G41M9nq5KYFp6KMqOc/5RkU5DfSP7OJ79ZeTFFcCcPKYulVWI9Nu5UGy1Pk1ZTh3JzFgEnNWGq/JVr7GU2L61iROYTByRmY6z8gwfULyU0jgOT2/yG2A+VAZX0VRXkR/0zunqS/M4AdLZblCiGu/ZtrxwCzgWjgTSHEb/s5J18IkdXysxZ/qO8GIBOYI4R45Y/X7HXtpcClACkpKSOKior+6tR/Jd9vreC6TzciBDxzxlBOGJJw4Iv+gnkF87jnN38/qkcmPcIx6cegOp2U33EH1h9+RBcTQ9Krr2AeMKDdY/9ca+GancVYvD5mRYfybN8UQnQdKx4phCBndSW/fpKLx+UjfUgU087riznIcOCLJRKJpAVFUdYLIUYe+MzW8x8GlgshfjjgyW0f82jgWCHEdS3bm4HRQghXy/YnwBNCiI2KogwDbhVCnKUoSrYQom/LOTcAdiHE622558iRI8W6desOfOLf0OfuN/GIwAOfKJFIDm8E+OuWit/LlwoFfwCmr3WfEDr/KRpv63VCGAB1r30a/z7Fi6L49wlVjxA6NBoXKGrLPhNCaNFobK23VtUgQEWjcbQMr0WoZhTFg6JxtlxnRAgjiuJEUfwrlapqBHRoNM2t46u+YECgKHtWgxWEMAFeNFr7PmOBp3UsIfSAFkVjb7Vf9QUBCoriblnpU0AYAB8arc2/TzW0jO+DlrEQWv9YihM0/oJCwhfU8nv17PW71vs/t7bZ/2tXdQg1AFBbP4//gBYUF4rGBQoYaCbn4d9DqjvDX/nStoTgDgGmCiFeEEK8ABwJ9ANOAKYf4Nq78M/EtpUYYBDwPDADOFtRlH5/dbIQ4g0hxEghxMjo6Oi/Ou1fy7GD4nnj/JFoNQrXfbqRT9YUd3is4zKO4/kjnker0XLbr7cxJ3sOGpOJxKefJvKKy/FWV1N07nlYFy5s99hHRYXy88jeDA4yM7/GwtHrctje7DjwhftBURT6jo3n9LtGEZMazO7Ntcx5aA2l2fUdGk8ikUjayGXAfEVRLF2YAxoJ7J3M3whE/MXxvY/t7dv/eM2fUBTlUkVR1imKsq6mpubvTpVIJJL9I/CLGaGA0PhfaPF/He0diaZBtIhJofpfewTV7/GWflEkhB7VZ0b1BaCqJkTruPuep6oBqL4gVF9wi9D78z1Bh6oGo6rBLSJMQexzjv/mQphQ1UBUNZA9gaLiT1m3GoQwt7xM/Jk9H0SPEKaWc7T7HP19+U/xi1VhbBGfLfv+NJbWf1wYWscSSsvHVPa9J6LltWesPwUCakDo/K+9u4UdxIDBA4bgArGAe69tDxAlhHArv8v/P6EoyixgnRCirh0hkA1AuRBic8sYS/AL0p1tHUCyL9P6xPD+RaO5+L113PHVVpqdXi6ZnHHgC/fD5KTJvDHjDa5eeDUPr36YOmcdVwy5gpjrrsOQmkrFPfdSevU1xNx8ExEXXdSu0NdUs5Fvh/fi3vwy3i+vY9b6XB7tncRZ8ZEdsjUsJoBTbhnBmu8K2PBzMd88v4nhR6Uw+vgMtLo2pT5LJBJJe2jzamk7qAfC9toObdm3v+N7H1P/5po/IYR4A3gD/CugHba2hZyH/9vZISQSSTfh8anoW6rsWp0edlZYsbm8NLu82FxebG4fdpeXib2iGJbizyl9eXE+awvrcbh9OD0+7G4fDo8PIWD57UcAkFdlZcazv+73ntce2YsbZ/QG4JbPN/P5+tL9nrft/hMINOpotLsZ+uCC/Z5z9ugM/nfyIACe+TmH91YWodcqoKhoNQKNIlAUwRXHBGIw2Ak3hfPmT8HoNAp2r5VqRyUCHwIvQSHVxMRvw+F1MCvuFgrK/ef9sHs+je4GQEVRVPThK9Hom+gd3ptZEU/h9qnsthQwd9eX+IWfikZvQRfslys3DHqEMIagqD5e3/IahbYi/3mKitZcjEbXTIQmmPu8N+F0uCn2VPK8fgkoAo0Q6PUCU2AtWlVh9rY4+pXFobjcfJlZS16EC62qYoyKJCjKiNGg4svO47L3BB6NDqtZ8OkUBZ0q0Or1hE6aRHCAimhoJPP/ltK/0AwI1vWGglgFrSoIHDmC0MEDsKg+vD//wpWz7m/vn1W7aYsAXYp/VveDlu1zgGWKogQBf9d8cSgwVVGU8fhFZF9FUc4QQvxlnKwQwqkoSoGiKMlCiBJgBPBVWz6I5K8ZkxHJx5eM4fy31/C/73dS2+zitpl99ynD3FaGxw7nnZnvcPkvl/Pq5leptldz99i7CTvpJAyJiZRecy3VTz6FK38XcQ/cj8bQ9tBXk1bDE32SGR0ayC05pdyQXcLqRhuP9E7ap4lwW9HqNIw7OYvkfhH88s4ONvxUTGl2AzMuGkBYbMCBB5BIJJI2IoQoUhQlBMgSQmzoomFXAw8riqIH4oHmPeG3LSzFX51+U8v7nnoLmxVFGS+EWAEcA1zfRfZIJJJ/GJ8qsDg8NNrdNDo8WOweGh1uLHYPE7Ki6NVSaffxH7PZXNJIk9NDs9OL1enF6vISZtaz5i5/AGNuVTOnv75yv/cx6bWtAnR7uYUlOb9HRxi0Gkx6DYFGHUIIFEUhxKxnXEYkJr0Gk17b8tJg1GkZtlff1JOHJTI4OQyjToPAjUc48QknbmFnWdliHL5mwo0R/HLjZPRaDRuq1/BD4Twcvmbs3iY2+qxMmWOl2d3MuzPf5cajjgJgypwp1Dt/n2t7YqP/fXjMcD677D3An052x7InWs+xAVW1oNPo6D3Cy3/H+IWtddGblFvLMKgaTOZgTIbhGLVGIisdnJi3FNVqpdhZTriuBp3Thzk0nIgjT8agOQPf9hzibnuI6EoneDxcFwNOPehUSLztLsLGjkev0VPx38sI3nI3AKkKjNeA1gemtDQyf/RncjT/tpySh/8LZINGww15RoTRiMZkIuG28wk55hh+qWvCu+xp1H678On1aHV6ZtfqcOn0TE9PJvaomyl1ujnjl9V4B8SycbAOr1aHW6fDq9MxLS6Ss6eMx5iRzu25pSzO7MUZCWn8Xne5e2hLDqgeuByY2rJrCfCaEMLT5psoyrvAm0AQEC2E+EBRlEnAfcB4YAXwihDiK0VRhuIPwdUDi4QQd7flHl2Rt3K4k19t5fy31lBucXLysESemD24dRasvZQ1l3H5gsspbCpkavJUnpj8BGadGXdJCSVXXIE7fxfmESNIevEFdBF/G/21X7JtDi7ZVkie3UXfQBNvDkwjK2B/YQ5tw9nsYdEHO9m9uRadUcvkM3rTd1ycLFAkkUj2SwdyQI8FXgd8Qog0RVFG4i8EdHwn7bgI+C/+afbrAC8wQwjxpKIoZvx9uZOAUuA/LRO5Gfir8hqAH4QQD7f1ftKXSiQHlz0CDmDlrjpKGuzUNbupt7losHtosLkJNOp44Sx/X9GluTVc8Paa/Y716CmDWosPnf/2Gn7N9YvGQIOWYJOeIJOO6CAjn1w6FoAKi4PXlxYQaNQSaNQRZNQRYNARZNTSNy6EtCh/LndtswtVFZgNWn+hoT88Owoh8KgeDFr/okNeQx5barbQ4GrA4rLQ4Gx5dzXw+OTHSQzyy5uRH47E5fvzWtbExIm8Ov1VAD7L+YyHVj3UeixAF0CQPohAQyD/m/A/BkX7ReNTa5/C6XMSoA8gQBdAoD4Qs85MjAhmjL43vsYGGupKqWwoRW91EBQZR8yxJ2LUGXH8vJDaV1/D19SE2tS0T3eH1A/eJ2CUv6B5/hFH4ikv54+YBgwg/csvALAuXkzVQw+jCQxEExDgfw8MQDEHEHHeeZgHDWSb1U7dN9/isjZj1Ruw6vU06Qy4jEauGZCJqV8/cmxOzli7E5fTiVWrx6vVsqda1O3pcVyf5i/8efWOIr6o2n/bxd2TB2PSKNR5fBy1LgejCkYBRhUMKhh8MMKpYapbz/hTslhS38TqMgsnR4bSOyFkv2O2l7/ypQcUoIcK0mm2jUqLkwveXkNOlZXJvaN59ZzhBBrbshD+ZxqcDVy98Gq21G5hSPQQXjriJcJMYfisVspuugnbr8vQJyaS9OormHr3bvf4Nq+PW3NL+bKqgUCthqf7JHNSbPiBL/wLhBBsX1bO8s/z8HpUskbEMPWcPhgD9Ae+WCKR/KvogABdCxyPX/ANa9m3QwjRv7ts7A6kL5VIOo8QAqdHxWzw59cV1dmYv7WC6iYXNc0uava8W108duogjmupEnvyK8vZWNz4p/Giggysu3sGALlVVh6at4NQs56wAD1hZgNhAXpCTHpGpoWTER0EQF2zC51GQ5BJh7YDEW8ATq+TGkcNcYFx6DV6fKqPlze9TK2jllpHLXXOOmodtTQ4G5ieMp0npvhXF9/e9jbPrn92v2N+eOyHDIkeAsANi29AFSqhxlBCDCGEGEMINgSTEpzChER/xV6r27/aGWQIIkAXgFbj/516Kipw7tyJt64OX10d3rp6fHW1eOvqiX/gfgxpaQDkjBqNarX+yY6A0aNJfd+/Mto4dy4Vd96FNjgYTWgo2uBgtKEhaEJCibrickx9+gBgmTcffF6U4GCcJjMWkxm72cyAuBh04eEUOVy8UVJDvcdLvcfX8u5/3Z2ZwMVJ/no1F2wt4Kfapj/ZpFWgdMoQFEWhyOHihA15hOi0BOu0hGi1BKJgUmGSzsgEvZnkfhGstdjYVWWlYlMdWrsXpdmLxuYFqweNxcvk03sxYJJf8H/3wiaKd/w5I0Nn0HDZC1MBqC+34XZ6icsI/dN5HaHDAlRRFB1wEf6Q2tYlKCHERV1iWRchnWbbsdg9XPL+OtYU1jM4KZS3LxxFVJCxQ2PZPXZu/fVWlpYuJS0kjddnvE5CUALC56P6iSepf+89NAEBJDz9FMHTprV7fCEEH1bUcXdeGS5VcEFCJA9kJWLq4MotQH2FjZ/f2k5daTNBEUZm/GcACb3COjyeRCI5/OiIABVCjFIUZeNeArT150MF6UslkgOz96rl/C0V5FQ2UdbopMLioNLipLLJSVyoiUU3TQXgt7xazn1r9T5jmPQaooON3D6zH7MGxwMwd2MZjXY3EUFGogINhAcaiAj0C0xjB7sD/BVWtxWXz0WUOQqA5WXL+bHwR6psVdQ4aqi2V9Pk9oukb076hoxQf/2QMR+Nwe61t44TZgwjwhTBhMQJ3DrqVgB21u1kU80mwo3hhJnCCDP6X6HGUMw6MwfCungx3spKPFVVeKuq8VZX462pwTRwIAmP/A+A+vc/oOqRR/Z7fco7bxM4bhwA5bfdhvB40IaFow1veYWGok9MJGC4/+tZ+HygKHiAareXKpeHSreHfoFmMgL8z8cP7ypnWYOVWreXGrcXd4t+Ctdp2TnJvwq7vdnBkWtz9rElXKclQq/jqpQYzk7w1zX5uqqBAruLEBRMHoHJpWJwqOhtXiKbfWQMiSE6xR9K/dOb2yjd2YDT7tmnKWZAiIH/POHvylW1u4kvHv/9e1ujUTAG6jAG6Bl5TCp9xvr/vnauKMdS48Bo1mMM0GEw6zCa/e+x6f4Vzz26sKsiBDsjQN/Cnys6DXgVOBv4VQhxVZdY1kVIp9k+nB4f13+6iR+3V5IaGcD7F40mNbJjZfO9qpeHVj3EV3lfEW2O5pXpr9A3oi8ADZ99RuWDD4HP16HiRHvYarVzyfZCCh1uBgeZeWNgGmnmjolmAJ9HZeXcXWxeWIKiwIhj0hg1Kw1NJ4StRCI5fOiAAF0EnIV/BXS4oihT8Yfgtn/m7R9E+lKJxI/F4WFTSSOlDXZK6h2UNNgprbdT0uDgvuP7c+JQ/6rSGa+vZPXu31eVDDoNcSEmMqIDefc/owGot7lZnl9LdLCRmGAj0cFGgoy6g5IGJITggx0fUG4rp6y5jIrmCsqby7F6rMxIncEzU58B4IMdH/DEWv8Kpk7RER0QTXRANDHmGK4bfh1poWkArKlYQ5AhiEhTJBHmCPSatkeReWtrcWbn4Ckvw1NRgbe8Ak+F/5X00kuY+vij5XLHjcfX8IewUq2WoEmTSH7NH5rr2LqN5sWL0EZGomt5aSOj0EVGoAkJ+dPv1ubzUe70UOHy0OT1cVxMGADbrHau3VlMpdtDvce3zzUPZiVwaXIMAP/dtpt5NRbCdVqiDXqiDDqiDDqi9Toe7uXvOW/3qWy22glWwdjsQ2f14rS4sVlc2BtdKFqFSaf7P2Pxjjq+e2Hzfn9Pk8/szaCpSQD8+PpWqgqbMAXpMQcbMAXqMQfpCQg1MGKm/9/E4/bRWGnHFOQXlnqjtsekmHVGgG4VQgxSFGWLEGKwoiihwDdCiKndZGuHkE6z/fhUwX3fbuPDVcVEBRl484JRDN0rUbw9CCF4ZfMrvLb5NQJ0ATw99WkmJvpnZmyr11B27bX4LBZCTzyBuAcfRGNsv3hs8vq4IbuY+TUWgrUanu2b0voF0lGKttex8L2dOJrcxKaHMOOiAYRGH3h2TiKRHN50QICOBl4D0oHNQC/gBCHE+m4ysVuQvlTyb8GnCsoaHOyqbaaw1kZRnR2H28fjswcDsKqgjjPfWPWn62KCjdw2sy+njvALhGV5NTg9KglhJhJCzYQF6A/aw3+Tu4ldjbsotBRSYi2h1FpKaXMpJdYS3j76bXqF9wJg0qeTaHQ1AqBRNMQGxBIfGM+Y+DFcOfRKAKrt1f5w24A4wk3haJT2TcgLIfDV1uIuLsZdXIK7uAhPaRn6uDhibroRgIZPPqHygQf/dK0SEEDyKy8TONafm1r/4UcoOi262Fj0sbHoYmLQhoejaP96Fdji8VLsdBNl0BFv9OeiPlZQwaK6Jkpd7n3EZYBWw65Jg1AUhXy7k8mrs4kx6Ik16og16Ikz6ok16JkWEczwUP/iTJPXh0mjIFwq1jonzQ1Omhtcre/9xseT2NufJvblE+upLLD8yUZjoI7/Pj3Z/7uotLH4w2wCQowEhBgICPELTHOwgeiUYIIjOl73pCfRGQG6RggxWlGUjcAEIYS9J+a1SKfZMYQQvLw4n6d+zsWo0/DcGUM5ZlB8h8f7Ku8rHlzp/3K5a+xdnNb7NADcJSWUXnkVrrw8TIMHk/Tii+hjYzpk71tltTyQX45HCC5OjOLerASMmo6vXNqb3Cx6fydF2+rQm7RMOasPfcbEdXg8iURy6NNeAdpyTSj+wnoKsEII0dgdtnUn0pdKDjecHh8FNTZCA/QkhvknmG/9YjNzN5Xj9qr7nKvTKGQ/NBOdVkNds4vXfy0gOdxMUkQAyeEBJIWbMem7NhT2QLh9boqaiiiwFFBgKSDEEMI5/c4B4OOdH/Pomkf/dE2MOYYnpjzBiNgRACwuXkyQIYiEoARiAmLatWr5R7wNDbh3F+IuLCRwwnj0sbEA5M84Ck9JyZ/ON/bpQ8Y3cwFw7tiBZf589PEJ6BMS0CfEo4+P3++K5R/ZO+y50uXhjZIaipwuih1uip1uLF6/wLw7I56rU/02XbWjiK+qGog16Eky6Uk0GUgw6ok36rk4KRqtoqAKgSr8//ZCCFx2L021DppqnVjrnHg9PkbNSgegNLueb57btF/7JszOYuh0fwGoNd8V0FjtIDDMSGCooeXdSECogbCYf1cXhs4I0B/xhxXdit+x1gIhQogZ3WFoR5FOs3N8s6mMWz7fgkdVuX1mXy6dnNHhGbwVZSu4cemN2Dw2Lh54MdcOvxaNosHXbKP81ltpXrQIXUwMSS+/hHnQoA7dY2OTncu2F1LsdDMk2MwbA9JI7URIrhCCrUtKWfHlLnxelV6jYplydh+M5o4VaJJIJIc2bRWgiqL87dOEEML+d8d7GtKXSg5lbC4vS3JqyKmykltpJbfaSmGtDVXA9dN7cf10f/jjvd9s47e8WjKig8iMDiQ9KpC0qEDSIgOJDTH+I+GLHtWf46fX+sXh3b/dzeaazZRYS/CJ31fveof35ssTvgRge912vsr9itSQVFJCUkgOTiYxKBGTrutWz5w7d1L/0Ue483fh3r0bn+X3lb3E554jZObRAJTdeCM+SxOG1BT0ySkYUpLRJyWjT0xEG9T2FK9at5dcm5MCh4t8u5MCu4vdDhfVbi87Jw5EoygUO1yMXuXvuakA8UY9KSYDyWYDx0eHcVSUv4COxePFrNVg2GuRQlUFzfVOLDUOQqJMhEb7v8IXvb+TXRuqcTv3DcPVGTRc+vwUFEWhqdbBwvd2EhRhJCjcRHC4/z0w3EholBmDfGb8E50RoFohhE9RFA3+/M8w4D0hxJ9LSv2DSKfZedYW1nPp++tosHs4a3QKD544oMNtWnLqc7hq4VVU2auYmTaThyc+jFFrRKgqNS+8QN1rr6MYDMT/72FCj+9YlwKLx8sN2SV8X2shROcPyZ0VHdahsfZQV9bMz29tp77cRnCEiRkX9Sc+q3NjSiSSQ492CFCVfUpD7IsQ4uAul3QS6UslhwIOt48dFU3srGgiu7KJ+48fgE6robrJyehHFraeZ9BqyIwJondsEMcMjGfmQH90096raf8EtY5adtbtJKchh9yGXPIa8ihsKuSpyU9xZOqRAFzwwwVsq91GWmgaGaEZZIRmkB6WTmZoZmtobVfgravDlZuLMycHV24errw8FI2GtDmfAmBbtYriC/8DgC46GkNaGob0dAxpaQRNm4oxPb3991QFxU43uTYneXYn/YLMTI/0F8G5bmcxcyr3rdRqUBRSzQa+Hd6LcL0OnxAsrbeSajaQZDL8KQpu739fW6OLTb8U01hlp7HaQVOtA9Xn/8oee1JGax7lwvd3UppdT0ikmZAoEyFRZkIiTQRHmonPDEXpYCXhfzudEaC3CiGeONC+fxrpNLuG3bU2Lnp3LbtrbUzqFcXL5wwnxNSxUI0qWxVXL7qa7PpshsUM44VpLxBmCgOg6fvvKb/zLoTTSeR/Lyb6hhv+Nrb/r/hjSO5FiVHcm5nQqSq5XrePFV/vYuviUn+BomPTGHWsLFAkkfyb6EAO6N2AC3gD/6T8fwGDEGL/ZRp7KNKXSnoqv+XV8vXGMraVWcirtqLu9fj6y42TyYoJ9tejWLKLjKhAescFkxoR8KeelQcTIURrTub4hPEArKpYxSU/X7LPeVpFS1pIGlcNu4oZqf4AwwZnAyGGkNa2I522xefDXViIJiioNWy25PIraF6y5E/nGtLSyPh+PopGg2qz4crPx5CRgTY4uH333KuiqtXr47bcUrKbHeTbXa1VZAHOiIvg+X7+8NW5VQ2sttjIDDCSaTaSGWAk0WRAu58JA59XxVLjoKHSRkOF3f9eacda7+SiJyaiaBSs9U7ev3MFABqdQmiUmdCYAEKjzKQNjiSpb0SrrT2lcM/hRGcE6AYhxPAD7funkU6z62iwubnsg/WsKaynT2wwb14wkuSIjsWs2zw2blp6E8vLlpMcnMzLR75Meqh/tsyxbTulV1+Nt7KSwEmTSHzqSbShHes7tHdI7qAgM68PSGstnd1RCrfWsuj9nTisHuIyQpj+H1mgSCL5t9ABAbo/X7leCDGi663rPqQvlfyTqKpgd52NjcWNbCxuIDrY2Bo2++7y3dz/3Q60GoXescEMSgyhf3wI/RNCGZwUetBzM/dHjb2GrbVb2Va7jR11O9het51GVyMGjYFV56xCr9FT56jj1l9vpW9EX/pG9KV3eG/SQ9MxaA1dZofw+XDl5+PcuhXH9u24duzEmZuLcDiIuvYaoq9sKTz09DM4Nm/G2KcPxt69MPXpgzErC01A+5/57D6V7GYH21peO5odZNucLBndlySTAVUIMn/diluopJmM9A400TvQRK8AI4ODA+gd+Ndhwz6fiqXaQX25jcBQQ2tk2qL3d7JzRcW+JysQHGFi9m0jCQgxIFRByc56wmIDCIowoZErmQeVdgtQRVFmAEcB5wHv73UoFBjd03qbSafZtbi8Pm77YgtzN5UTEWjglXOGMzYjskNjeVUvj615jDk5cwg2BPPM1GcYG++vdOatraX02utwbNiAITWVpJdfwpiV1aH7WDxebsopYV6NhUCthqf6JHNybHiHxtqDvcnNwvd2Ury9pUDRmb3pPSZOzpJJJIc5HRCgO4HjhRD5LduZwDwhRL/usrE7kL5U8k/w+tJdLN9Vx+aSRiwOT+v+zOhAFrb00qxqclLe6KBffEiPEJtun5sddTswao30i/T/N793+b18nf916zkRpggGRA6gf2R/Lhp4EQH6ri9AI4TAU1SEY+s2QmYdi6LR4KmsJH/qvh2g9ElJmPr1I2TWrNa8zY7S6PFS5/GSGeAXje+U1XJXbinqH85LNOp5c2A6w0L8n3uX3Umi0XDAKDWfR2XL4lLqypqpLW2mocqG6vXrlT5j45h+ob8OavaqCgq31BEeH0B4XADhcYGExQagN/zzfx8SPx0RoFOAqcDl+EvL76EJ+FoIUdj1ZnYc6TS7HiEEry7dxZM/5aBVFB44cQDnjEnt8FgfZ3/ME2ufQEHZp0KucLup/N8jNM6ZgyYwkIQnnyD4iCM6fJ/3yuu4L78Mlyo4Jz6Ch3olEdCJEJw/FSgaGeMvUBTQ8SpyEomkZ9MBAXoK/vDbPW1XhgGXCiHmdoN53Yb0pZLupMHmZk1hPesK65k5MI4Rqf7wx9NfX8ma3fVEBRkYmhzOsJQwhqWEMTgpjCBjzyjs0uhsZEP1BjZUbWBjzUZ21u3Eo3qYmTaTJ6c8CcDSkqWsr17PwMiBDIwaSHxgfJdPWKs2G46tW3Fs2oRj02Ycmze39szMmD8PY2YmQggq770XQ1o6pgEDMPXvhzYkpEP3s3p9bLba2dhkZ4vVwRarnSKnmxEhAcwf4V+dXlzXxCMFFQwIMjMw2Ez/QDP9g0yE6f/6305VBZZqOzUlVmqLm6kpsXLE+f0IjjAhVMEbN/yK1+VDUSAsNoCIhEAi4gOJ7xVGckvYrKTn05kQ3IFCiG3dZlkXIZ1m97FwZxXXfbqJZpeX88amcu/x/TtcnGhZ6TJu+fUWbB4b5/U/j5tG3NSa39Dw6RwqH34YvF6irr2GqMsvR+lge5VtVjuXbi+iwOGiT6CJ1wek0jewc+GzdWXNLHh7O3VlNoLCjUz/T//Wnk8SieTwooNtWGKAMS2bq4QQNV1vWfcifamkK3F6fPyaW8PKgjpW7qoju/L3+pVXTs3k1pl9AdhebiHYqCc5wtxjIowqbZWEGEII0AegCpWJn0zE6vnd/rSQNAZHD2Zi4kSOST+m2+zwVFfjra7BPHAAAHXvvkv1Y4+3HteGhmIeOhTTkMGEnXpqa35nh+6lCnbaHETv1UvziDXZ7LA5W88xaxT6B5kZHRrIfVmJbRp37/zKou11rJu/m9oyG17XvhVnZ101mLRBUQDs3lJLUJiR8PgAdD1gxVvSMTqyAnrl3w0ohHili2zrEqTT7F7yqqz89/11FNXZGZsRwSvnjCAisGP5CnkNeVy98GrKbeVMSZrC45MfJ1DvL9FtX7+e0muvw1dXR/CMGcQ/+mi7ynfvTXNLwvuXVQ2YNQoP90ri7PiITjk3r8fHqrkFbF5YAgoMPzqV0celo9XJAkUSyeFERwTo4YD0pZLO4PT4WFtYz4TMKDQahWqrk9H/81el1SgwMDGUsRmRjEqLYGRqOOEdfI7oDmodtaytXMuayjWsrVxLUVMRT095mqPSjgLgibVPoFW0DI8ZzrCYYa1FFbsad2kp9tWrsa9bj339ejzFxRiyMsmcNw8AZ04ODZ98gnnoUMxDhmBIS+vwc02d28v6JhvrLDbWNtnY1OTAoarcl5nAFSn+Xu3PF1ZR7nIzNCSAocEB9AowofubPEohBJZqB1W7LVQXWakusqIzaDjxen/mXtG2Oua9tJngSBPRycFEJQe1vAcTGGboMRMQkq6hIwL0nb8ZTwghLuoq47oC6TS7n0a7m6s+3sDy/DqSI8y8fu5I+id0LKSjzlHH9YuvZ1PNJrLCsnjxiBdJCk4CwFNRQenV1+Dcvh1DVibJL72EIS2tQ/cRQjCnsp47cstwqConxITxVJ9kQnSdm00r3lHHwnd3Ym9yE50SzIyL+hMe1zGhLJFIeh5SgEokB0ZVBdmVVpbl1fBbfi2rd9fj9qp8d/VEBiX5iwq+vDifvnHBjEqP6HBV/e4kryGPW5bewi7LrtZ9eo2eIdFDuGjgRUxKmtSt9xc+X2sXgJKrrqZ54e9tZBSDAdPgQQSMHEn0tdd2OCoM/M9DxU53a8/0QoeLsS29NPeQbDIwMiSAU+MiWtuitGXcPaJx6cc55K2rwmX37nNOeHwgZ907GkVR8Hp8eN0qpsCe97cg6Xo6HIJ7qCCd5sHB61N5eP5O3l1RiEmv4dFTBnHysKQOjeXyubh/xf3MK5hHmDGMp6c8zej40QCoTieV992P5Ztv0AQHk/jUkwRNmdJhu/NsTi7bXsgOm5Nkk4HX+6cyPLRzgtHR7GbxB9ns3lyLzqBhwuxeDJiUIGfvJJLDAClAJZK/Z0lONTd/voXaZlfrvuQIM5N6RXPRhHSyYoL+Qev+jCpUdtbtZEX5CtZVrePFI17EoDXQ7G5m6mdT6RvRl9FxoxkdP5qh0UMx6f66Kmtn8Fks2FatxrZiBbYVKwg98USir74KgJqXXsaxYT0Bo8cQMGokpkGD0Bg6tkqsCkGOzcnKxmZWW2ysamymyu1l3bj+rVVpz95cQN8gE6NCAxkZEkis8e9FoVAF9RU2KgssLa8mBk5OZMiRyYC/Km3h1lpi00KITQ8hJi2EmJQQTEFSbP5b6UwOqAJcCkxv2fUz8KboYcpVOs2Dy1cbSrnz6604PSrnj0vl7ln9MXQgDFUIwXvb3+OZ9c+gUTTcNvo2zuxzJoqiIISg4YMPqXr8cVBVoq+7lsjLLuuwwHP6VB7YVc47ZbXoFLg9PZ4rU2LQdEIwCiHY8Vs5v32eh9etkjYokmnn9SMgpOeEFUkkkvbTgSJEfYUQ2Qfa19ORvlTyR4QQ5FU3szi7mpxKK8+cMRSA/OpmTnp5OeMzI5nUO5rJvaJIjexZkUAWl4XlZctZVraM5WXLaXA1tB57/5j3GRbjDwt1ep3dJjgBhMdDzSuvYFuxAufWbaD668UqBgPhZ51F7B23d+n95lY1cGdeKfWe33Msg7QaRoUGcl9WQrtrYjibPSx4ezuVu5twO35f3dToFIZNT2HsSZmAv4+6Vq+RE/GSVjojQJ/EX81vT0juBcAmIcStXW5lJ5BO8+Czo7yJyz9cT3G9neEpYbxyzgjiQjv2Bf5r6a/c9uttNHuaObXXqdw15i70Wv+MmW3NGsquvwFffX2n80IBvq9p5MbsEhq9PqaEB/NCv5QDzvodiMYqOwve2UF1YRPmYD1HnN+vNZFeIpEcenRRH9Ae1zP7QEhfKgF/tNP6ogYW7Khiwc4qiursrcd+u20aSeEB/vYfPtGhyefuQghBjaOGmAB//uLXeV9z74p7AQg2BDM2fiwTEiYwLmEcCUEJ3WaHp6oK27JlBE6YgD4+HoD86TPwlJZi7NuXwPHjCRw/noCRI9CYOi58S51ufq238muDFYeq8t6gDACWN1i5eFsh48KCGBcWyNiwIPoHmv82dxPA5fBSkd9IRX4j5XkWjr5kAEHh/qq0b928DK1OQ1xmKHEZocRnhhKdHIxW33P+/SU9j84I0C3AcCGEt2VbD6wXQgzuFks7iHSa/wwWu4cbPtvEouxqooIMvHjWcMZldqxfaIGlgOsWXUdhUyHDY4bzzNRniDT7x/KUl1N6zbX+vNDMTJJefBFjRnqH7S51url6RxGrLDYi9Tqe75fS5nyHv8LnU1n3fSHrvy9ECBg4OZHxs7NkPyqJ5BCkrQJUUZQoIAb4AjgV2POEFwq8K4To031Wdj3Sl/572TuX77lfcnnulzwA9FqFcZlRTO8Xw7Q+MSRHdH0vy87gUT1sqNrA4pLFLC5ejMvnYtHpi9AoGmrsNXy480MmJ01mSPQQdJruaekifD4cmzfTvHgJzcuW4cr2Bz7E3nUXEeedC4Bjyxb0CQnoojo3Of1rvZX5NY0sa2imwPF7+HOEXsvG8QMwajT4Wp7ttW1YiWyotLFjeQXluQ3UFFvZWxYce8Ug0odEA/6+6OZgvVzdlLSLzgjQrcBQIYSvZVsHbBRCDOoWSzuIdJr/HKoqeGlxPs/+kotGUbj5qD5cNjkDzQFm2vZHk7uJW5feyvLy5cQFxvHc1OcYEOUvPa46nVTe/wCWuXP9/UIff4zg6dMPMOJf41UFzxdV8XRhJSpwSVIUd2cmYOxEkj9AZYGFBW9vp6nWSVhsANMv7E9seufErUQiObi0Q4BeB1wPJADlex2yAC8KId7qHgu7B+lL/13Y3V4WZ9fw/bYKmhwePrjY30VoW5mF/1tWwIz+sUzpHU1wDywetLZyLV/mfcmvpb9idfvboxg0BsYljON/E/9HqDH0oNjhLiqi8MyzWntxoiiYhwwhcPIkQmbOxJiR0eGxVSHYbHUgEAwP8Ud+3ZZTwnvldRgUhZGhgUwJD2ZSRBCDgwIOuMLpcfuoyG/EUu1g0FR//Y6SnfV8+/wmUCAqKYiEXmEk9AojPjNMphNJOk1nBOhTwGDg3ZZdFwBbhBC3dLWRnUE6zX+eJTnVXD9nE412D1N6R/PM6UOIDDK2exyf6uO5Dc/x7vZ3MWgM3D32bk7udTLgn6Ft+OQTqh55FLxeIi+/jOhrrmmtINcRVjc2c+WOIspcHgYGmXm1fyq9AjuXC+J2eln2WR7ZKypQNAqjZqUxYmYqmg72T5VIJAeXDoTg3imEeKQ7bToYSF96+GNzeVmYXc0PWytYnFON0+PPR4wNMbLopqkEGrtnlbCzNLubKWsuo0+EP6jgve3v8dS6pwg1hjIlaQrTkqcxPmE8AfruW6H11tRgXbgI66KFJDz+OLrwcITPR8GxszANGEDQtGkETpyALrzjPcJr3V4W1zexuN7Kkvom6j0+pkeG8OFgv5Dd0eygyuVhdFgggQd49lF9KtVFVkqzGyjNrqeiwILqFWi0Chc/PQmDSYfH7aNkRz0JvcJkZVpJl9ORNiyThBDLFEXRAJcBR7Yc+gV4Qwihdpu1HUA6zZ5BeaODaz/ZyLqiBmJDjLxw5jDGZHQsJPfH3T9y74p7cXgdnNHnDG4bdVtrXqh9w0bKrrsOb00NgRMnkvjUk2jDwjpsd6PHy805JcyrsWDWaHi4V2Kne4YCFGysYfFH2TibPcSkhTDjP/0Ji+1Z4UsSieTPdKQKrqIoAUAS0PoEL4TY0dW2dSfSlx7+3DN3Gx+sKgIgLsTEzIFxzBocz/CUcLQdiFzqTprcTSwpWcKCwgUsL19OQlAC3530HYqiUGOvobCpkGExw7ottBb8fTmtC37BumABjo0b2ROjmvjM04Qceyywb/hyZzh78y4W11vZ82QeqtMyOTyYmVEhnBoX0aYxfB61NS9z6cc5bPu1rPVYaLSZpL7hJPWNIG1wJDq9TBGSdC8dEaC7AB/wNvC+EKJ8vyf2EKTT7Dl4fCrPLsjllSW70Chww/TeXDktq0OOLbchl+sXX0+JtYSh0UN5ZuozRAf48xG8NTWUXn8DjvXr0SclkfTiC5j69euw3UIIPqmo5648f8/QWdGhPNUnmXB95xybzeJiyYfZFG6tk+1aJJJDhA6sgF4JPA404Ped4O+Z3fH4u38A6UsPH3yqYOWuOr7dXIbLq/L8mf6KrxuKG5i3uYJZg+MZlhzWoXSZ7ua3st/4POdzlpUtw6N6AEgMSmRG6gyuGXYNBu3BCQ21rVpN8YUXtm4b+/UjeMZ0gqdPx9irV4f9uMOnsqzByk+1Fo6KCuXoKH+48BXbC8mzu5geGcKRkSEMCz5wWK3X7aMst5Hi7XUU76gnIj6QYy73Z8kVbqkld21Vi+gMJySyfdVvJZLO0qEQXEVRpgEXAicBvwFvAd/uKUjUk5BOs+exNLeGG+Zsot7mZkJWJM+eMZSY4PaHtlpcFu5YdgfLypYRZY7imanPtJZOFx4PVU88ScMHH6AYjcTdfz9hJ5/UKbvz7U6u3F7ElmYH8UY9L/ZLYWJ4cKfGbG3X8kU+XpePlAGRHHF+XwJD2x+iLJFIup8OCNACYJoQoqgbzep2pC899MmubOKrDWXM3VhGtdVfpCYqyMBvtx2BqYeueHl8HqrsVSQF+/MSX9r4Eq9veZ2EwASOTj+ao9OOpn9E/26duHUXFdH04080L15MyjtvozGbUV0uSq+4gsBJkwmeMR1DUsf6ngPUub0sqLPwU20TS+r9VWsBTosL58V+qQB4VIG+jRMCO1eUs2tDDaU5Dfhawqg1GoWUgZHMurJH1QmV/IvpcA5oy8XBwBn4xWgv4CMhxI1dbWRnkE6zZ1LV5OTaTzayenc9EYEGnjh1MNP7x7Z7HFWovLLpFV7f8jo6RcdNI2/inH7ntDojy7ffUnHvfQink7AzziD2rjs73LwZwK2qPFZQySsl1SjAVSkx3Joeh6GTBYoaq+0sfHcHlQVNGAN1TDmrD71Gtv/3IZFIupcOCNAVQojx3WnTwUD60kObR7/fyeu/FgBg0ms4ekAcJw5NYGJWdI9qlwL+idnNNZuZVzCPHwt/JCkoiU+P+xSASlslVfYqBkcN7lbR6Skvp+n772n6/gecO1qi5RWFlLffInDcuC67j0tV6ffbNuw+v1CMM+g5KiqEo6NCmRAWhOkA9SFUn0rFLgumQD2RiUEA/PDaVgo21RAUbiR1UBSpAyJI7BOOwdQzc3gl/046JUBbBlCAY4H7gf5CiB7VbVg6zZ6L16fy0uJ8XlyUj08VnD0mhbtn9SPA0P4vycXFi7nrt7uweqwclXoUD4x/gCCD/8vYmZND6bXX4ikqxjRoEEnPP4c+oXN9vn6tt3LNziKq3F4GB/sLFGUGdK5AkepT2bigmDXf7Ub1CXqNjGHyWX1k8r9E0oPogAB9ADADnwLOPftlDqiku3B7VRZlV1HW6OTiif62ZIuzq3nj1wJOGZ7IzIFxPbJ6bYm1hHm75jGvYB7F1mIAgvXBHJV2FHeNvQu95uDYbF2yhNLLr2jdNo8YQcjMmQQfdRT62JgOj5tnc/J9jYX5NY3ckRHPtJYWb/fklRKk1XJ0VChDgs0HFNZuh5ei7XUUbq2laGsdLruXfuPjOeJ8f6pRbWkzigYi4gNlSo+kx9KZKrh9gIuAc/GXmH8H+FgI0dgNdnYY6TR7PhuKG7j+000U19vJiA7k+TOGMSip/WXSS5pKuHHpjWTXZ5MWksYzU5+hV3gvAHxNTZTfcSfNCxeiDQsj4amnCJo4oVN217m93JRTzI+1TZg1Gh7qlcg5XVCgqLbUyi/v7KCuzEZAqIEjzutH6sCOFWySSCRdSwcE6O797JY5oJIuJ6/KymfrSvhqQxl1NjcmvYa1d03vkWJzD3sX6Xl09aN8nP0xOkXHxMSJHJd5HFOTp2LUdl9KiupwYP1lIc2LF5Pw+GMoej2+5mZK/nsJwUcfTcgxM9HHxXV4/Gybg2+rG/muupE8+++9Oe/MiOfa1PZFOQlVMP+VLZTsrEf1+Z/RjQE6UgdF0mtkLGmDOtdHVCI5mHSkCNGlwH+ATOAj4B0hxJZutbITSKd5aNDs8vLAt9v5fH0pOo3CTUf14dLJGe0uUOT0OnlszWN8mfclZp2Ze8bew/GZxwMgVJW6N9+i5rnnQAiir72GyMsuQ+lE+KwQgg8r6rg3rxyHqjIzKoSn+6QQ2YFV3L3xeVTWzCtg48/FCAH9JyUw4dQsGUIjkfzDdKQK7uGA9KU9l8XZ1bywKI+NxY0ABBi0HD84gdNHJTE8JbzHrYIJIVhftZ6v87/G5rHx3LTnAMhryGNt5Vpmps8kwtS2yq4dur+qYl+7Dss332D98UdUux2AlHfe7pLw2j2iutbtZfDybaiAVoHxYUEcGx3GMVGhxBkPPCnQWGWnYFMNyf0iiE7x15uY++xGmhucpA+OIn1IFHEZobKNm+SQpCMC9Hv8FXC/EUJ4utm+TiOd5qHF91sruOOrrVgcHkanRfDkaYNJjWx/VPfc/Lk8vOphXD4Xp/U+jdtG39Y6i2pbuZKym27GV19P4JTJJD7+eKdatUBLgaIdRWyxOogx6Hi+b0preE1nqMhv5Jf3dtJU4yA40sSR5/cjsU/H+4hJJJLO0cE2LEcC/YQQLymKEgOECSFyu8fC7kH60p5FXbOrtZ/23I1lXD9nEyNTwzl9ZDKzBsf3yJ6dNfYa5ubP5ev8rymxlgAQbY5m3snzurVH597YN2yk7Oab8JZXAKANDyfkuOMIPeF4TAMHdlisF9hdfF3VwDfVjTzTN5mRof7nlgfzy8kIMHJMVOgBJ6aFENSX29i1oZpdG2uoL7cBMOSIZCae7o/mcju96I3aHjepIJG0l07ngPZ0pNM89KiwOLjl8y38ll+LWa/ljmP7cu6Y1HaXhM+pz+HGJTdSbC2mT3gfnpryFGmhaQB4Kispu/4GHJs2oU9IIPH55zAPGtQpu92qylO7K3mxuBoB/DcpirszEg5YROCA4zq9rPx6F9uW+nt2DZqWxLiTMtEbe2bVQonkcKYDIbi346+TEC+E6KUoShLwqRBiYrcZ2Q1IX/rP43D7mLelnI/XFFNUZ2flHUdg1GlxenyUNjjIign6p038S97f/j7PrH8Gn/ChU3RMSprEyVknMzFpYrfmdvqabTQvWULIrGNRFAVvTQ35Rx1N0OTJhJ54IkGTJqLoO3b/Cpebb6oa+bq6gc1WBwAa4OFeiVyUFN2usdxOL188to6GSv9qrKJAQq8w0odGkzE0muCIztWYkEh6GlKASnokQgg+Wl3MI9/vxO72MS4jkidmDyY5on2zpFa3lQdWPsBPhT9h1pm5d9y9HJdxnP8ebjfVTz9N/Xvvo+j1xNxxO+FnndXpmcWVjc1cvaOIMpeHPoEmXumfyoCgzvfYKsmuZ9H7O2mudxEabebIC/oRnxXW6XElEknb6YAA3QyMBNYIIYa17NsihDik+iFIX/rPUVRn44OVRXy2roQmp7/b3cjUcJ49Y2i7feLBotpezdaarRyZeiQAayrWcP/K+5ndezYnZJ5AlLn78hWFEDjWr6fxy69o+uknhN1O6scfETB8OACq3Y4moHO/twqXm+ErdrDnSXlUSCAnx4ZxfEwY0YYDC9q68mby11XTe3Qs4XH+1dIvn1iH3qglc3gMGUOjMQcfnJ6mEsk/gRSgkh5NSb2dW77YzKqCegINWu6c1Y+zR6e0SyQKIfg893MeX/M4btXNyVknc8eYOzDr/KKw6cefqLjrLlSbjZBZs4h/8AE0gZ0r5tzk9XFHbilfVjWgVxRuS4/jipQYtJ0Ut26Hl+Vf5LFjeQUoMPTIZMackIHOIFdDJZKDQQcE6CohxFhFUTbuJUA3CyGGdJ+VXY/0pf8MP2yt4MqPNyAEBBt1nDoiibPHpNA7tnM9qLsDIQRrKtcwJ2cOi4oXoVE0LDxtIeGmcIQQCAQapfvyFX2NjTR+8QWNn3+Bu8jfdlcbGUnoiScSfvbZGJISOzSuW1VZVGfli6p6bs+IJ6ul4v3VO4roE2jipNhwkk0HFouNVXby11eRt666Nbx25LFpjDnBX49M9akyn1Pyr0EKUEmPR1UFH6wq4rEfsnF4fEzIiuSRkwe1Ozc0pz6Hm5feTGFTIZmhmTw15SmywrMAcO3eTdl11+PKzcWQmUnSc89i7NWr07bPrWrgttxSLF4fY0MDebF/apsc1YEo2l7H4g+ysTW6CIsN4Ijz+xGf2f7KwRKJpH10QIB+DjwPvIB/JfRO/C3Lzu4mE7sF6UsPDhaHh3lbyjlrVAoajYLF7uGCd9Zw2sgkThqa2CNzO61uK9/u+pY5OXPYbfEXfU4OTua03qcxu/dsgg3dJ5b3rqLrLixk18xjQKslaPJkwmafStDkyR0KsRVCsLHJzmdVDXxb3UC9xwd0rHqts9nDty9soqbYCvjDaxP7hNNrZCwZQ6MxBfXcKsUSSXchBajkkKGw1satX2xhTWE9Jr2G66f35uKJ6ejbMWNo99h5eNXDfFfwHSatiVtH38rsXrNRFAXV4aDygQexzJ2LYjYTd++9hJ18UqftLne6uS67mGUNzQRrNTzSO4nZsZ2vTOiye/jti3yyV/hXQ4dMS2bMSRno5WqoRNJtdECAxgHvA1MBFVgGnCOEqO4eC7sH6Uu7l921Nt5dvpvP15did/t476LRTOndvjzCf4qfCn/i5qU3o1E0TE6azJl9zmRcwrhuXe301tTQ+OVXNH7xBcmvvYoxyz+Z3Dh3LoHjxneqX2eN28MpG/Nb26YEazUcHxPG7NgIxoYFojmA73bZPezaWENK/0iCwo0IIfjkgdWYgvT0GhlL5vAYAkJkeK3k340UoJJDClUVfLK2mMe+z8bq8tIvPoTHTx3E4KSwNo8hhGBu/lweXfMoDq+DI1OO5P5x9xNmCkMIgeWrr6h88CGEy0XoKacQd8/daMydy+FUheCt0loeLijHpQqOjw7j8T5JROg7P5tdtL2OJR9m09zgIiTazBHn9SWxt6yUK5F0B+0RoIqiaIGLhRBvKIoSAGiEEM3da2H3IH1p1yOEYMWuOt7+bTeLcqoRAsID9Jw9JoULxqURE9LzCs/4VB/LypYxN38uj016DJPOhEf18ObWNzkx80QSghK67d5CCOxr1tLw6SdYF/wCXi/odMQ/8ABhp57S4XGdPpWf6iyMDwsi2qBHCMHUtTkkGQ2cHh/OUZGhmA8w0e3zqBRtqyNnTSVFW+vweVXGnZzJ8KNTAfC6fTJVRiLZCylAJYckVU1O7vtmOz9ur0SjwH8mpHPjjN7tCk/abdnNbb/exs76ncQExPDIxEcYEz8GAGdOLmU33IC7oABjrywSn3sOY2Zmp+3Otjm4ekcx25odxBp0PNM3hSO7oF2Ly+FlxZf57PitHIBBUxIZe3Km7BsqkXQxHVgBXXc49A2VvrTrKWt0MOnxRagCesUEcdHEdE4amoi5BwoVq9vK3Py5fLzzY0qbSwF4aspTHJ129EG5v7e+nqLzzse9axcA+sREws44g7BTTkYX1bGCRtubHXxUXsdXVQ00en3ck5nAVSn+lVOXqmJsQ49wt8NfpT5vXRUuu79AVFCEkd6j4ug7Lq61wJBEItkXKUAlhzQ/ba/kvm+2U9nkJDHMzD3H9efoAbFtDm/1+Dy8uOlF3tn2DgoK/xn4H64eejV6rR7VZqPi/gdo+u47lIAA4u+/j9ATTui0zXvatbxUXI0KnJ8QyX2ZCQTqOv/QUbKjnkUf+ivlBkeYmHpOH1IGRHZ6XIlE4qcDAvRJYLUQ4otuNKvbkb6081gcHj5ZU8wRfWNaiwi9vDifwUmhTMyK6pG9HRucDbyx5Q2+yvsKu9eOVtEyPXU65/Q7h6HRQ7vVZmduLrroaHTh/iJGhafORhcTQ/hZZxI4cSKKtv0+06WqzKmo56OKutbWKSE6DSfFhHNBYlSbKtY31TowBeoxmHWoquD9O1fgcfnIGhFDnzFxxGeGorSzbZxE8m9DClDJIY/V6eHJn3L4YFURQsDk3tE8cMIA0qPaPvO4snwld/12FzWOGgZEDuCxSY+RFprmD8n98ksqH3rYH5J76inE3XVXp0u4A6xpbOaancUUOd2kmQ281C+1tXl1Z3A7vaz6ehdbW/qG9hkTx8TTeslCBxJJF9ABAVoDRAIOwAYogBBCdDxJ7R9A+tKOU9pg553lhXy6phib28dpI5J48rSeXQR5T3Efq9vK9M+no9fqOa33aZzR5wziAuO6775eL9ZFi2j48CPsa9YQde01RF95JQCq243G0P7cSSEEKqBVFLyqYMTK7VS5vYwLC+Sc+EhmRYcdMMTW7fSya0MN2SsrKM9rZOo5fRgwyV9Vt66smdAYMzp9z1u5lkh6KlKASg4btpZauOebbWwqacSg1XDJ5HSumpZFgKFtYagNzgbuXXEvS0qWYNKauHHkjZzZ50wURcGZk0PZ9Tfg3r0bQ2Ymic8+g6l3707bbPP6uH9XOR+U16EBrkmN5aa0WAxtCP05EBX5jSz+MJuGSjvmYD2TzuhN1oiYHjnLLpEcKnRAgA4FGv64XwhR1JV2dTfSl7afnEorry3dxbeby/GpglCznvPGpnL++FRigntmfufiksW8t/09Lhl8CZOTJgOwsXojfcL7EKDvvp6j3oYGGj//goZPP8FbXgGAadAgoi67lODp0zs0ZpPXx+eV9XxQXselSdGcneCPBlpc10Sq2UhGgPFvrxeqoDyvkeyVFeRvrMHr8lfCjU4JZsQxqWQOO6TmkCSSHoUUoJLDClUVfL6+hMd+yKbB7mkJy+3H0QPi2iS8hBB8mfclT6x9AofXwfiE8Tw4/kFiA2NRbTYqH3rYXyXXaCT2zjsJO/20LhF0C2ot3JhTQo3bS/9AEy/1T6V/G0KBDoTX42Pd94Vs/KkYVRWkDY5iylm9CQrveQ8/EsmhQDuLECnAdiFE/242q9uRvrT9nPTycjaVNJIUbua/E9M5fVRymydEDyYun4tv8r/hve3vUWwtBuD8/udzy6hbDsr9hRAUzDzG37tTrydk5kwizj0H85D2rxILIdhotfNBeR1zqxpwqAIFuDQ5mgey2tcH1Ovx8e5ty3HZvZhDDPQZHUvfcfFEJga12y6JRLIvUoBKDksa7W6e+jmHj1YXIwSMzYjgnuP6MyChbb0yS5pKuGv5XWys3kiIIYS7x97NMenH+MeeO9dfJdduJ/iYmcQ/+CDa4M73Oatze7k9t5TvahrRKwq3pMdxZXIMui7IJakttbL4g2yqi6zoTVrGnpjBwClJaGSeikTSLjqwAvoTcKYQ4k+roIcS0pf+PUIIluTWUN/s5tQRSQCsyK+l2uriuMHx6NrRLuxgIYTg7W1v8/6O96l31qNRNMxIncF/BvyHAVEDuu++qopt+XJAIWjSRAAaPv0Ub20d4Wecji66Y+1nhBDM3rSL5Y3+QtPRBh1nxUVwTkIkqea/X+30enzs3lTLzhXl9J+YSNYI/+rmjt/KCQgxkDwgAm0P/DeUSA5VpACVHNZsLbXw4LztrC1sQFHgtBFJ3HxUnzaVt/epPt7d/i4vbXoJr+rlmLRjuGvsXYQaQ3EV7KbsxhtxZWejT0oi8ZmnMQ8e3Gl7hRB8U93I7bmlNHp9DA8J4IV+KWQFdH7FUvWpbFlcyupvC/C6VWLSQph2bh+ikrqvSbhEcrjRAQH6GTAa+B5obcEihLi1G8zrNqQv3T+qKvhpeyUvLspnR0UTYQF6Vt5+ZI+sZLuHPfmdADctuYmlpUs5KeskLhhwAcnByd12X9XpxDL3G+rff99fYb53b9K/mdupKKJChwuHT6VfS8TQ7bml5NmcXJAYxcyokAOms9SUWNn5Wzm5a3+vYjtgciJTz+7TYZskEsmB+ccEqKIovYHtwDQhxG977T8ZeARIF0KY9tq/BDACLmCrEOKattxHOk2JEIIft1XyyA87Kal3EGDQcsWUTC6ZnIGpDUUDcupzuOO3O8hryCPKHMW9Y+9lWso0VJeL6scfp+HjT0CnI/q6a4m8+GKULsjfrHR5uDG7mEX1VswahbsyE7goMeqADbDbQlOtg18/zaVoWx2KRmHo9GRGHZeOvgc/MEkkPYUOCND79rdfCPFA11nV/Uhfui9en8q8LRW8tDif/Gr/vMK0PtFcOS2LUWkR/7B1+6ekqYS3tr2FRtFw77h7AShvLseoNRJp7r5q6d66Oho+/oSGjz/G19AAikLQkUcQcf75BIwa1W4BqgrBonorb5XWsLjeyrSIYD4Z4m+T5hMCbVvSbVTB109voGKXBQCDWUfv0bH0Gx9PdEqwrJUgkXQz/6QA/QCIB+7/gwCNxF8pcJsQImuv/UuAc4UQpe25j3Sakj24vD7eW1HIiwvzsbq8xIeauGFGb04ZlnjA8Ci3z80rm17hne3voAqVWRmzuH3U7YSZwmhasICKu+9BtVgIGDuWhMcfRx/b+eIEQgg+rqjn3vwybD6VcWGBPNc35YChRG0dO399Ncs+y8PR5CYkysSUs2TLFonkQLRXgB4uSF+6Lx+sKuKeudsAmDkgjquPyGJgYttSPA42eQ15vLn1TX4s/BFVqCQEJvDtyd9i1Hbel7SFyv89QsMHH6CYzYSdcgoRF5yPISWl3eNYvT7mVNbzdmktBQ4XAP0CTVyUFMW58ZF/KxqFEFTusmAw61pzOH9+azt2i4t+ExLIHBaNTk7CSiQHjX9EgCqKMgaYDUQDb+4tQPc6J/8PAnQxoAfcwMNCiEVtuZd0mpI/Utfs4tlfcvlkTQk+VZAZHcgtR/dpU6GibbXbuPu3u9ll2UWkKZJ7xt3DkSlH4qmspPyWW7GvXYs2LIz4R/5H8BFHdIm9xQ4XN2SXsLyxmQCthnszEzg/IbJLVkNddg8rv97F9mXlAGSNiGHiab0IDDs4DyYSyaFGB1ZAn9jffhmCe2jh9qr8sK2C4wcnoNEo2FxeHvxuBxdNTKdPXM9MY8ipz+HlTS+zuGQxAElBSVw06CJOzDwRg7b97Uzain3DBmwrVxJ91VUAuEtKaJr/PeFnnoE2LKzD4165o4ivqhrQAMdGh3JxUjRjQwP/1m87bR5yVlWyfVkZDZV2eo2M4aj/DgTA51NlXqdE8g/xTwnQb4H/AE/TdgEaJYSoVRQlGfgFGCmEsP7F+JcClwKkpKSMKCo6pKrdSw4SBTXNPLMgl3lb/CXfhySFcuvMvkzIivrb69w+N69tfo23t72NT/g4Jv0Y7hh9B2H6EOr+701qXnwRfD7Czz6LmFtvRWPqgvxNIXivvI4H88txqCqTwoN4pm8KyaaueYio2GVh6cfZ1JXZ0Ju0jDkhg0FTEtFI5yyR7EMnQ3BNwHHAOiHEf7rcuG7k3ypA3V6VL9aX8vLifMoaHbx27ghmDuy+PphdyU+FP3Hz0pvJCsvi4kEXMzNtJjpN91ThFapK85Kl1L35Jo4NGwDImD8PY2Zmx8YTgt8amsmxO/lvkr8o0VqLjZ9qLVyYGEXSAXxfeV4j25eVsWtDDT6viqJAyoBIBk5JJG3Q3/t4iUTS/Rx0AaooyixghBDiQUVR3qWNAvQPxz4DHhVCbDzQ/f6tTlPSdraVWXjipxx+za0BYEJWJDfO6M2I1L/P49leu527l99NfmM+4cZwbh19K7PSZ+HcvJmym2/BU1qKsVcWCU8+ialv3y6xtdDh4vqdxayy2AjSarg/K5Fz4iO6JF/F51PZsrCUNfP8RYqikoOYenZfYtNDusByieTwoLMhuIqiBANfCCGO7sQYEcD7QCiwCbhW/MFpK4oyE9gjfu8XQvykKEoasBHY3LL/SSHE/Lbc89/mS/8oPLUahVOGJXLNEb1Iiey+fpidYUPVBj7a+REPTXiIAH0AqlBZXracCYkT0CjdM5ko3G4s87+n/u23cOXlA2AeOYLI//6XoClT2u2bXKrK3KpGXi+pZofNiUFRWD++P9EGfbvG+e6FTRTvqCcwzEj/CfH0m5BAcIRsPyaR9BT+CQF6F3Ak/lDaQUAJcMYfm3LvLUBbeqkFCyGaWpz3GmCCEKL+QPf7tzlNScdZuauOJ37KZmNxIwATs6K4bnqvvy0o4fa5+b+t/8ebW9/Eq3qZkDCBe8bdQ5wIofLBh2j67jsUvZ7o668n4j8XdkmBIlUI3i6r5X+7ynGoginhwTzVN7nLVkOt9U6Wzcll9+ZaUGDgpETGnJiBKbB9DwASyeFIFwhQBdghhOjXiTEew99f9ANFUd4GPhNC/LjXcS1+oTm5ZddSYDiQjH/Sd3p77/lv8qVOj4+jn/uVojo7Wo3CqcMTuWpaFqmRgf+0aftlbeVaXtv8Gmsq1wDw8ISHOTHrxINyb/v69RSdcy4AQUceSeR/LyZg2LB2j1Pn9vJ+eS3vlNVS7fZXoz0iIpjLk2OYFB70l0JWCEHV7ia2/VqGOdjAhFP96xYV+Y247F5SBkTISB6JpAfyj7Zh2bMCCgQB0S3OdBL+WdvxwArgFeA7YBXgwJ8H+owQYk5b7vFvcpqSziOEYHFONc//ksfmUn91vHEZkVw3vRdjM/66QE9+Qz73r7yfzTWbMevMXDX0Ks7pdw62H36i8oEHUZuaCBg9moTHH0MfH98lthbYXdyQXcxqi41ArYZ7ujA3FGD3llqWfZqLtd6JKUjPuJMz6TcuHkX2DpX8i+lkDqgGGAE0CCFO6YQNK4FjhRANLZXjRwsh7tjreB/gCSHEiS3b3wC34q8ivxbIBnYDNwgh6tpyz8Pdl/pUQbPLS6jZP9F26xebEYIev+L5yqZXWF25GoDB0YO5YsgVTEiY0G1VXH1WKw2ffErY7FPRRUQghKD21VcJOeoojFn7DVprE3OrGrh8RxEmjcJpcRFckhRN78C/XrF0O7zkrqlk26/l1JX5qxCHxwVw1r1jpI+SSA4BZB9QiWQ/CCFYmlvD8wvzWldEx6RHcPURWUzMitqvc1eFymc5n/HchueweWz0j+zPfePuo5c7nPLb78C+ejWa4GDi7r+P0FmzusTO31dDK3CoKhPCgnimb3KXVMoF8Lh9rP+hkI0LilG9griMECaf2YfolJ5ZdEMi6W46mQPqBfKBr4QQnk7YkAP0FUIIRVGmAWcKIS7b6/h44FIhxIUt2+8BrwEbAIMQwtpSK2GMEOLiv7nPYV9PQVUF32+r4Llf8ugXH8KLZ/lX7/buldkTsXvsTP98OlaPlSHRQ7hy6JWMix/XbTZ76+upf/99Gj76GNVqJfLyy4i5/voOj7ehycb7ZXU82jsJs1aDVxX8X2kNp8dFEGn4+zzVjT8Xs3b+bjwuHwAp/SMYMDmRtEGRcrVTIjlEkAJUIvkbhBAsy6vl+YV5rC9qAGBAQgiXTcnk2IFx+23fUmmr5JHVj7C4ZDEaRcOZfc7kqiFX4vn4K2qefRbh8RBy3HHE3XM32tCuKdtf5HBxY0ulXLNGw12Z8V3WNxSgscrOr3NyKdlRj6LAwMn+sFxjgAzLlfy7OFhtWFrCaJfv59B8YBYwUwjRqCjKSfiF5N4roH2Bx4QQJ7VszwVuE0Lk7HVOALBSCDGkLfYcbr5UCMGi7Gqe/CmH7Ep/PcNZg+N54cxhaHvoCtrWmq0ADIoeBMDc/LlEm6MZnzC+24Snp7qa+rffoWHOHITDgSYwkPCzzybigvPRRbWvmI8qBL/UNfFKcTWrLDYAXuiXwulxf19vwedVaay2E5ngb5+yfVkZq+YW0G98PAMmJxAa3TNXqCUSyV8jBahE0gaEEKwsqOP1pQUsbSlWlBRu5pJJGZw+MhnzH/qHCSFYWLyQx9Y8RpW9iihzFDePvJkjXBlU3Horrrx8dDExxP/vYYImTeoSG1Uh+KC8jgd3lWPzqYwODeTpPsn0+pswpvYghKBgYw2/fZ5Hc4MLc7CesSfJsFzJv4sOrIB+CVyyp2ZBS6/rV4UQp3fChseBzUKIjxVFeQP/iuofc0A3AHu+XJbhzwENEkJYWs45Dji/rXYcTr50U0kjD83b0TqpOL1fDDfM6M2AhJ7ZxzOnPoeXNr3EkpIlDI4azIfHfnhQVmeFEOyaORNPUTHasDAiLjif8HPOQRvSvsJ0HlUwt7qBl4qrybE5ARgVEsiVKdEcHRX6lxOl1non25eVseO3cjRaDef/bxwarQafR0Ug0Oll306J5FBFClCJpJ3sKG/ijV938d2WCnyqIDxAz3ljUzlnbCqxIfuKPbvHzmubX+ODHR/gFV5Gx43mjmG3EPzed9S//Q4IQdgZZxB76y1oArumwEWJ080t2SUsabBiUBRuSovjypQY9F0kEj0uH+u+L2TTL8WoPkFMajCTzuxNXHrPfHiTSLqSDgjQjUKIYX/Yt0EIMbwTNkTir4IbAmwBrhFCqIqiPAf8TwhRoyjKscA9LZc8JIT4vmW19F7ACjjxC+PittzzcPKlC3dWcfF76xifGcnNR/dheEr4P23Sfim0FPLKplf4sfBHBILe4b25auhVTEue1n0rnuXleKqqWgsJNX71Nb6GesLPPLPDPsri8TJi5Q6afSozo0K4KiWWUaH7H0sIQVlOA1uXlLF7cw1CgM6goffoOMadnCmL4UkkhwlSgEokHaS0wc5bv+3m0zUlODw+dBqFYwbFc+H4NIanhO3zgJDXkMfDqx5mQ/UGdBod5/c/n3N9o7Dc9SCekhL0SUkkPPoIAaNGdYltQgg+q2zgvvwyGr0+BgSZeKZvCkOCuy5UqbHKzm+f51G0zV/DpO/4eMadlElASPc1OJdI/mk6IEC3AkOFEL6WbT2wSQgxoLts7A4OZV+aX93Mq0t2cf8J/Qk26RFCsKG4kRGpPVN4AnyW8xmPrH4En/CRFpLGVUOv4qi0o7qtnYqnrIza19+g8euvMSQkkPH9fBRtx1YYGzxe3iqtZWxYIBPD/fUCvqluoG+gmT4HiMjZsriUZXNyAQiNMTNoShJ9x8XJdA+J5DBDClCJpJNY7B4+W1fCeysLKW1wADA4KZQLx6cxa3A8Rp3fiQshmFcwj6fWPUW9s55oczTXDbyCUV/swPLxp6AoRFxwAdHXX4fG1DVhszVuD3fmlvFdTSMa4PLkGG5Jj8PchYUaCrfUsuzzPJpqHBhMWkYdl86gaUloZTEIyWFIBwTok0A68FzLruuBIiHETV1vXfdxKPrSSouT537J5fP1pfhUwT3H9efiien/tFl/idPrxKTzf/fnN+RzzaJruHTwpRyfeTw6zd8X5ukonvJyv/D86ivweNDFxhJ56SWEn346ir59oq/K5eG1kmreL6/D5lOZFhHMJ0My//aaxio7uzZWM/zoVBRFwd7kZslH2QycnEhyvwiZ3iGRHKZIASqRdBE+VbBwZxXvrihkxS7/qmBEoIFThydy5ugUMqP9BRSa3E28uulVPs3+FK/wMjhqMNcajyHioTfxVlVhSE0l/pH/ETBiRJfZ9n1NI7fnllLt9pJmNvBE72QmR3RdJVuvx8emX0pY/0MhXrdKeFwAE2b3InXgX7eukUgORTogQPXAHcBxLbvm4S8Q5O4O+7qLQ8mXWuweXl26i3eW78blVUkMM3PDjN6cPCyxRxYYanY3896O9/gk+xM+OfYTkkOSAfCpPrSa7stztMyfT/ntd+wjPMNOOw2NoX1RLEUOFy8XVzOnsh6XKgjTabk4KYqLk6KJ0P9ZOAtVULyjni2LSyne7veVJ980jIRePXdFWiKRdC1SgEok3UBOpZX3VhbyzcYybG5/qfjR6RGcPTqFmQPjMOm1FDQW8MTaJ1he7i90eXzKMZy12Ifus+9BUQg/71xirr8eTUDXhM1aPF4e3FXORxX1AJwWF879mYkHLHnfHpobnKz8ehe5a6oASBkQycTTsgiP65kN3CWS9nKwquD2NA4VX6qqgiOfWcruWhvhAXqumpbFuWNTMfXAgjUun4s52XP4v63/R6OrkSB9EI9MfIRpKdO67Z4+i6W1+rqnooKi884n4sILCTttNhpjx9p3/XfbbubVWIgx6Lg8OYbzEyIJ0v359+12esleWcmWxSVYqv3RQpGJgQyelkyv0bHoDT3v30gikXQPUoBKJN2IzeXlu83lfLKmmM2lFgBCzXpOHpbIqcOTGJAQzLKyZTyx9gmKrcWYdWbOCT6CaS+uRl9SiT45mfiHHyZwzOgus2llYzO35JSQb3cRoddyf1Yip8WGd2lRi4pdFn77LJfqIisajcKgqUmMOi5N5vFIDnmkAO15+FRBjdVFXKg/fPWDVUVUNzm5ZHIGIaae952jCpV5BfN4aeNLVNgqMGqNnN33bC4aeBFhprBuuae3ro66/3uThk8/JfXDDzEP9KcgC1VF0bQvXWJ7s4MNTTbOS4hq3V5nsXFGXASmv0m9qK+w8ckDq1EUSB8SzeBpSST0DjsoFX0lEknPQgpQieQgsb3cwqdrSpi7sQyrywtAr5ggThmexKzBMSwq/5I3tryB1WMlyhTJucUpjHl7LVoB4WefRfSNN6INCuoSW1yqygtFVbxQVI1HCCaFB/FE72TSAzo2A74/hCrIWV3Jyq93YW9yYwrSM/q4dAZMSpDNwiWHLFKA9hyEEPyaV8uj3+8EYP61k3pkiO0f8ak+Zn83m92W3ZyUdRKXD7mcuMC47rmXxULd2+9Q/8EHCLsdbXg48Q89SPD06e0ea0OTjeeLqviptgm9orBybD+STPsP1xVCUJFvYfOiEsaemNEaBbN9WRnJ/SMIiTR36nNJJJJDGylAJZKDjMPt4+cdlXy5oYzf8mpQBSgKTMiMYsbAMIp83/J1wcd4VS/phnjO/t7O4LV16OPiiLv3XoKP6LrwrFybk1tySlhtsWHUKFyXGstVKTEY2zkj/ne4nV7W/1jE5l9K8Hl/zw9NGRAhZ74lhxxSgPYMtpVZeOyHbH7LrwXg6AGxPHHqEEJ7aJTF9trtVNorOTLlSAC21GwhyBBERmhGt9xPeL3U/d//Uff2O6hWK5qQECIvuojwc89FG9S+lIg1jc08U1jFkgYrAH0DTVyfGsvxMWFo//Ad7vOq5K+vZvPCEmqK/eePPDaNMSd0z+eUSCSHJlKASiT/IFVNTr7ZVMaX68vIqfI7a4NWw+iMIFzmlex0zkHRuhniiuG0ORX0LhMEHzOTuDvvRBcd3SU2qELwcUU9D+8qp9HrIyvAyGO9k1rL53cVTbUOVs7dRf66agCS+0cw4dQsIhO7ZlVXIjkYSAH6z1JpcfL4j9l8vbEMgOEpYdx5bD9GpkX8w5btnxJrCS9seIEfC38k3BjOD6f+QKC++3PihRAUn3c+zh07iLjwQiIuvABtSEi7x/m13srpm3cBMDjYzI2pcRwVFYLmj8LTp7L5lxK2LC7F1ugCIKlvOEOOTCZ1QKSsZiuRSPZBClCJpAcghGBHRRPfbi5n/paK1nYuBh2EhJXQbFyGLjCH0VVaTv++iXRXCLG33UroKad02SpijdvDg7vK+byyAYDZseHcl5VAtKFrVxQqCyz89nkeVbubUBToNzGB0celExjadeG/Ekl30VYBqihKDbA/R6oAQggR0+XGdSM9xZcW1DRz1LO/khwRwG0z+3D0gLgeGUlhcVl4fcvrfJL9iT+aJTSd64dfz7Tkad1ir/B6sXzzDWpzMxEXXACAu7AQTWgouvC2V5cVQrCisZnhIYGYtRp8QnD1jiJOjYvgyIjgP9kuhEBRFIQQzHl4LQ1VNvqMjmPIkclyclEikfwlUoBKJD0MIQSbSy3M21zO/K0VVFicACiKD01APrrgHYyr3MF5vzaQ1WsMcffdhzGj63rbLW+wcntuKXl2F6E6LXdkxHNeQuSfQq06gxCC/HXVrPg6n+Z6FzqjluFHpTB0egp6o6yEKOm5tEOApv7dcSFEUddZ1f38U77U41P5aFURE3tFkxXjFzSrCuoYkRqOvofmkq+vWs81i67B6rYSaYrkqmFXcXLWyd3Sy1MIgXXBAmqeex53QQGaoCB6/bq03dXThRD81tDMU4WVrLbYeCgrkUuS/zrKprLAwsYFxcSmhTD8aP+fek2xlcAwIwEh7WvjIpFI/n1IASqR9GBUVbCxpJEftlawYEcVRfX21mNaUzG9mrdz9tZcZh57HFGXXYrGZOqS+7pVlVeLa3i2qBKnKhgcbOaxXkkMD+3a0DGv28eWxaWs/6EQt9NHQKiBMcdn0HdcnCxUJOmRyBDcg4MQgp+2V/H4j9nsrrVxzMA4Xj2363ojdzV7npkURcHqtjL729mckHUCFw64sNtCbm2r11D99NM4t2wBIOTYY4i65hqM6W2fkPyj8AQYGxrI7RnxjA3bdwVTqILdW2rZ+HMxlQX+qu6JvcM46cbhXfSJJBLJvwUpQCWSQwQhBLlVzfy0vZwvNuZSXPu7QDOKJsY0FXPC1FHMOH5SlxXiKHK4uDe/jJ9qmwA4Oz6COzMSiOrC3qEAjmY36+YXsm1pGaoqiEgIZNzJmaQOjOyR4XWSfy/tFaCKoiQDTwBDgNYZIiHEIVWV5WD60o3FDTzy/U7WFvrTAU4elshNR/UmKbxreiJ3NZtrNvPE2ie4fPDlTEqaBIDH50Gv7b6CSE0//0zZtdcBEDhhAtE33oB5wIB2jSGE4Lytu/mlzv/9PjY0kJvT45gQFvSn793cNZWsmbe7tX9nfFYow2akkDYoSuZ3SiSSdiMFqERyiFJc38Szyxbw845SbNZkUP05lBqhMiwxhIn94pmQFcXQ5DAMus6tJi6otXB3XhlFTjehOi23Z8RzfheH5QI0VttZNbeAXRv8hYoSeoUx7pRM4tJDu/Q+EklH6YAAXQB8CtwMXARcAewSQjzQTSZ2CwfLlz6zIJcXFuYBMDYjgrtn9WdgYs/8/1/eXM5zG57jh90/AHBGnzO4e+zd3XY/T3k5uthYFK0W1e2m/KabCD/7bALHjWvXOFavj2CdP9Xh8YIKVjY271d47snvBFj3fSFrvisgY1g0Q2ekyO9kiUTSKaQAlUgOcTw+D1/lfsuLy76lqjEWn603qiu+9bhZr2VUegTjMyMZnxnJgITQDvXKc/pUXimp5oWiKpyqYGCQmYd7Jf4pTKsrqCywsOKrfCry/WFeGcOi9+klJ5H8U3RAgG4UQgxTFGWrEGKQoigaYKUQYkw3mtnldKcv9fpUdC0h98vza7n3m23ceWw/jugb0yMjIJrdzby17S3e3/4+btVNemg6N4+8mUmJk7rFXl9jI7Wvv0HDhx8S9+CDhJ18UofGWd3YzBO7K/EIwTfDslAUBa8q0CrsY3dzg5NNC0uoLmzi5JuGoygKLrsHp81DaHTPXIWWSCSHFlKASiSHCR7Vw/y8b3lt5XOUeFV89kxCa3vjVQZT5/q9sE+QUcfw1HBGpYYzKj2CoclhmPRtL/xT7HBxX345P9T6xeGJMWHck5nwlw3JO4oQgqKtdaycu4v6cpu/Yu74eEYdl0FQuKyYK/ln6IAAXS2EGKMoykrgDKAK2ClDcKHJ6eHt33bz+bpS5l87kbAA/3eITxUdmiQ7WNy7/F6+zv+aMGMYVw69ktm9Z6PXdH24repy0fDhR9S+/jpqUxPayEhib7+d0OOPa9c46y02nthdydKWPp4jQwL4aHAGofp9UynqK2xs/LmI3DVVqD6BMUDHaXeMIjTa3GWfSSKRSEAKUInksMOrevlh8+e8ufpFCsxWhIC0xlSGJP6HRncaawsbqLa6Ws/XaxUGJYYyIjWcocnhDE0JIyHUdMCZ/KX1Vu7JKyPX7sSsUbgqJZYrU2II6OLiQaoqyF1dyervCmiud6HVaxg8NYnhR6diCuqZTeclhy8dEKBPAo8Cs4BnABfwuRDihm4ysVvoSl/a7PLy3opC3vi1AIvDQ5BRx2vnjmBir6guGb87KLGWkByc7P+5qYQ5OXO4ZPAlhBq7JxTVMm8+1c88jbe8AsVsJvI//yHioovQBrU9CiTX5uSB/HIW1vtzPIcGB3BbehxT/9BOpbHKzsq5uyjYVAMCgsKNDJ2eQr8J8RhMXV+5VyKRSKQAlUgOU4QQLF7yLm+te5UtMf7CEUlqKOePvILhMTPZWupgXWE9awrrKaix7XNtdLCRoclhra8BCSGtqxN741EF75XX8uTuSixeH4lGPfdkJnBiTFiXh6J5PT62LS1j/Q9FOG0e9CYtQ6enMPTIZAxm+ZAkOTh0pgquoigpQIgQYlsXm9XtdIUvdXp8vLeikNd/LaDe5sas13LhhDQunZRBeGDPbN1Rai3lmfXPsKh4EV8c/wVZ4VkH5b6VDz5Iw6dzCJs9m6irr0If0/62sdusdqavy2VQkJlb0uOYERmy3+/lhkobHz+wmvDYAIYfnUqv0bFoZRVyiUTSjUgBKpEc5givlxWfPsd7OR+yKsOL0CgEKSZm9z+Ts/qeRUJQAnXNLjaVNO7zsjq9+4yTFG5mQEIIAxNCGZgYyoCEEKKDjSiKQp3byxO7K/igvA4Vf4jXA1mJjOjiti0AboeXTQtL2PRLMR6nD1OgnuFHpzJoaiI6g+whKule2tEH1CiEcCmKst+kOSGEfX/7eypd4Usdbh+Tn1xMk8PD+eNSuWxKJlFBPTOc3u6x8+bWN3lv+3u4VTdZYVk8NOEhBkYN7Jb7ufLzsS5aTNSllwDgravD19CAMavtgrfA7uKZwkpuy4gnuSUlYr3FxvCQgFbhKYSgcGsdG34s5Ijz+7Xm1VfuthCbGiIr2kokkoOCFKASyb8Eb0MDW176H59V/ciiQeAwKWhQODJ1Ouf0O4fhMcNbH1JUVVBQa2NTSSPbyixsK7Owo6IJu9u3z5jhAXr6xoXQJy6YvnHBGEONfGC1sMLqf7Y+MSaMOzPiSTV3/UOms9nDhp+K2LqkFK9HJSDUwMhj0ug/IQGtXs7eS7qHdgjQDUKI4YqiqIAAlL3fhRCH1GxJV/nSVQV1ZEQFEhPSNT2LuxpVqMwvmM+z65+lxlFDmDGMq4dezam9T0Wn6fpIC29NDTUvvUzj55+DqpL22RzMgwe3a4wSp5tnCiv5rLIen4CrUmK4JzNhn3OEKijYVMO6HwqpLWkGYNzJmQw/OrXLPotEIpG0FSlAJZJ/Gc7cXAqfepTvbav5YaSGygi/6OwT3ofT+5zOrIxZ+22c7lMFu2ttbC/3C9KdFVayK5uobXb/6dzIECM2s4ZmkwZNkJ7jM6K5bVAyKSFdX8zCZnGx/vtCtv9WjuoTBIUbGXFMGv3Gx6PtZPsZieSPdCYE91Dm3+JLfaqPs+afRW5DLmf1PYvLh1zeLXmeqsNB/bvvUvd/b6La7egS4om54QZCZs1C0bTte6vG7eG5wireL6/DIwSJRj03pcdxemwEupaVTKEK8tZXsf6HIurL/akWqQMjGXlsGnEZspWKRCL5Z5ACVCL5FyKEwLZsGRWPP84advP9aC1b0vzHAnQBzMqYxWm9T6NfZL8DjlXb7CKn0srOiiZyKq3kVlnJr27G9ofVUgCTUUvv6CAyogJJiwokPSqQtMhAUiICCAvQdypvtKnWwfofCtm5shKhCoIjTIw8No0+4+JkPpOky+hAEaKBwG4hhK1lOxBIE0Js7y4bu4PD2ZfWOerIachhfMJ4ALLrs9Fr9GSGZXbL/bx1dew+dTbeyko0QUFEXnYpEeedh8bUvlXho9blsMXqINqg4/rUWM5NiMT4B/EqhOCzR9ZSW9JM+pAoRh6bRkxqSFd+HIlEImk3UoBKJP9ihMdDw+efU/vCi5QpjSwcY2bJUB0WxV+0aFDUIGb3ns3RaUfvd1X0L8cVgsomJ/nVzWyrbOLbglp2VFnB5kFxqfu9JtioIykigJQIM8nhAaREBpAYZiah5RVi0rVJoFpqHKz7oZCcVX4hGhJlYsQxafQZK4WopPN0QICuB8YKITwt2wZgxaG2ino4+lKP6uGTnZ/w6uZXAZh38jwizZHddj/h86Fo/ZHXJZdfgT4hgairr0IXEdGm620+H/l2F0OC/WnFP9VayLU5uSgpisCWcVWfSu5afxuV/hP8YbiVBRb0Ri2RiV3fs1kikUg6ghSgEokEn9VK3VtvUf/ue7g9TtaNCWfREZFs9hUCYNaZmZE6g5OyTmJE7Ag0SvuFXJnTzVOFlcwpqUU4fKSqGiYaTGjsXkrqHRTX26mwOFD/4qsnyKgjIcxEQpiZ+FATsSGm1ve4UBPxIWZCzL+L1MZqO+u+LyR3dSVCQHCkieFHp9JvXLzMEZV0mA4I0E1CiKEH2tfTOdx86crylTy25jEKLAWYdWYuGXQJ5w84H6O26/PV3YWFVD31FIaUVGJvvQUAoaptDrV1qyofVdTzbGElAlg1tl+r4NyDqgry1laxdv5uLNUOzMF6Lnhkgvyuk0gkPRIpQCUSSSueqmpqX36Zxi+/BJ+PquGprJzdh588m6lz1gGQGJTIiVknckLmCSQGJbb7Htk2B48WVPBTrb833cSwIO7MiGd4aCAen0p5o6NVkJY3OihvdFDW6KDc4qCi0Yn3rxQqYNRpiA42EhNsbHk3EaQo2HZbsRZYMfsgKtjI+CNTGTE5Eb1Rtm+RtI8OCNC1wBlCiIKW7UzgMyHEiO6ysTs4XHxpnaOO/63+HwuKFgBwTPox3DTiJmIDY7v8Xj6LhdpXXqX+44/B48E0eDBpn3zcugp6IFQhmFvdyOMFFRQ53RgUhfMSIrk1PY5Qvf+7S1UF+euqWDu/kMYqOyjQa2QsI49NIyK+66uQSyQSSVcgBahEIvkTroICap59DusC/0Oabugg8i+axk/6HJaWLsWr+lu0DIsZxrHpx3JU2lFEmNoWRraH1Y3NPLyrgrVN/sIYR0WGcFtGPAOC/rpQkU8V1FhdVDY5qbQ4qLQ4qWxyUdXkpNLipNrqpNrq+lMLmf2hFRBm0hMTbiIyyEh4oIGIAD3hgQbCAwyEBegJNesJCzAQZtYTFqAn2KRHK9sU/KvpgAA9Dvg/YH7LrmOBS4QQ8//6qp7H4eJL7R47x399PKGmUO4YfQej4kZ1+T2E10vDZ59R+8KL+Bob0UZHEXP99YSedFKbxWe2zcFVO4rY3uxEAU6LC+fmtDhS/lBRfMviUpbNyfULzxExjDw2nYgEKTwlEknPRgpQiUTylzg2baLmhRewrVgJgHnkCPRX/YfFoRXMK5jH9jp/HRWtomVcwjiOTT+WI1KOaHO+qBCCBXVNPL67gu3NTgBOiAnjlrQ4egV2vE2D0+Ojxuqi2uqixuqkptlNXbOLumY3lQ0OikubqLG6sCNwKvgbY7SRYKOOELOeYJP/PcSkJ8SsI8SkJ8ioI9ikI8ikI8jo3xdo1BFo1BJk1BFo9O836jSdKrgk+efoSBVcRVF6A9NbNn8WQuR3vWXdy6HsS5eWLEWv1bcWGSq0FJIUnNQtbVUAGufOpeL2O1AMBiIu+g+R/70EbVD7RGGDx8uYVTsYHxbEbenx9GuZmBNCUJ7XSEKvMBRFwe30suzTXIYelUJkgszxlEgkhwZSgEokkgNiX7uW6uefx7FuPQCB48cRdc01VGeE88PuH5i/ez5FTUUAGLVGJiZOZHrqdKYkTSHYEHzA8VUh+K6mkSd3V5Jvd6EBZseFc1NaXLf0EAVobnCxaWExW5eV0ez24TFqiBsSSdSAcByKwOLw0Gj30OjwYLF7aHS4abR7sDq9NDk9NDk8f5mveiB0GgWzQUugQUeAUUuAQUuAQdfyrsWs12E2aAgw6DDp9+zTYtJrMOm1ra+99xl1Lcd0Wox6jRS53YRsw3LoUGIt4fE1j7O0dClJQUl8d/J33SY6XQW7MaQko+h0CK+X6qefIeLcc9Anti1NIdfm5NGCCi5KjGJShP87s9rlIcaobz2nLKeBVd8UUFlg4ZjLB5ExNLpbPotEIpF0N1KASiSSNiGEwLZiBTUvvIBz8xagRYhecQXmkSPZUb+D+QXz+bnwZ6rsVQDoNf5Vh+mp05mWPO2A/fS8quCr6gae2l1JsdONVoHZsRFcnxpLekD3CFFns4ctS0rZsrgEl82LRqvQd2wcQ2ekEB7316sWQghsbh9NDg8Wh4dml5dmpxery4vV6fH/7PTS7PJic3mxub00u3z+n1u2HW4fNpcPh+fPLWu6AoNOg1GraRGkWv+2ToNBp8GgbXnfz8/6PS+dglGrQde6T/n9WMvPutZ9CjrN79s6jf9dq1H2OabT+PfpNMqftjWHQHhzB0Jwk4EngCFA67K+ECKjG8zrNg4lX+r0Onlr21u8vfVt3KqbAZEDuGvMXQyKHtTl9/I1NlLz0ss0fPIJcXffRfhZZ7Xr+vKW4myfVtSjArNjw3mpf+o+51QVNrH6m12U7GwAILFPGONPyZLtVCQSySGLFKASiaRdCCFoXrqU2pdfwbl1KwABI0cSdeUVBIwbh0CwrXYbC4oWsKBoAWXNZYA/THd47HCmJk1lavJUUkJS/vIeblVlTmU9zxdVUer0oFXglNhwrk+NJTOg46G5f4fb6WXHb+Vs+qUEW6MLgLTBUQybkUJ8Vmi3riaqqsDh8bWKUofHh93t8//s9mH3+HC6fTi9/m2nR8Xh8eFsebm8Ki6vf//e726v6j/mUXH7VFwt5/5dIad/EkXxrw77BakGjQK6FhGrVfz79xare+/zb+P/ee/9LT/3jQvm1pl9u8DGdgvQBcCnwM3ARcAVwC4hxAOdNuYgcqj40i01W7j111spay4j1BjKdcOv45SsU9Bq2pZ72VaE10vDnDn+PE+LBV1MDLF33knIzKPbdH2jx8uLxdW8VVqDUxUkmfTclh7PKbHhaFu+a1RV8POb29i1oQaAmLQQxp6UQXLf9uXbSyQSSU9DClCJRNIhhBDYfltO7auv4tiwAQDzkCFEXnE5QVOmoCgKQgiy67NZULSAhcULKbAUtF6fHprO1KSpTEmewpDoIfsNjXOrKp9XNvBcURUlTjca4OQWIdqZHNG/w+fx99Hb9Esx9eX+Akmx6SEMnZ5CxrDoQ2KV7kD4VIHH5xenbq9fnLq9Kh7f79ueffb7z/eqKh6v8B/3qXh9Ao/a8u7zn+f1+QXunuNeVeDd6xxVCDw+ga9lv98WgSoE3r/Y71NbXkLgaxnTJwSqKtospsdmRPDppeM6/bvrgADdKIQYpijKViHEIEVRNMBKIcSYThtzEDlUfGmlrZKTvjmJY9OP5dph1xJmCuvye9hWrKDq0Udx5eWjGI1EXnwRkf/9L5qAgDaPceWOIr6qaiBCr+WG1DjOT4zE2NKWRQjROuG14J3t1JY0M+aEDNKHRMmweolEclggBahEIukUQgjsa9ZS+8or2FevBsDYuzeRF19EyLHHouh/z2EqaSphSekSlpYsZX3VerzCX602WB/M2ISxjE8Yz4SECcQHxe9zD48q+LyqnucKqyh2ulGAY6NDuSYllqEhbX/oa+/nKtpWx6ZfiinLaQQgJMrE4COS6Tc+HoNJtnDpKah7xKn6u2BVVfbZp9UoRAV1Poy7AwJ0tRBijKIoK4EzgCpgpwzB7RqcXifvbHuHKclT6B/ZH4AGZwPhpvBuu2fVY49T/+67BB8zk9ibb25TnqdPCDY32Rke6g/rz7U5+aqqgatSYgjW+Vdn7U1u1v1QiNfl44jz+wHgcnjRG7WHxcSXRCKR7EEKUIlE0mXYN2yg7v/epHnxYgB08fFEXngBYbNnowncN5+yyd3E8rLlLC1dysryldQ761uPpYemMyFhAuMSxjE8ZjhBBn91R48q+LKqnpeKq8m3+8Nkp4YHc21qLOPCArttdaC6qIlNC4rJ31CDUAUGk5Z+ExIYPC2JkKi/bhsjOfzogAB9EngUmAU8A7iAz4UQN3STid1CT/Slv5b+yqOrH6W0uZSJiRN5dfqr3XIfX7MN608/EXbqKf7tpiZcubkEjDzwn4EQgkX1Vh7eVU6e3cmy0f3+lM/ucnjZtKCYTQtL8Lp8BIYZOfv+MXKSSyKRHLZIASqRSLocV14edW+/g2XePPB40ISGEn7WmYSffTb6mJg/na8Klez6bJaXLWd5+XI2V29uXR3VKloGRA5gVNwoRsePZljMMAxaEz/UWHihuIotVgcAI0MCuCY1lhmRIWi6SYha651sXVLKjt/Kcdm9KAqkD4lm8BFJrW0RJIc3bRWgiqJMEUIsVRTFKIRwtexLAUKEENu63dAupif50rLmMh5f8ziLS/wTXaf2OpXrhl/X5aueQlWxfPMt1c88ja+mluT/e4OgSZPafP1mq52H8sv5rbEZgGOjQrkvK6G1srfX7WPrkjLW/1SIy+bFGKBj2FEpDJ6WjN7YtTmrEolE0pOQAlQikXQbnspK6t97n8Y5c1DtdtDrCT32GMLPOx/zwAF/eV2zu5k1lWtaX3kNea3HdBodAyMHMjx2OMNihmHTZ/FWhZMVLQ95WQFGLkuOZnZsBGatpns+l8tHzqoKNi8qpbHKDkBkYhCDpibSe3ScfHg8jGmHAF0vhBihKMoGIcTwg2Fbd9JTfOl3u77jwZUP4vQ56RfRj7vH3s3g6MFdfh/Hli1UPvw/nFu2gE5HxLnnEnXlFWhDDlx5tsnr4/bcUr6q8letHR0ayD2ZCYwK3TcKZPfmGr5/dSs6vYbBRyYzbEYKpkD9/oaUSCSSwwopQCUSSbfja2qi8bPPqP/oY7wVFQCYR4wg4vzzCT7yCBTd34ea1TnqWFu1lrUVa1lTuYbCpsJ9jmeFZZEcNogi0ljnSsCniyXSoOeixCguTIwi0tA9oWxCFRTvrGfLohKKt/tDiA1mHX3HxTFoShJhsd2Tnyr552iHAN0KvAtcjz8Edx+EEK90uXHdSE/xpdtrt3PZL5dx1dCrOL336V1f3dbjoeLe+7B8/TUAgZMmEXvH7Rgz2p6y6xOCI9fm4FEFd2fGMzPq/9m76/i6q/vx469z3XJv3JMmTd0dKFbcBmxjyGDCGINt3405G7IhQ+fKhmw/NjYY7i6ltBSoUaFuadz15vq95/fHvU3TUknaJDdN3s/H4z4+/rnvT2+S0/c9Fh9FW8c02z9upHhyOhabCa01q17fxYTj8nB6BmaaKSGEGIokARVCDBodidD51lu0/PuR7pFzzfn5pF52GakXfR5TRkav7tPkb2J1w2pWNaxiVf0qNrVsIqr3zKVpNqXgN5cStJShrGM4v3gO3yoZzfgBGjkXoK3BxyfvVbNpaS1BX7z5cNHENKacVMioaRkYB6g2VgyuPiSgxwFfJj7w0Av7HNZa66sGIr6BkqyytL6rnj99/Ceum3kdOc4cAHxhHw7zwH25U/Xd6whs2UzOz36Ga8GCQzatD8Zi/L+qJlJMRq7Ij/8NqwyEyLWYMRvio4FXbmjhw+d30FjRyTEXlDLn3NIBi18IIYY6SUCFEEnhX7eOln8/Qsdrr0E4jDKbSTnrLNK+eBn2WbP61J/SF/axpnENaxvXsq5pHeua1u01qBFA1JhJqmssJ+RO5fzCmUzJnDwgI2WGQ1G2Lqtn3aIqmirjzYIdHguTjs9n4vw8GbToKHcYgxD9WGv9m4GMaTAMdlkajoV5dOOj3Lf6PnwRH1dOvpIfzfnRgLyXd8n7BDdvIuPrXwcg0tqKwenEYLEc9DqtNS80tnHn9loqAiEKrGY+OnYSph4j1tbtaOfD57ZTvaUNgFFTMzj2wtFkFqYMyLMIIcTRQBJQIURSRRobaXv6aVoff6K7ea517FhSv3gZngsuwOhy9fmeWmuqvFWsa1zHx41rWVK3lur2LSTGgumW7chhQvp4xqWNY3xafFnsLt7vnKSHE0P9zg7Wv1fN1pUNRMMxUFA8KZ3JJxYwaqrUih6N+pKAqvi3KOu11pMGOKwBN5hl6bLaZdz10V1sb9+Ow+Tg2zO+zeUTL8ds6N/+kaGqaurvuRvvW2+D0ciYt9/CnJvbq2uXt3dx67ZqVnb4UMBleelcX5pLnnVP0vr2vzeyaWn8b1reGA/HfraM/DGp/foMQghxgMtXCAABAABJREFUNEpaAqqUGgesB07RWi/psf9zwF1Aqdbats81ZmAD8C+t9R29eR9JQIU4OuhoFO+iRbQ+9j+6liwBrVE2G+6zziL1CxdhnzPniEaZjUQjvFi9kX/tXM765g0YQuWYQ7tQOrDXeVajlbLUMsakjmG0ZzRjUsdQllpGvisfgzq8hDHQFWbLsjrWL66hpaYLAIfbwvhjcpkwP4/0POch7iCGisOoAX0duExr3TqAYQ24wSpLf7P8N/xrw78AOKf0HH40+0fdTW/7SywYpPmhh2h+4EF0MIh1wgRyb76pV9OqADxT38q3N+wC4KQ0F7eMKWCyK96yIRbT3XN2rnp9F1uW1XPsZ0czakqGjJIthBAJyUxAHwHygFv3SUAzgC7gE631mH2u+Q5wFvCRJKBCDF+hykranniCtueeI9rYBIBl1Cg8F12E57MX7ncql76oCoR4uLqJR2saaQvUYwpVUmyoJU/V4fPtpLKzEs3efwPtJjulnlJK3CXxlye+HOUe1ev+aFpr6nZ0sGFxNdtWNRAJxQDIHe1mwnF5jJ2Tg8Uuc/8NZYeRgD4BzANeAby792utrx+A8AbMYJWlb+96mz9//GduPOZG5uXN6/f7h6ur2XXl1whXVmJwu8n6/vdIu/RSlPHggxl1RaM4DAaUUnRGonx13U7+rzibU9NTUErh6wix8tVy6ss7uOgns1EGRTQaw6AUyiCJpxBC9JSUBFQpdQzwBSALeKhnAtrjnG09E1CllAt4HHgSKJQEVIjhT4fDeBcvpu2pp/EuWgTRKBiNOE84Hs8FF5By6qkY7IffpzIYi/FSQxv/rmnmo/Z4zWSaycjnsx2c6OwkHKxke9t2trdtZ1vbNqq91Z9KTAGyHdmMco+iKKWo+1WcUkxRShEuy/6bEIf8EbatbGDj0lrqdrQDYDIbGD0riwnH5FEwIa27JkUMHYeRgN6yv/1a69v6L6qBNxBlaTgW5qXtL/Hklid56MyHcJgdaK2J6mi/NIPfHx2NUn7ZF7FNGE/WD36AKT39oOdHteax2hbu2VHL7ycUcUamZ6/jQX+E1W9WsPrtSiLBKA63hc//ZDaeLOnrLYQQB5KsBPQF4GvAb+l9AnobsAQo4BAJqFLqGuAagOLi4tm7du3q5ycQQgy2cEMDHS+8QNvTzxDauRMAg9NJypln4rnwAhzz5qEMh9+ncqPXz79rmnmyrgVvNF4zOdft5PL8dC7ISsVpMhKIBKjorKC8vZzyjnLK28vZ1bGLnR076Qx17ve+qdZU8l35FLgKKHAVdK/nO/PJdebisrhoreti0we1bPqgDl9HCACnx8LYebmMPyaXzMK+94MVA6OvCehw0Z8JaCQW4ZWdr/D3NX+nsrMSgzLwp1P+xMlFJ/fL/XuKBQI0P/AAxowM0q+4Ir4vFDrkAEMAS1o7+cXWajZ0BTArxc/L8rimKN76IhKO8smiala+uotAVxiL3cSss4qZdkqRzAMshBCHMOgJqFLqPGC21vp2pdTD9CIBVUrlAA9qrS9QSl2J1IAKMWJprQmsW0f78y/Q8corRFvjXetMubm4zz0X9znnYJsy+bD7W3VFojzf0Majtc2s6PAB4DQa+Fx2GpfnpTPT7djvvduD7VR0VFDZWUlFZ3xZ2VlJtbeaRl/jfmtOAVLMKeQ4c8hz5pHjyMHW5SZcacG/3YDN58YZ8pCXG68VHTMnm5T0gZtKRhzaYdSAZgO/A4q11icppaYB87XWfx+wIAdAf5Sl0ViUV8tf5e9r/s6ujl0YlIFzS8/l2mnXUuIp6Z9Ae+h8ZyH1d95JuLoac2EhZa+9esg5hwF2+YPctq2GV5riLRPOzfTw87J8Sh175upsrevisduXYTAqpp9ayMwzR2Fz9u8gSUIIMVwlIwG9CTgNCAFTgUrgUq31rn3O65mAng78EmgnXgNqBX6ktX7xUO8nCagQw5cOh/EuWUL7Cy/gffsddChee2guLsZ99tm4zz0H6/jxh52Mbury81hNC0/Wt9ASjs8zOtZh5eLcdC7KSaPAduhaFIBQNERtVy3V3ur4q7OaOl8dtd5a6rrqaPA1ENGRA15vilpwhjw4wm7SLRkUZeVRVlxEXmoumfZMMuwZpNvSSbOl9ftIoWJvh5GAPg+8Cnxbaz1NKWUBVmitpw1YkAOgP8rSzlAnZz19Ft6Ql7NLz+ab07/JaM/ofopwj1BVFfV33oV34UJQitSLLybrB9/HlNa7aZe+ub6c5xramOyycduYAk5IS0FrzY7VjWTku0jNiff53vxRHYXj03CmWg9xRyGEED0ldRqW3TWggAvI0lo/opQ6EbgFmA8sBe7TWj/T45orkRpQIcQ+op2deN95h45XXsW7dCmEwwBYSktxn3M2KaefjnXixMNKRoOxGK83dfBYbTOLWjqJAQo4PtXFxbnpnJflwWU6/GZ30ViUJn8TtV211PvqafQ10uBroN5XT4OvgTpvfBnaZxqZ/fFYPaTb0rtfqdZU0mxppFnTupcem4dUayqp1lQcpv3X6Ir9O4wEdIXWeo5S6mOt9czEvtVa6xlHEEM68G/AA6wGrtP7FNpKqX8C5wAva62v7rH/RuA8IAhcpbUu78179ldZ+vautxnlHsWYtDGHPvkwdH34IZXXfhMdDGKbPJncW36BfdrBc32tNeu8fqalxBPLCn+Qd1o6+XJ+BkalqNrcygfPbqehvIOyWdmcfc2UAYldCCFGCpkHVAgx7ETb2+l86y06XnmVrg8/jA9eBJgLCkg5/TRSTj8d+6xZhxz5cn/qg2GerW/lyfoW1nvjU7jYDQbOynTzuZw0TklPwXIEfVEPRGtNV7iLHVVVrF27lU1bd9Lga8Rv7iRg8aJTA4RdProMnbQEmwnHwr26r8lgwmOJJ6Qeqwe3xY3b6t57aXGTYknZ8zLHlw6z47CnpjlaHUYC+qHW+tjdCahSKhVYpLWefgQx3EN8ftFHEonmE1rr1/Y5pwAYC3xpdwKqlJoA/EVrfbpS6iTitbKX9eY9j5ayNNbVRfnlV5B22aWkXnLJIX/HN3j93LS1io/aunhr7ngmufYMHtRY0cmHz22nYkMLEJ/D99jPlpFVnDKgzyCEEMOdJKBCiGEt0tqK95136HzzLbqWLu1upmtMT8d1ygJSTjkF53HHYXD2fS7O9V4/T9a18Ex9Kw2heBNaj8nIeVkePpedxvw0F8YBql3UWtNU5WXbyga2r2ygvdEPgDIoCsZ7yJ/hwjPWiN/spS3QRkughbZgG62BVtqCbbQH2z+1PFgz4P1RKJxmJy6LC5fZ9al1h8mBy+LCaXLiMDtwmV04zA4cJsdeS7vJjsPkwGwc+s2HDyMBvR4oI9715A7g28B/tdZ/PIIYPgDO1Vq3JubOnqe1vmE/5y1g7wT0WsChtf59Ynuj1npib95zqJalkcZG6u+5F/e555By2mkA6FjskAOSdUSi/HpnLf+sbiKq4bhUJ78aV8RYZ7yP9Ucv7mDFy+UAZJe4Oe5zZRSO710TXiGEEAcnCagQYsSIervoWrKYzjfexLtoEbGu+NQrymzGMW8ergULcC04GUtRUd/uqzUftHl5tr6VlxvbaYvEa1yzLSY+k5XKZ7JSOSbVOaDJaHO1l+2rGtm2soG2+vjgSSjIG+2hdHoWo2dm4sk68HylWmv8ET8doQ7ag+10hDrir2AHnaFOOsOdeENeOkLx7Y5QB13hLjpDnXSFu/CGvH1OYPdlUibsZnt3Qmo3xddtJtveS2N8aTVasZls2Iw2rKYe64n9VqO1+5jT5CTVlnpE8cHhjYKrlLoCuIB4y+0XtNb/OcIYNgMTtNZaKXUKcJnW+tr9nLeAvRPQG4EarfXDie0tWutxB3mfITuivI5GaX3sfzT+4Q/EvF6cJ55I8YMPHPo6rXmqvpXbt9fQGIqQZzVzS1k+F2anEotpjMZ44rrrk2aWPLmVYy8czeiZWdJMXQgh+pEkoEKIESkWCuFbvhzvu4vwvvsu4crK7mOWsjJcJxyP8/jjccyZg8Fx4MRtX6FYjHdbOnm2vpXXmjrwx+JTumSaTZyb5eEzWanMT3VhGqA5PrXWtNR0sf3jRnauaaSp0tt9LD3fyegZWZRMyyS7OAXVjzForQlGg3jDXrwhL12RLnxhH13hrr1e/og/vj9x3Bfx4Qv78Ef8e718YR+BaKDf4puVPYt/nfOvI77PYdSAnqq1fudQ+/ZznRF4fz+HXibeh/NsrXWbUuqzwDG9rAH9JmDTWv8hsX1U1oD6131C3a23Eli/HmWzkfl/3ybjq19F9WJqlajWnLtyC+u9fq4tyuaHo3IwRzSr36xg4we1XHbzPKwOM1prdExjMI6sJuZCCDEYJAEVQox4WmtCO3fiXfgu3kWL8K1c2d1vVJnN2OfMxnV8PCG1jh/f6/lGfdEYC1s6eKmhjTeaO+hKzC+abjZyRoaHszPdnJzuxjGA/8ntaPKzc00TO9c0UrO1jd1/2u1uCyVTMiiZmknhxDQstkNPTzHYYjpGIBIgEA0QiATwR/wEIgF8ER/BaJBgJIg/Gt8XjAbxR/zd+wPR+L7d26WeUq6bdd0Rx3QYCegqrfWsQ+3rYwz3Amu01o8qpR4Antm3D2jivAXsnYBOBP6gtT5LKTWf+OBFR1Uf0LZnnqX2pptAa1ynnELuzTdhLig46DWdkSjrOv3MT4vPp7vR68eoFGVWC+sX17DilZ34O+NzeZ77zakUSFNbIYQYUJKACiHEPqJeL76PPsK7ZAld7y8lXFHRfcyYmopj3jwcx8zDeeyxWEaP7lXzvEA0xnutnbzY2MbrTe10ROLJqM2gOCkthbMzPZyR6SbLMnD9IP3eELvWNVO+romKDS2EA/Ek22BSFIxLY9TkDEZNycCTbZcmhwfQ2wRUKTUGGAf8CeiZ+XqAW7TWE44ghgzio+C6gbXAd7XWMaXUH4A7tdaNSqk7iI+CmwtsBC7UWncppX4OnE18KrSva6139OY9h0pZGq6vp/Lqq8n6/ve7+3weiNaaFxrbuGVrDd5olPePmUiO1YyOabatbODD57fT0RTAYFJMW1DI7LNLsLmGfj9kIYQ42kkCKoQQhxCqqKDr/ffpWrqUrmXLibW3dx8zZmXinDsPx9w52GfPxjpmzCFrSEOxGB+2dfFaUzuvN7VTHYyPWKuA2W4Hp2W4OT3DzRTXwCWC0UiMmq1tlK9ronxtEx1Ne5q7ujNtFE/OoHhyBoXj0zBbD3+KmeGmDwnoV4ErgTlAz0KoA3hAa/3ywEQ4MJJVloYqKqi7806yrrsO++TJQDyxPNTvxQ5fkBu3VPFuaydGBVcXZPGT0lxcJiOhQIT//PwD/N4wE47JZe75pbgz7Ae9nxBCiP4jCagQQvSBjsUIbtpE10fL8H30Eb7ly7sHMwIweDw4Zs7EMWc29lmzsU2ZjOEgfdO01nzi9SeS0Q4+8fq7j+VYTJya4ea0dDcnpafgPoK5Rg/6TFrTVu9j1yfNVGxooWZLG9FEDa3BpMgr81A0MZ2iielkFqVgGKD+q0eDw2iCe+XuQX+OZoNdlsaCQZofeojmBx5EB4OkXnopebfdesjrAtEYf6qo5y+7GghpzRy3g3vHF5HdEiESipI3JhWA8rVNpGTYyChwDeyDCCGE+BRJQIUQ4gjoSITAxo34VqzEt3IF/pWriLa2dh9XZjO2SZOwz5iOfcYM7NOnY8rLO2ANTm0wxDvNnbzV3MGi1k58iX6jRgWz3U5OTkthQXoK01McAzaQUTgYpXpLKxUbWqj4pLl7ihcAq9NE4fg0iiamUzghHXembUQ11z3MUXDLiE/F0t3RVmv9Sn/HNpAGsyz1Ll5M3R13EN5VgcHtJvtHPyL14i/0qu91VyTKScs24YvG+HlZPueY7Cx/cSdbl9eTlufkspvnysBCQgiRZJKACiFEP9o9oJFvRTwZ9a9eTWif6StMWVnYpk/DPmUKtslTsE2ZjCnt0wOfBGMxlrV18VZzBwtbOtni29NM1mMyckKai5PTUjg+zcVou3XAEsGOJj+VG1uo3NhK1eYWgl17pltxpVspHJdGwfj4KyXdNiAxDBWHUQN6F/AN4v0wo4ndWmt96kDEN1AGqyxt/NOfabrvPgA8n/882T/+Eab09INeUxMIURsMM9sTn8v34w4fWRHY8UYV6xdXE4tqUnMcMqWKEEIMEZKACiHEAIu0thJYuxb/mjX4V6/Gv2btXs12AcyFhdimTME2aRK2iROxTZyAKTNzr3NqAiHea+1kUUsn77V6aQ7vSQTzrGaOT3UxP83FCakuiu3WAXmWWEzTVNlJ5cYWqje3UrutnUg41n3cnWmjYHwa+WNTyR+TSkrG8KohPYwEdBswS2vdMYBhDbiBLEt1LNZduxnYsIHam39Ozs034Zh18IGCIzHNQ1WN/Lq8jlSTkfeOmYDTaGTHx4289fAGwsEoDo+FeZ8pZeL8PKn5FEKIIUISUCGEGGQ6FiNUXk7gk0/wf/IJgU/WE9i4Ee3373WeMSsT24SJ2CZMwDphPLZx47CUlKDMZmJas97r571WL++3dvJRe1f3NC8ARTYLx6Y6Odbj4phUJ2UDVEMajcSoL++genMr1ZtbqdvR0d1/FMCVZo0no2NTyRuTSlqOo1/nHx1sh5GALtZanziQMQ2GgShLo94u2p58ktb//pdR/3q4ezqV3gwytKzNy8+2VLGhK4DTaOAnJblcXZiFyaBob/Tz9K9WMP20IqadWoTZIoNoCSHEUCIJqBBCDAE6EiG4YwfBjRsJbNxEYONGAps27TXiLgBmM9bSUqzjxmEdOxbruLFYy8ogL5+1viDvt3pZ0tbJ8vYuArE9f8czzSaOSSSkcz1OJrvsmAcgEYyEozSUd1CztY2arW3U7uggEox2H7c6TeSO9pBXFn9lj3JjOooShMNIQH8FFAFPAt1tqEdyH9BIUxMtj/yH1sceI9bRAWYz+Xffjecz5x3y2uZQhF9ur+F/dS0AnJ/l4ateC+UvV3DON6d2DyoUDccwmqXGUwghhiJJQIUQYojSWhOprY0no5s3E9y6leCWrYTKyyEa3etcZbViKS3FWlaGpWw0xtJSdmbn81FKGksDEZa3d9Ee2XON3aCYnuJgjsfJHLeT2R7HgMxBGo3GaKrwUr013ly3bkc7AW+4+7jBqMgsSiG31E3OaDc5JZ4hPbDRYSSgC/eze0T2AY16u2j49a9pf/ZZdCiEwekk9dJLSf/qVzDn5PTqHrXBECd8tIkss4kfWN3wag1NlV5QcOIlY5l2StERxSiEEGLgSQIqhBBHmVgwSGjHjnhCunUrwe07CG3fTqiyEmKxvU9WClNeLpaSUroKCinPzuWT1EyWOFP52JVK2Lwn6Sy2WZjpdjAzxcEMt4OpKXacxv6tndRa097gp3Z7G3Xb26nd3k5rnW+vc+wpZnJK3OSUesguSSF7lBubs/+T48NxOKPgDgf9UZbqWIwd555H1Osl/StfIe2ySzG63Ye8bmV7F3ajgUmu+Fydb25uoO3Vaho2xUebLpmWybEXjpYpVYQQ4ighCagQQgwTsWCQUPkuQju2x5PS8vL4a+fOTw16BIDBQDg7m5bsPMozs9ngSacyPYvazCxqM7LpdKUwwWVnutvBVJed6SkOJrrsOPp5MJdAV5iG8g7qyzuo29FB/c52gr7IXue4s+zkjEohu8RN9qgUMotSsNhMB7jjwOltAqqUmnSw41rrDf0X1cDrr7I0uGMn5oJ8DNZDD5LVGApz945aHq1tYWaKg5dnj0UBT/9qJfU7O8gd7eG4z5eRn5jbUwghxNHhQGXp4JfqQgghjojBasU2fhy28eP22q+1JtrURKi8nGB5OeGKSkIVFYQqKzDsqiCnro4c4Jh97he02qhJz6QuI4uG9AweSc+iMT0Da34+2aOKGF1UwCSPi8kuO2nmwy82bE4zxZMzKJ6c0R1ve4Of+p3t1O/qpHFXB42VXrY2+tm6oiF+kYLUbAdZxSlkFaWQVewisyhlyNSUAi8f5JgGRg9WIEOJdXTpIc8JxmI8VNXEH8rr6IzGKLaY+bLRgSHRLPuEi8fi6whROj1zyDbVFkII0XeSgAohxDChlMKUlYUpKwvH3Ll7HdNaE21tJbRrF+GqasLV1YSrqwhVVWGuqqa0tpbS2qr93jdqMNDsSeWttAy86ZmonBwceXlkFORTUFRAcVEBtpxsDLa+zQ2qlCI1x0FqjoPxx+bF3ysao6Wmi4byDhp2ddJU2UlTtZe2eh9bl9d3X5uSYSOz0EVGoYuswhQyCl1J6VOqtT50piU+ZWmrlx9sqmBXIITDYOBLISulLzTQpZvwj83G7rKQO9qT7DCFEEIMAElAhRBiBFBKYUpPx5SeDjNnfuq4jkaJNDYSrqklXFNDuLaGQFU1bVVVBGvr8DQ2kL1jK+zYuvd1wK7EesDlIpKRiTE7G2d2Np7cHMxZmZgy40mxKTMDU0YGBo/ngImi0WiI13QWpTA5MalJNBKjta6LxopOGiu8NFZ00FTdxc41Texc09R9rcVmZNTUTM78+uT++CcTA8htMlAdDHGmtjLrzWbMTSEsNiMzzxp1VI2WLIQQou8kARVCCIEyGjHn5mLOzYVZexLUwh7nxPx+wrV1VFdWUbWrgpaaOnz19ejGRuzNTaS3tZJRWYlpVzkxoPVAb2YyYcrIwJiRjikjM76eno4pPQ1jWjrG9DRM6ekY09MxpqVhcDrJLEwhszCFifPjt9AxTXujn6YqL01VnTRXeWmq8hINxw70riJJYlrzXmsn/6lp5pYxBRTZLOR1xLh+kR9jbQtGk4EppxUx+5xR2F2WZIcrhBBigEkCKoQQolcMdjvW0aWMHl36qY6NwViM7b4gmzp97Kipo6G2nra6ekJNjaS2tZHeEX+ldbST1tlBRmcHroZNGHozEJ7ZjDHVgyk1DWNqKsa0xNLjIS3VQ2ZqKsbxHozzPKgMGSF1qGgJR/hfbQuP1DSx0x8CoMBq4baxBbiz7HgCMYqOz2PueaWkpPet+bYQQoijlySgQgghjpjVEJ8+Y5LLDnkZQLwZbCgWY4c/yNauIFt9AT7wBdnmC7LdF6ArFMbj7SS9sx1PZwep3g7SvJ0U+7vI93vJ6vLi6fLi6OzA3NFOdOdOiEQOGod91ixKHv3vIDyxOBBfNMb1myt5sbGNYExjBI4Nm5i6zsvlrnjzWrPFyOW3HYtZmtsKIcSIIwmoEEKIAWMxGJjgtDPBad9rv9aapnAkkYwG2ekPUu4PstEX5BV/CP++85zGL6IoEmJCJMDocICigJ/coJ8sv49UnxdXlxdbQcEgPZk4ELtBsa6tC1cEFlREmLC2E7dfYzAqGos7GT83F0CSTyGEGKEkARVCCDHolFJkWcxkWcwcl7p3s1mtNfWhCDv9QXb5g+zyh6gIhKjwh9gVCPJmyAp2D7g/fd+T0lw8MUjPIPYvFtWc92IT9rYIRqBgfBpj5+YwekbWUJo+RwghRJJIAiqEEGJIUUqRazWTa/10cgrQFY1SHQhTGQjt/fKHKLDJIDbJZjQZOO3kYsxWE2NmZ+Nwy2cihBBiD0lAhRBCHFWcRiPjnEbGOWXgmqFq9tklyQ5BCCHEEGVIdgBCCCGEEEIIIUYGSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKSUCFEEIIIYQQQgwKpbVOdgz9QinVCOzqp9tlAk39dK+jxUh8ZhiZzy3PPHKMxOfur2cepbXO6of7HFX6sSwdiT97MDKfW5555BiJzy3PfGT2W5YOmwS0PymlVmit5yQ7jsE0Ep8ZRuZzyzOPHCPxuUfiMw9FI/VzGInPLc88cozE55ZnHhjSBFcIIYQQQgghxKCQBFQIIYQQQgghxKCQBHT/Hkh2AEkwEp8ZRuZzyzOPHCPxuUfiMw9FI/VzGInPLc88cozE55ZnHgDSB1QIIYQQQgghxKCQGlAhhBBCCCGEEINCElAhhBBCCCGEEINCElAhhBBCCCGEEINCElAhhBBCCCGEEINCEtB9KKWuVEotVUq9r5Salex4BoJS6nWlVKNS6ubEtlJK/VkptVgp9ZJSKj3ZMfY3pdTMxGf6nlLqHaXUaKWUTSn138Rz/1cpZUt2nP1NKeVO/Dy/q5RappQ6bSR83gBKqXFKqbBS6oQR8ln7E5/zu0qpr4+gz3m2UuoNpdRCpdSvRspzD2UjoRwFKUtHSlkq5ejIKUdhZJalyShHJQHtQSmVBlwHLAC+BPwpqQENnK8DP+mxfRbg0FqfCDwBXJ+UqAZWLXC21vok4DfAbcCVwKbEc29ObA83XuAkrfUC4DLgHkbG5w3wc2BRYv1Khv9nXa21XpB4/YMR8DkrpSzEf6Yv0lqforW+nhHw3EPZCCpHQcrSkVKWSjkadyXD+3PebUSVpckqRyUB3ds8YLHWOqS13gmkKKWsyQ6qv2mtq/bZdTLwUmL9xcT2sKK1rtNadyY2g0CEkfHcMa11JLHpBtYyAp5bKXUMUAfs/lkf9s8M5CqlFimlnlFKlTAynvk44v85fDRRG3MiI+O5h7IRUY6ClKWMkLJUytERVY7CyCtLk1KOSgK6twygtcd2GzDsqtr3o+dztwFpyQtlYCmlnMAdwK/59HMPy89aKVWglFoCvAE8y8j4vG8i/o3ebiPhsy7RWp8M3A/8g5HxOecD04ErgC8DDwKZDP/nHspGajkKI+N3Dhh5ZamUo8AI+JwTRlpZmpRyVBLQvbUAqT22PYl9w13P5/aw938ehg2llBl4HLhXa72BTz/3sPystdbVWusTiNdM/IVh/nkrpc4DVmitm3vsHvaftda6KbF8HRjFMP+cE1qApVrrDq11NdAEGBn+zz2UjdRyFEbG79yILEulHAVGwOcMI7IsTUo5Kgno3j4CTlBKmZVSxYBXax1MdlCDYBFwbmL9XPa09x82lFIG4D/Ac1rr5xK7R8Jz92z61gF0MvyfewawQCn1GnAG8X5KGxnGz6yUcimljIn1acQLkOH+OUP8b/Y4pZRJKZUCZANPM/yfeygbqeUojIDfuZFYlko5OjLKURixZWlSylGlte7vex7VlFJXAVcDGvie1npFkkPqd0qpB4H5gBX4BPg88GdgGvE/rl/Z51uvo55S6gvAw8Duz3Md8U7V/wQKifdx+JrWOpCUAAeIUmo28HsgCpiAW4GFDPPPezel1MPAQ8BKhvFnrZSaR7y5UCfxv13XEf8ZH/afs1Lqy8C1gJl4c7HnGQHPPZSNhHIUpCxN7Br2ZamUoyOjHIWRW5YmoxyVBFQIIYQQQgghxKCQJrhCCCGEEEIIIQaFJKBCCCGEEEIIIQaFJKBCCCGEEEIIIQaFJKBCCCGEEEIIIQaFJKBCCCGEEEIIIQaFJKBCCCGEEEIIIQaFJKBC9BOl1EdKqdVKqQ1KqUhifbVS6v8ppW5XSl06gO99pVKqTSm19DCunaOU+u8Rvn+JUuqafrjHaqVUSCk15UjuJYQQ4ugj5aiUo2JkkHlAhehnSqkSYIXWOnMQ3/NK4DNa6y8M1nvu8/4LgN9oref0w73KiT/LJ0d6LyGEEEcfKUeP+F7lSDkqhjCpARViECilHlZKfSexfqtS6n9KqVeUUtuUUo8rpWYqpd5RSm1XSv26x3V5SqmnlFLLlFLrlFI39uE9tVLqJqXUcqXUDqXUaUqpu5VSHyulPlFKTUyct0AptSKxXqKUalJK3Zk4b7NS6oR9z9vP9l+BSYlvXp9KHB+vlHo18f5rlFJfS+x3KKWeTHzDvUYp9cSR/esKIYQY7qQclXJUDB+mZAcgxAg1G5gDeIFVwD3AOcR/J3cqpR7QWm8F/g38Umv9nlLKArytlFqutX6zl+/TprWeq5S6GHgeuExrfYNS6nrgJuBL+7kmA/hAa32TUuoK4F7g+EO8z//R45tbpZQJeBS4Qmu9SSmVAqxQSn0ATATcWutJiXPTevksQgghxG5Sjko5Ko5SkoAKkRyva63bAZRSa4E1WusgEFRKbQbKlFI1wAIgSym1+7oU4gVPbwvOxxPLVYDWWr+U2F4JfP4A13h7nPch8NtevldP4xJx/q9H7NbEvjXARKXUX4F3gZcP4/5CCCFGNilHpRwVRylJQIVIjkCP9eh+tk3Em8hrYK7WOnyE7xMFgvt5j/050HkR9m62bzvI+yqgSWs9Y78HlZoMnEb82+q7lFJTtdaB/Z0rhBBC7IeUo1KOiqOU9AEVYojSWncCi4Gf7d6nlCpSSuUmKaQdwGilVJqKfx37xR7HOgBPj+3NgE8p9eXdO5RSE5RSbqVUIRDVWj8H/ADIAtIHPHohhBAjipSjQgxNkoAKMbRdQXxQgnVKqXXEmwKlJiMQrXUN8WZEK4GlQG2Pw2uBzYlBGZ7SWkeA84HLlFJrlVLrgfsACzAV+EAptQZYBtyduLcQQgjR36QcFWKIkWlYhBgGVJKHj+9PSoaPF0IIMcikHBVi8EgNqBDDgx+Yow5jAu2hIjF0/WrADBxuXx0hhBDicEg5KsQgkRpQIYQQQgghhBCDQmpAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMCklAhRBCCCGEEEIMClOyA+gvmZmZuqSkJNlhCCGEGAZWrlzZpLXOGsz3VEr5gY8Sm49orf/R49h84H5gLDBGa12V2P8wMB1oBxq11hcn9p8N3JK4/Fat9eu9iUHKUiGEEP3lQGXpsElAS0pKWLFiRbLDEEIIMQwopXYl4W2rtdYLDnBsPXAc8NJ+jn1Xa71k94ZSygj8CjgpsWuRUuotrXX0UAFIWSqEEKK/HKgslSa4QgghxNCQq5RapJR6RilV0vOA1rpda+09wHW/U0otVkpdmtgeA+zUWrdprduA8sS+/VJKXaOUWqGUWtHY2HjkTyGEEEIcxLCpARVCCCGOciVa6yal1FnAP4DTenHNjxPXpANvK6WWAxlAa49z2oD0A91Aa/0A8ADAnDlz9OEGL4QQQvSG1IAKIYQQQ4DWuimxfB0Y1cdrWoA3ifcHbQFSe5zmSewTQgghkk4SUCGEECLJlFKuRN9NlFLTgKZeXpeaWFqA44EtwFagVCnlVkq5gVJg20DELYQQQvSVNMEVQgghkm8ScL9SqhPQwLVKqRnAGVrrXyulxgH3Ea/hfEwp9ajW+m/A40opF2AG/qO1Xg+glLoB2D3y7Q29GYBICCGEGAySgAohhBBJprVeBszcz6HVieNbgNP3c91ZB7jfK8Ar/RiiEEII0S+kCa4QQgghhBBCiEEhCagQQgghhBBCiEEhCeh+aC2j0AshhBBCCCFEf5M+oD14W4MsfnwL2SUpzD67JNnhCCGEEEIIMSxorYnGNJFYfBnVmmj009vRxHkxrYlE48vdx2M9ztWa7vPi5xBfj2limsQ5u+8VP6b1nvWYjscU2+v4nmO7z4/vS2wnnmP3uZr4fva6HjSJ493XxPfF3xNAE4v1OK/HOXRv97x2zzaJerI975G4bq9zE2d0n7ungm13Ndvuc+lxbLc/XTaTNKelv38EukkC2oPBqKja1ELFxhbGH5OHK82a7JCEEEIIIYQ4IK3jSVwwEiMQjhKMxAiGowTCMYKRKKFIjGAktmcZjRIMxwhHd2/HCEc0oWiUcFQTisSPxV86cTxGJKa790eimnBME+lej8WTy2j8nGhsz3J3ghmJSQvDo0U4GhvQ+0sC2oPDbWHuZ0p5/6ltfPjcdk7/2qRkhySEEEIIIY5yWmt8oShdwQjeYISuYBRvMIIvFMEXivZYxs/xh6P4Q1H84fi+QI/tQCK5DCTW/eEoycztTAaFyagwGQzdS7PRgN1ixGw0YDQoTAa1zzKx36gwqPh+g2HP0qj2XjcYFEYDe9YTS4OK7zeo3esKg2LPMaVQCowGhSKx7HGeUnv2GxLnKpU4xp5z9l7uuW73NYbENmpPLPH98Z1Ksdf5KrEP2Gt7r/XEMfbaVj32f/o+7HPOXsd3f2CJ/Xvee891u8/z2M39+0OyjwFNQJVSVwLXEK/h/a7WelWPY/OB+4GxwBitdVVi/5NAAWAE/qa1fnggY9zX1AWFrF9cw+aP6phycgG5oz2D+fZCCCGEEGKICUdjtPvDtPnCtPtDdPgjdATCtPvDdPjDdAQidPjDdAYidAYjdAbCeAMROgOJhDMUQfdTkmgzG7CZjdjNRtx2M1ZTfNtmNmA1Gbu3rSYDFlN8X3xp2GtpMSaWpnjC2HOf2WjAbFSJpQGTQWExxZdmkwFzd7KpuhMXIXprwBJQpVQacB1wLPGE8hHghB6nrAeOA17a59IbtdZblVI24BOl1P+01oGBinNfRpOBEy4ey0t/WcPix7fwhZ/OQRnkF0sIIYQQYjiIxjStvhDN3hDN3iDNXSFafSFaukK0doW6t1u7womkM0RXKNrn97EYDaTYTKQ7LRSnO3BZTTitRpxWE06LCafVhMtqxG6J73dYTDgtRuyW+LrdHF+3W4w4EutWk0ESPnHUG8ga0HnAYq11CNiplEpRSlm11kEArXU78KlfIq311sRqCIiyp3/spyilriFew0pxcXG/BT5qSgajpmSw65NmNn1Yx8T5ef12byGEEEII0f+8wQh17QEaOgM0dgZp6AjS6A3S0BGgoTMYTzi7grR0hXrVZFUpcNvMZKZYGWM343FYSLWb8SRebrsJt233uhm3zUyKzYTLZiLFZsJqMg78QwtxFBrIBDQDaO2x3QakA7W9vP4G4H+7E9b90Vo/ADwAMGfOnH5t/X7CxWOp3NDCB89tp2xmFha7dJcVQgghhEgGXyhCTZufqlY/NW0Batr81HUEqGsPUNcRoL49QGcwctB7mAyKDJeFCbluMlwWMl1WMpwWMlxW0p1m0hwW0p0W0pwW0h0W3HYzRmkFJ0S/G8isqgVI7bHtSew7JKXUV4BpwBf7P6zeSc1xMO3UQla/VcmKV8uZ//kxyQpFCCGEEGJYC4SjVLX6qWz1UdkSf1W0+Khq9VPd5qfNFz7gtRajgWy3lfG5KeR4bOSk2Mh2W8lOsZKVYiU7xUZ2ihWP3YxBEkohkm4gE9CPgDuUUmYgD/AerDZzN6XUhcDlwAVa64EdA/gQ5pxXyuaP6ljzdiWTjs8nNceRzHCEEEIIIY5awUiUXc0+djZ1sau5i51NPsqbuihv7qK2ff/DfSgFOSk2ZhU7KUhzkJ9qoyDVTr7HTq7HRq7HRrrDIomlEEeRAUtAtdatSqn7gEXE+3F+Tyk1AzhDa/1rpdQ44D5gOvCYUupRrfXfgP8Cm4A3Ev1Dr9BaVw9UnAdjtZs49rNlLHxkE+8/vY3zvj0tGWEIIYQQQhw12v1htjV0sr2hi22NXrY3eNnW6KWyxbffvpdum4lphR6K0h0UpTkoSrdTnFjPT7VjMRkG/yGEEANmQDs2aq3/Cfxzn92rE8e2AKfv5xrXQMbUVxOPy+OTRdWUr22iYn0zxZMzkh2SEEIIIUTSBcJRtjV42VzXyeb6TjbXdbKlvnO/tZk2s4EJuW5GZzkpzXRSkuGkJDO+nuYwy8iuQowgMrLOISiD4sRLxvLMb1ax5MmtXDo+DaN8EyeEEEKIEaTNF2JDTQfrazrYUNvB+pp2tjd2Ed2nSjPFZmLOqDTG5qQwJttFWZaTsiwXBal2aSYrhAAkAe2VvDGpjJuXw5Zl9ax9p4qZZ/bflC9CCCGEEENJRyDMJ1XtrKlqZ21VG2ur2qlu8+91jsmgGJvtYmKem/G5KfFXTgp5HpvUZgohDkoS0F6a//kx7FzTxPKXdzJuXg7OVGuyQxJCCCGEOCLRmGZzXScrK1r5eFcrq6va2NHYtdc5DouROaPSmJzvZlK+m8n5HsbmuGSeSyHEYZEEtJecqVbmnlfK0me2sfSZbZxx1eRkhySEEEII0SedgTCrKtpYuauVVbta+biila5QtPu4xWhgeqGHaYWpTC30ML0wlTHZLpkPUwjRbyQB7YNppxWycWkNW5bVM/nEfPLHpiU7JCGEEEKIA2r3hVle3sJHO5v5aGcLn1S37zUSba7bxoIJ2cwuTmPWqDQm5qVIzaYQYkBJAtoHRqOBEy8dxwt/XM17/9vKJTfOwWCUAYmEEEIIMTT4QhE+2tnC+1ubWLq9mY11HegeCeeE3BTmlaYzpySdOaPSyE+1Jy9YIcSIJAloHxVNTKdsVhbbVzXyyXvVTDulKNkhCSGEEGKEisY0a6vaWLK1iSXbmlhV0Uo4Gs84DQom57s5pjSDY0rTmVeaTqrDkuSIhRAjnSSgh+H4L4xl17pmPnphJ2Nm5+Bwyx9zIYQQQgyOZm+Q97Y2snBTI+9tbaTNF+4+NjrTyfFjMjl+TCbHlWXgsZuTGKkQQnyaJKCHISXdxuxzSvjohR18+Nx2Tv3KxGSHJIQQQohhSmvNJ9UdvLOpgYWbG1hT1dbdrNZjN3P+9HxOHJPJ8WMzKZAmtUKIIU4S0MM044wiNn5Qy8altUw6MZ/cUk+yQxJCCCHEMBGOxli2s4U31tfx5oZ6atoD3cemFLg5ZXw2C8ZnMaMoTUaoFUIcVSQBPUwms5ETLxnLy39dy3uPbeELP5uDQQoAIYQQQhwmfyjKu5sbeH19He9saqAjEAHAZjZwxqQczpiYw4LxWWS7bUmOVAghDp8koEegZGomJdMyKV/bxIbF1Uw5uTDZIQkhhBDiKBIIR3l3cyMvr6vl7Y31+BJzcqY5zHxhdiFnTsrhxLFZ2C0yNYoQYniQBPQInXjJWKo2tvDBczsYPTNbBiQSQgghxEGFIjEWbWnkpbU1vLWhnq5E0pmVYuXi2YWcPSWPuSVpmGSqNyGSKhqLEtVRIrEIER2JL2OR7v1RHSUaixLRkb3OjenYXsd3L/farxPbPfb3XO5+RXUUrfVey+7jxIjF4kutdfd+zZ7ze+7ba6n3s524z50n3InHOnDdCyUB3cfC5g48JiOzPM5ene/OtDP73BI+en4HS5/exulfmzTAEQohhBDiaKO1ZlVFK89+XM1La2u7R67NdFn4/KxCzpuWx9ySdOnPKUYcrTWRWIRANEAwGoy/IsE964lXKBoiFA3tWY+FupfhaHjPeixMOBqOL/ddj4WJxCJ7LcPRcHeCuXv/7pdGH/oBhqFgNDig95cEtId1nT6+uHYHk1023pgzHqPqXSEw8/RiNn9Yx+aP6ph4fB4F49IGOFIhhBDDjVLKD3yU2HxEa/2PHsfmA/cDY4ExWuuqxP4ngQLACPxNa/3woe4lBtfOpi6e/bia5z6upqLFB0CKzcRlc4u4YHo+x4zOkKRTHBUisQhd4S58YR9d4S66Il10hbvwh/34Ij58YV98mVj3R/wEIgH8ET/+qB9/2B/fFw0QjAQJRAMEIvGkM6qjg/osCoXZYMZkMGEymDAbzJgNZuwme3yfMnUfMxlMGJURs8GM0WDEZDBhUIb4tjJiNBgxqj37d6/3PGZQBkwqcbzHvr2WBgMGDN3n7N6vlOo+z8CeYwYMKKXi6z1eij37dl+rUPFz2bNfET+GYq/7KRQZtowB/feXBLSHqSkOzs308EpTO4/UNHNlQWavrjOaDZz8xXE8/4fVLHp0M5fePA+jSZrNCCGE6JNqrfWCAxxbDxwHvLTP/hu11luVUjbgE6XU/7TWgUPcSwwwXyjCK+vqeHx5BcvLWwEwGxVnTMrhczMLOHVCNjaz9OkUgysQCdAebKc91E5HsIOOUAedoc7u1+5tb9iLN+SNL8NeOkOddIW7+q1WzGq0YjVasZlsZNozsZls2Ey2PfuNNixGCzaTDYvBgtVoxWLcs9xr3WDBbDRjMVi6j+3etzupNBvMn9o2GuT3L5kkAd3HLWPyeaelg3t31HJBdirp5t79ExVOSGfs3By2Lq9nzduVzDpr1ABHKoQQYpjJVUotApqBH2qty3cf0Fq3A6h9WuZorbcmVkNAFLrbix3wXmJgaK1ZU9XO48sreXFNDd5gfATb6UWpfGF2IZ+ZmkeaU8aJEP0jFA3REmjpfrUGWmkNtNIWbKM12EpboI22YPzVEeygPdR+WAmk3WTHZXaR58zDZXbhNDtxmB04zc496yYndpMdh9mBw+TAYXZgN9n3etlMNhwmB1ajVZI/IQnovkbZrXy7OJvflddzz45afjW+qNfXHv+FMexa18Tyl3cyZk427gyZDFoIIUSvlWitm5RSZwH/AE7rw7U3AP/TWu/+H2av76WUuga4BqC4uPjwIh/BOgNhnv24mkc/qmBTXScQH8H2quNLuXRuEeNzU5IcoThaxHSMlkALDb4GmvxN+321BFpo8bfQGe7s1T1NyoTb6ibflY/H4sFjjb/cFjdui5sUS8p+X7uTTZNBUgXR/+Snaj++U5zD47UtPFLTzJfzM5ia4ujVdU6PlWMuLGPx41tY8sRWzv3WtAGOVAghxHChtW5KLF9XSv21t9cppb4CTAO+eDj30lo/ADwAMGfOnJE54sZh2FjbwX8+3MWzH1fjC0VRCk4al8Wlc4o4fVI2VpPU8og9orEoTf4martqqfPVUeeto85XR4OvgXpffTzp9DUR0ZGD3sduspNuS6fUU0q6LZ10ezrptnRSramk29LxWD2kWdNItaWSZk3DaXZ+quWEEMkmCeh+OIwGbh1TwDfWl3Pz1mqemzmm17+8U04uYOPSGnauaWLn2iZKp/WuH6kQQoiRSynlAvxa66hSahrQ1MvrLgQuBy7QWseO5F7i0EKRGK9+Ust/PtzV3bcz3Wnhq/NLuHxeMUXpvfvCWgw/MR2jwddAVWcV1d5qarw1VHmrqPHWUOOtocHXcNDkMsWSQomnhGxHNln2LLId2WTYM8iyZ5Fpz+x+OczyMyaOfpKAHsBnsjyckOpiSZuXZ+pbuSg3vVfXGQyKBZdP4KlfrWDx/7ZQOD4Ns1W+BRVCCHFQk4D7lVKdxPtxXquUmgGcobX+tVJqHHAfMB14TCn1qNb6b8B/gU3AG4kvSq8gPiruXvca9KcZZlq7Qjy6rIJ/LS2noTPeynlWcSpfPm4U50zJkwGFRohoLEpNVw0VHRVUdFZQ0VFBZWclFZ0VVHdWE4qF9nud0+ykxFNCnjOPXGdu9zLXmUuOI4csRxZ2k3TbEiOHJKAHoJTijnEFnLZ8M7dvr+GsTA+uXjanySl1M/nEAta/V83yl3Yy/6IxAxytEEKIo5nWehkwcz+HVieObwFO3891rv1cU32Ae4k+2tbg5Z/v7+SZVVUEwjEsJgOXzCnkK8eVMKVg4CZpF8nlC/vY3radHe07KO8op7y9nPKOcio6KvabZJoNZgpTCil0FVKYUkiBq4ACVwH5rnwKXAW4LW5pBitED5KAHsQEp52rCjJ5sKqJP+yq5+ay/F5fe9xnR7NzdSOr365k3DE5ZBbKIARCCCHEUKe15oMdzTz43g4Wbm4EINNl4Zsnj+JLx44i02VNcoSivwQiAba3b2dr61a2t21nW9s2trdtp7ar9lPnmpSJwpRCSjwllLhLKHYXU5RSRHFKMTmOHBnZVYg+kAT0EH5ckssz9W3cX9nIF/PSKXPYenWd1WHmhEvG8sZD61n4n81cdP1sDDLRtBBCCDEkxWKaNzfWc9+721lT2QbAhNwUrjqhlAum50sz26Nco6+RTS2b2Ny6mS0tW9jcupnyjnJi8a7T3RwmB1Mzp1KWWkaZp6w74SxIKcBsMCcpeiGGF0lAD8FjNnFTWR4/3FTJz7dW899po3vdjGLM7Gw2f1jHrk+a+WRRNdNOKRzgaIUQQgjRF+FojOdX1/D3RdvZ1uAF4PgxGXzz5DJOGJMpTSePMlprGnwNbGjewIaWDfFl8waa/HuPxWUymBibOpZxaeMYmzaWstQyxqSOIc+ZJ5+5EANMEtBeuCw3nUeqm3mnpZPXmto5Jyu1V9cppTjpsnE8dvtHfPj8dkbPyMKVJk13hBBiuFFKLevFaY1a6/MGPBjRK4FwlCdWVPL3d7dT0x5AKTh7ci7fXFDGjKLUZIcneqkr3MX6pvWsbVrL2sa1rGta96lkM8WSwjF5xzAxfSLj0sYxLm0coz2jMRulRlOIZJAEtBcMSnH3uELOWbmFm7dWc3K6G4fR0Ktr3Zl25n1mNEuf2cbix7dwzjenDnC0QgghkiANuPogxxXQ67k9xcDZnXjet3A7dR0BTAbFxbMLufbk0YzJlvEahjKtNVWdVaxqWMXHDR+ztmkt29u279WMNsUcTzYnZUxiUsYkJmdMptBVKLWaQgwhkoD20gy3g6/kZ/Cvmmb+uKueG0bn9fra6acVsmV5HTtWN7JjdSOjZ2QNYKRCCCGS4E9a60UHO0Ep9ffBCkZ82r6Jp9mouOKYYr59yhgKUmUKjKEoGouypXULqxpWsao+nnQ2+hu7jxuVkXFp45iWOY1pWdOYmjWVEncJBtW7SgIhRHJIAtoHPxudx4uNbdxX0cAluWm9HpDIYDSw4IoJPH3vChY/voXCCWlYbPJPL4QQw4XW+s/9cY7of6FIjMdXVPLXd7ZJ4jnExXSMbW3bWFa7jGV1y1hRv4LOUGf3cZfZxfEFxzMrexYzs2cyOWMyDrMjiRELIQ6HZEF9kGY28fOyfH6wqZIbt1Tzv+m9H5Aop8TN1AWFrF1YxUfP7+DES8cNcLRCCCGSQSl1GlBGjzJWa31f8iIamWIxzYtra/jtG1uoaPFhNiouP6aYby8oozBNkpahotpbzdKapXxY8yHL65bTGmztPpZuS+fMUWcyO2c2s3JmMTZ1rEx3IsQwIAloH12am86jNS0sau3kpcZ2zs9O7fW1x1w4mh2rG1n7bhXj5uWSU+oeuECFEEIMOqXUw8AcYBUQTezWSQtoBNJa8+7mRu59bROb6joxKLhoViHfP30sRemSeCabL+xjed1yltYsZWnNUso7yruPuS1uTi06lXl585iXO48xqWOk76YQw5AkoH0UH5CogDNXbOEX26o5NT0Fp6l338ZZbCZOumwcr/xtHe88spFLbpyL0ST9FIQQYhiZD0zWWoeTHchItHJXC/e+upll5S0AnDEph5+cNZ5xOTK4UDJVdFSwqGoRi6oWsbJ+JZFYBIhPhTI3dy7z8+dzXP5xTEibIDWcQowAkoAehikpDq4qzOShqiZ+t6uen5fl9/ra0ulZjJmTzbYVDax6fRdzzysdwEiFEEIMsspkBzASVTT7uOe1jbyyrg6AeaXp/PTsCcwelZbkyEamSCzCxw0fs6gynnT2rOUc5R7F/Pz5HJ9/PHNz50ofTiFGIElAD9P1pXk839DG/ZUNXJKbznhn7wYkAjjxknFUbmxhxSvllM3MJj3fOYCRCiGEGGhKqW8nVrcAbyulngMCu49LH9CB0e4P89eF23j4/XJC0RjjclzccO5EFozLkqabgywYDbK0eilvVbzFu5Xv0hHqAMCkTByTewwnFZ7EyUUnM8o9KrmBCiGSbkATUKXUlcA1xPu/fFdrvarHsfnA/cBYYIzWuiqxvwT4J2AFXtZa3zWQMR4ut8nILWX5fGdjBTdsqeLpGWW9LuwcbgsnXjyWtx7eyML/bORzP56NwSAFpRBCHMXm9ljfDvSc9Fn6gPazcDTGY8sq+P2bW2j1hcl0WfjhGeO5ZE4hpl7O0y2OXFe4i8VVi3mr4i3eq3oPf8QPxPtynj/6fE4uOpn5+fNJsUgTaCHEHgOWgCql0oDrgGOBAuAR4IQep6wHjgNe2ufSe4BbtNaLlVJvKaWe0VpvGqg4j8RFOWn8t7aZpW1enqpv5eLc9F5fO+6YXLYsr6difQvr3q1i+qlFAxipEEKIgaS1/tqBjimligczluFu8dZGbn1hPdsbu7CYDHx7QRnfWlBGis2c7NBGhEAkwHtV7/Hqzld5r+o9QrEQAJn2TC4ou4DTik9jTu4czAb5PIQQ+zeQNaDzgMVa6xCwUymVopSyaq2DAFrrdmB/tYYztNaLE+svAycDQzIBVUpx77giTlu+mVu2VXNahpt0c+/+SZVSnHz5eB67fRkfPred0mmZuDNlPjIhhBgOlFIW4CLg68BsQDojHqGqVh93vLSR19bH+3leMD2f688eL1OqDIJwNMzSmqW8Wv4qCysW4ov4AMh15nLmqDM5Y9QZTMuahkFJ7bMQ4tAGMgHNAFp7bLcB6UDtIa7r+derDcg90IlKqWuIN/GluDg5XzCPc9r4TnE2v99Vzx3ba/jdhN7H4c6wc9xny1j8+BbefXQz5393uvRZEUKIo5hSajbxpPMSwJ5YPz+pQR3lAuEoD7y3g78u3EYwEmNCbgq3XziFeaW9b3Uk+k5rzbqmdbyw/QVeK3+N9mA7EJ+b88IxF3JO6TlMz5ouSacQRzmtNcRi6GgUIhF0LIbB4UAZBu53eyAT0BYgtce2J7HvUGK9vUZr/QDwAMCcOXOS1sfmulE5PNfQyqO1LVySm86xqa5eXzv15AK2rainckMLmz+sY8JxeQMYqRBCiIGglPoe8DXiSefDwEzgPa31/5IZ19HurQ313P7SBipafLhtJm46byKXzyuWfp4DqNZby0s7XuKF7S90j16bYk7hc2M+xzml5zA3dy4mg4xhKURPWmsIh4mFQuh9XrFgML4eDqND4R7rieWnXiF0JALd2xF0JBJfj0TQkXA8Udy9PxKJb+9+RaMQCaMj0cR2BCLRvRLM7vVoFKLRTz3P2MXvYcrKGrB/r4H8C/IRcIdSygzkAd7dzW8PYY1Sar7WeilwDvD9AYyxX9iNBu4dV8Qla7bzk82VvD13PJZefmugDIpTvjyB/92xjCVPbqVoUjpOj3WAIxZCCNHPfg+8DVyrtd4BoJSSwYcOU02bn1teWM+bG+pRCi6bW8RPzhpPhkvKx4EQjAZ5a9dbPLv1WZbVLUOjMSojJxWexPll57OgcAE2U+9H+xdiqNBao/1+Yj5f/JVY134/sUCAmM9PzO9D+wPEAgF0YPfST8wfIBbwowNBdDAYTyQDAWLBADoYQgcT+0PxdWKxQwc0GJRCmUxgMqGMxr3WsVowGBPru48ZDaie+xLLgTRgCajWulUpdR+wiPgIgN9TSs0AztBa/1opNQ64D5gOPKaUelRr/TfgBuAfif4zr2qtNw5UjP3ppPQULspJ4+n6Vv5W0cj3SnJ6fW1arpO555by0Qs7eO+xLZx97RRpiiuEEEeXScBVwBKl1BbitaBSTddHkWiMf32wi9++sRlfKMqUAjd3fHYqM4pSkx3asLS9bTtPbXmKF3e82N3EdnzaeM4vO5/zRp9Hpj0zyRGKkUqHw0S9XmIdHUQ7Ool5O+Pbnd6917u69ry8XqK+xLrPh+6KJ5zo/v0uUJnNKKsVZbNhcDoxpqfHt60WDGZLfN2ye2lGWSwYLBaU2RLfbzbvWfZct5j37DOZutcxmVCmxHGTqfuFyYwyJ7aNRth93QA2ne0vSh/gQ1FKPdGL61u01t/s35AOz5w5c/SKFSuSGkNjKMwJH20iGIvx7rwJlNh7/01tNBrj6XtX0ljRyRlfn8S4uQfs+iqEEGKAKaVWaq3nHMZ1RuA84snoWcCTwH+11q/3c4gDIpll6bqqdm54di2fVHfgsBj50Znj+epxo6S5bT8LRAK8Xv46T215itWNqwFwmp2cV3oeF427iEkZk5IboBhWYsEg0bY2oq2te17t7YlXR4/1NmLtHUQ7O4l1dBDz+Q77PZXDgcHpwOBwYHA4E0sHBrs93rfRbsNg371tR9nt8W1bIqm02zHYbCibDWW1dq8brNZ4UjnAtYPDyYHK0oPVgB4D/OIQ9/3ZEUU1zGRZzPy8LJ8fb67kZ5ureGz66F7XZBqNBk79ykSevHs57/1vC4Xj03G4LQMcsRBCiP6ktY4CLwAvKKWyga8CvwWOigQ0GbqCEX77xhYeXrqTmIbTJ2Zz24VTKEiVkeH7U1VnFU9sfoJntj3TXds5PWs6F429iLNKzsJhltGExaHpaJRoWxuRpiYijU1EmhqJNrcQbW0h0txCpKWZaEsr0eZmIm1t6L4kkiYTRrcbU1YWBrcbY0pKYunC4ErBkOLC6Oqx7nRicLniL6cz/hrgwXNE/zhYAvqo1vpfB7tYKTWhn+M56l2el84TdS2829rJcw1tfC6n9yPvZxa6mHNuCcte3MmiRzdLU1whhDiKaa0bgF8nXmI/lmxt4mfPrKWq1U+u28atF0zmrMk5Uvb1k5iO8WHNhzy26TEWVS1Co3GanXxxwhe5eNzFjE0bm+wQxRChtSba0kK4ro5IQwORhkYi9fVEGhsI795OJJu96etocLsxZ2VhTE3FmJa2zzIVoycVo8eD0eNOLD0oh0N+90eIAyagWusbDnVxb84ZaQxKce+4Qs5YsZlfbKvmlPQUUns5NyjArLNHsXNNEztWN7J1Rb00xRVCiKOAUuoJrfUlR3rOSNERCHP3Kxt5bFklAF86tpifnj2BFJs5yZEND76wj+e3P8+jGx/tHsl2tGc0X5zwRc4vOx+n2ZncAMWgiwUChGtqCFdXE66uia/X1RKprSNcX0+krg4dCh34BkphzMjAOnYspszM+CsrE2NGJqbMDIzp6ZjS0zGmZ2BKS0VZpBWfOLBeZUZKqTKgrOf5WutXBiqoo91El51vF2Xzp4oGbttew+/7MDeo0WjgtK9O5Im74k1xC8alyai4Qggx9J2slPrVIc6ZOSiRDHELNzdw4zPrqG0PUJzu4N6LpnFcWUaywxoWGn2NPLbpMR7f/DgdoQ4MysBpxafxxQlfZF7uPKldGsZ0NEqkvp5QRSXhqso9y6pqwtXVRJubD3itstsxFxRgzsvFlJOLKScbU3Y25pwcTNnxdVNGRnzgGyH6wSF/kpRSdwNXAxuB3RPFaEAS0IP4QUkuLzW281htC5/PTuPE9JReX5tR4GLueSV89EK8Ke4535wqhYYQQgxt9/XinL8PeBRDWLsvzO0vbeDpVVUoBVcdX8qPzxqHwyL/qT1SW1q38O/1/+blnS8TiUVwmBx8aeKX+NKkL1HgKkh2eKKf6FiMSEMDofJyQuW74std8WW4qgodDu/3OmNmJrbp0zDn52MpKMCUn485Px9zXh7m3FwMbrf8P1MMqt781b8YKNNadwx0MEOFjsWOuAOz3WjgN+OL+Pzqbfx4cyUL503A0YeR/GaeNYodq5vYuaaJrcvrGTdPmuIKIcRQpbW+LdkxDGWLtzbykyfXUtcRYHSmk199YRpzStKTHdZRb1X9Kh5c9yBLqpcAkOPI4YqJV3DRuItwW9xJjk4cLh0OE6qsJLh9O6HtOxLL7QR37kT7/Z86X1ksmIuLsRQVYS4qSiwLsRQXYy4owGCTOVzF0NKbBLR2pCSfkcZG6m6/Hdu0aWR+4xtHfL/5aS6+nJ/BIzXN/HpnLbeM6f23kHs1xX18CwXjpSmuEEKIo4s/FOXuVzfy7w92oRRcfUIpPz5rPDazTGNwuLTWvF/zPg+ufZBVDasAmJA+ga9O/ipnlZyF2SD9aI8WWmsitbUEtmwhuGUrwS1b4q+dO2Hf2kylMBcUYBldimVUCZaSUVhKSrCMKsGclytTg4ijygETUKXUuYnVD5RSjxGfyyyw+/hw7AOqY5quDz7Eu3gJ7rPPxlJUdMT3/HlZPm82dXB/ZSMXZqcxw937Yc57NsV997+bOfdb0hRXCCHE0WF1ZRs/fHw1O5q6KEi189tLpnPsaOnrebhiOsbbFW/z4NoH2diyEYBZ2bP4xrRvcHz+8fL/gyFOh8MEd+wgsGEjgY0bCG7YSGDzZmKdnXufaDRiGTUK65gxWMpGYx1dhnVMGZaSEgx2mZpIDA8HqwH9yT7b3+2xPiz7gJpzssn+8Y+ou/U26m69jaKHHjziP+huk5F7xhVy5Sc7+eGmCl6fMx6zoff3nHlWfFTc8rVNbFxay6Tj848oHiGEEGIghaMx/vzONv66cBvRmObi2YX84vxJMsLtYYrpGG/seoO/r/4729u3A3B8wfF8Y+o3mJ0zO8nRif3RkQjBbdvwr1tHYN0nBNavJ7hly6f6aJpycrDPnIF17Fhs48ZhHTcOy+jRGKzS4k0MbwebhuWUAx1TSg3br2BSL7mE9udfoOv99+l46WU853/miO95dpaHz2R5eKmxnb9VNnDdqJxeX2s0Gjj9a5N4/M7lLHliK4Xj03BnDtt/fiGEOKoppX4GPKi1PvCQkwe+1g98lNh8RGv9jx7H5gP3A2OBMVrrqsT+EuCfgBV4WWt9V2L/2cAtictv1Vq/fnhP1DexmObyBz9keXkrGU4Ld39+KmdOljEMDkdMx3in4h3+uvqvbGvbBsDpxadz9bSrmZwxOcnRid201kRqavCtXo1/zZp4wrlxIzoQ2HOSUlhKSrBNnIh14gRsEydhmzQRU7r0gxYjU5+GnlNKHQd8HbgISBuQiJJMGQzk3nYrOz9/EfV3343rxBMwpqYe8X3vGlvIklYvvy2v49wsD2Mcve8Qnpbr5LjPlbHkia28/a+NfPYHM1F9qEUVQggxaPKADUqp14G/aq0/OtQFPVRrrRcc4Nh64DjgpX323wPcorVerJR6Syn1DLAV+BVwUuKcRUqpt7TWUQaYwaA4c1IuqY548pnpkpqcvtJas7ByIfetvo/NrZsBOLXoVL4949uMTx+f5OhELBQisH49/o9X41+9Gv/HHxNpbNzrHHNhIbapU7BPmYpt6hRskyZjdMncq0Ls1ptpWLKBrwJXAaXEv1GdPsBxJZVt3Dgyrv46zX+/n/pf/5r8O+884ntmW83cMiafH2yq5MebKnlm5hgMfWjeO21BITvXNFG9uZU171Qy4/Tezy0qhBBicGitv6eUuhH4MvCgUioE/BV4TGsdOPjV5CqlFgHNwA+11uU97tsO7K9byAyt9eLE+svAycS7yezUWrclrikHxgCbD//Jeu/rJ5Ry9Yml0ifxMHxQ8wF/XPVH1jevB+DkwpP51oxvSY1nEsV8PvyrV+NbsQLf8hX4165FB4Pdxw1OJ87587HPmIF9xnRsU6diShuWdTRC9JuDDUJ0IfGk83jgaeJzgf5Ha33vIMWWVJnf/CYdr75K+9PP4LnwQpzz5h3xPS/LTeeZ+lYWt3p5uLqJqwqzen2tMihO++pE/nf7R3z43A6KJqWTke864piEEEL0L611l1LqAaAB+ANwA/BLpdQPtdZPHOTSEq11k1LqLOAfwGm9eLue83u1AblABtC6z/4DtvVTSl0DXANQXHzkX24apIVOn21o3sAfVv6BD2o/AOD4/OP5vxn/x9SsqUmObOSJ+f34Vq7C9+EHdC1fTmD9BohEuo+bi4pwzJqFfdYs7DNmYB1TJiPQCtFHB6sBfRZ4G5iotW4EUErpQYlqCDDYbOTdeisVX7uKultupfS5Z4+4U7hSit+ML+KU5Zu5Y0ctp2W4GWXv/T1T0m2ceNk43n54I2/9vw184adzMJqObL5SIYQQ/UcplQNcC1wJrAC+pLV+TylVCiwCDpiAaq2bEsvXlVJ/7eVbxnqse4CWxCt1P/sP9L4PAA8AzJkzZ8SU80NBZUclf/74z7xa/ioAUzOn8oPZP2Bu7twkRzZy6HAY/7pP6PrwA3wffIh/9eq9Bguyjh2DffZsHHPm4pgzG3Ou9GkW4kgdLAE9D/gasFEp9SLwr8EJaehwHnccns9+lvbnnqP5/gfIuu67h77oEEbZrfy8LJ8btlTxw02VPDmjrE9Ncccfk8vO1U3sWN3IilfKOeaC0UcckxBCiH7zMfAwcNLugYIAtNY7lVL/70AXKaVcgF9rHVVKTQOaevl+a5RS87XWS4FzgO8T7wNaqpRyJ84pBbb1+UnEgGkJtPC31X/jqS1PEdERStwlfG/W9zit+DRpujwIwjU1eJcsoWvxEro++ICY19t9zDyqGOexx+E87lgc8+bJQEFCDICDjYL7KvCqUiod+BLwe6BQKXU78F+t9aD0JUm27J9ej/fdd2l68EHc552LtazsiO/51fwMXmxo4/02L/+uaebKgsxeX6uUYsEV46nd3sbK13YxamoGuaWeI45JCCFEvzhNa72x5w6l1ASt9Sat9S0HugiYBNyvlOok3ofzWqXUDOAMrfWvlVLjgPuIj8HwmFLqUa3134g37/2HUsoCvLr7vZVSNwC7R769YTAGIBKHFo6GeXTTo9y/5n46w51k27P51oxv8dkxn8Vk6NO4kKIPdCiEb8UKvO8txrtkMaFt27uPGTMycJ9yCs5jj8V53LGY82W6OzF0xLQmENP4ozH8sRiBWAx/NEYwprvXAzFNMLZnX/AA26GY3ntdx5e794f1nnPenTeBTMvA/U1SWve+tY1SahbxfqGXaa17nzUNgjlz5ugVK1YMyL3bnnuO2p/dgH32bEY98m+U4cibve7yBzlleTyHXzh3fJ+a4gLsXNPIK39bhyfLziU3zcVik4JLCCH6i1JqpdZ6zmFct0prPetQ+4aqgSxLRzKtNe9UvsPvVvyOis4K7CY7X5vyNa6cfCV2k0ytNhAira10vfcenQvfpWvxYmJdXfEDRiP2mTNwnXAizhNPwDZxYr/8v06MbDGt6YrG6IxE8UZjeKNRfNEY3kh8vSsaoysawxeN0ZU41pVIKn3ReCLpi8bwxfasB2Ix/LHB6RVhUQqLIfFSBl6fM44c65HP3XygsrRPWYvWehWwSin1oyOO6CjiufBCOl54ga6lH9D6v/+RfvnlR3zPUXYrN4/O48at1YfVFLd0ehaTTshnw5Ialjy5lVO/PPGIYxJCCHF4lFKZQDZgU0pNBHb/QfcAMv/CCLa5ZTO/Wv4rltUtA+CCsgu4buZ15Dh7Pye46J1QZSWdb76F95138K1aBbF4F2ljRgaes87CdfLJOOcfhzElJcmRiqFEa40vFqM9HKU9sufVFo7SGY3SHo7SEY3SGYnSEYnSGYnRmdj29kg6+4PdoLAbDTiMBjItJuwGA3ajAZvBgN2osBni6zajAZtBYTcYsBpU977d67uXlh5La2K/NZFkWhMJp1mpQW/6f7BRcP+gtf7+/o5prYOHOmc4UUqRe/vt7Dj/Ahp/81tSFizolyYaVxZk8lJj+2E1xQU44eKx1GxtY+P7tYyanEHZrOwjjkkIIcRhuYJ4/8t84JUe+9uJz8kpRpj2YDt//vjPPLnlSWI6xqzsWVw/93omZ8qUKv0puH07nW+8QccbbxLcuKf1u3XsWFynnELKqadgmzZNajlHiJjWtIajtIQjPV7x7eZwhLZwlNZIYhmO0pZYD/WhRehuNoPCZTSSZTEx2mjEZTLiMhq6lw6jAZcxvu40GXAajTiNBhwGA06joTvR3P2yGwx9qow6mh2sBvQipdSWQ1x/dn8GM5RZCgvJ/sH3qb/rbmpvvZWi++8/4m8LDErx+wlFLFi2mdu313BqegrFfWiKa7YaOeOqSTx970oW/mcTOaUeXGky6bcQQgw2rfUfgT8qpW7UWt+V7HhE8sR0jOe2PccfVv6B1mArec48fjTnR5w56kwZYKifBLdupePVV+l44409/TmVwj5rFilnnkHK6adjKSxMbpCi30RimqZwhIZQmIZQfNkYjNAYDtMUisRfiQSzORSht3WRZqVIMxsZZbeQZjbhMRn3vMxGUk1G3D1eKYljKUYjKSYDFvlS47AdLAF9CzjUOOCv9WMsQ17aFVfQ8fIrdL23mI4XX8RzwQVHfM9Rdis3l+VxU6Ip7hN9bIqbPcrNvAtK+fC5Hbz9rw1ccN0MlMzBJoQQg0opZU20DvqDUsqx73GttS8JYYlBtr55PXd9eBdrm9ZiNpj5xtRv8I1p35B+nv0gVFVFx8uv0PHyywS3JOpHDAYcxx7bnXSas6Ul2NFEa01rJEptMExNIERdKExtMEx9MExdMEJ9KExdMExzOMKh6idNCjLMJia6bGSYTWSYTaQnXmlmY/d6utlIqtlEmsmIw2iQL4WS5GCj4H5tMAM5Giijkbw772Dn5z5P/Z134Zw/H1PmkY/F9LWCTF5qbGNJm5d/1TTztT42xZ155igq1rdQtamV1W9XMvOMI59IXAghRJ98AMwCvMRHse35vxoNyEz1w1h7sJ0/rfoTT255Eo3m+ILjuWHeDYxyj0p2aEe1SFMTHa+8SsfLL+Nfsya+UykcxxyD+7xzSTnjDExpackNUhxQIBqjJhimKhCiKhCiMhCiKhiiJhBPNGuDoYMOsqOALIuJKS472RYz2VYT2RYzWZY9y0yziUxLvPZypDRfHQ5k6NQ+so4ZQ8a3vknTn/5M3Z13Uvj73x/xPQ1K8YcJxZyyfDO3b6vm5LQURjt635TWYFCc/rVJPH7HMj58bjuFE9LIKpIO9kIIMVh2j3KrtZY2WSOI1prXy1/nnmX30BxoJs+Zx0/n/ZRTi06VmpXDFAuF8L6zkPbnnsO7eDFE4zMI2aZNw3PeuaScfQ7mHKnpHApiWlMbDLPLH2JXIEiFP0RFIERFYrshFDngtXaDIt9qIc9qJs9mpsBqIcdqJs9iJsdqJtdqIstsxiSt+oYlSUAPQ+bVV9P5+ht0vvoaneedR8rppx/xPUfZrdw+poAfb67kOxt38cLMsX36pUtJt3Hy5eN546H1vPmP9Vx841zMFvnCXQghBpNSagqwU2vdldh2AiVa6/XJjUz0t2pvNXd8eAdLqpdgUia+PuXrXDv9Wmluexi01gTWrqXtuefoeOVVYu3tAJhHFeO58EI8n/kMlmJp3ZUMOpFkbvcF2ekPssMfpNwfZIcvnmQGD1CDmW42Mj3FTqHNQqHNQpHNQqHVQqHNTL7NQprJKF/SjGCSgB4GZbGQd8cdlF96KXW33Y5j3jyMbvcR3/eKvHRea2rnreYO/lJRz/dLcvt0/dg5OVR80symD+tY+tQ2Tr58/BHHJIQQok/+BRzbYzuc2NfnOUXF0BSJRfjvxv/y19V/xR/xMy1zGr847heMT5cyt68ira10vPACrU8+2T2YkCElhdRLLsHz2c9inzlDkpRBEorF2O4LssUXYLsvyDZfkG2J9a79TDFiUjDKZmWU3UKp3Uqx3cIoW3xZbLPgMkkliDiwQyagSqnLgKe01geuRx+B7FOnkP61K2n5xz+pv/de8u+884jvqZTid+OLWLB8E78pr+PUDDfTUj41lsVBnXjZOGq2tfHJe9UUTUxn9MysI45LCCFErxm11uHdG1rrkFJKvuwdJjY2b+SWpbewsWUjTrOTG+bdwKXjL8VokP9s95bWGt9Hy2h78kk633gDHQ6DUjhPPJHUz30W16mnYrDZkh3msBWJaXb4g2zs8rO5K8DmrgBbugLs9AeJ7FOZqYBCm4V5HitjHFZK7fHXaIeVAqtFmseKw9abQvGLwG+UUv8E7tdaVw9wTEeNrO98h8633qL96Wdwn30OrhNPOOJ7ZlvN/GpcEVevL+c7Gyp4Y844bMbedymy2EycefUUnvnVSt55ZCOZxS7cGdIcSAghBklYKTVaa70DQClVBkSTHJM4QuFomPvX3s8/1v2DiI5watGp3HDMDeQ6+9ZSaSSLtrXR9vQztD3xBKFduwAw5eaS+vnPk3rR5zEXFCQ5wuGnLRzhE6+fjd4A671+NnT52dIVILBPs1mzUoxx2BjvtDHOYWOM08pYh41SuxV7H/4PKkRvHTIB1VpfqJQqAa4FViillgD3aa0XDnRwQ53Bbif/rrvY9aUvU3vzzYx+8YV+aYr7mexUvtCUxlP1rdy9s5bbxvTtj3JOiZtjP1fG0qe38eY/NvC5H83EIH9AhBBiMNwGvK+UejmxfS7wjSTGI47QhuYN3Pz+zWxt3Uq6LZ0bj7mRs0rOSnZYR43Ahg20PPooHS++hA4GwWjEddpppF78BVwnnogySu1xf2gMhVnb6Wddp491Xj9rO/1UBkJ7naOAUruViS4bE512JjjjSWeJ3YpZajPFIOpVsyCtdTlwg1LqReB/wNlKqZ3A/2mtFw9gfEOeY/Zs0r/6VVoefpj6e+4l/64jb4oLcOfYApa2eXmgspEzM9wcn9a3UW1nnFZE1aZWKtY3s+ylnRx7YVm/xCWEEOLAtNYvKaVOBnaPTneP1npbMmMShycUDfH3NX/nn5/8k6iOcnbJ2dxwzA2k29KTHdqQp0MhOt54k9b//hf/xx8DYMzKJO3ir5N66SWYc3KSHOHRrSMSZW2nj487Eq9OH7XB8F7n2A0G5rqdTEmxM9llZ1Ii2XRK30wxBPSmD6gFuBT4NvF5zG4mnoTOA/4DlAxgfEeFrO9/D++779L+zDPxyZAXLDjie3rMJv44oZiL12zne5sqWDh3Ail9+KOhDIrTvjqRx+9cxsrXdlEwPo2iCVJoCiHEQNNabwG2JDsOcfg2NG/gpiU3sa1tG+m2dG4+9mbOGHVGssMa8iKtrbQ9/gSt//0vkcZGAOyzZpF2xeW4zzgDZbEkOcKjT0xrNncFWNHRxfL2Lj7u8LHVF9zrHJfRwHGpTqalOJjmsjM1xUGZw4pRBnAaEWIxTTgQIRSIEg5ECQUjhIPx9XAgsR6MEQ5GCIdiRILR+L5QNL4eihIJxQgHo0RC8e0rbjsWu2vgfl97UwNaDrwL/FBr/UGP/UuUUm8NRFBHG4PNRv49d1N++RXU/fwXOF56EaPHc8T3PTE9hasLM3moqokbt1bx54l9m9Da4bZwxtcm8fwfV/PWPzdw6c3zcLjlj78QQgwUpVQR8CtgOtA9korWenTSghK9FolF+Ocn/+Rvq/9GREc4p+QcbjjmBtJsackObUgLlZfT8u9/0/bMs+hAAGWx4Lno86RfcQW2SZOSHd5RpSsaZVW7j2XtXazo6GJlRxcdkT2j0JqVYnqKnZluJzNTHMx0OxjjsGKQZPOoFI3ECPkjBH2Jlz9M0BeJ7/PHlyF/NL4MJLYD0fh6IsGMhD49SvHhMJkNmKxGzFYjsX1HpOpnvUlAZ2uta3vuUEq5tdYdWuurByiuo459xgwyrvoazQ/9g/q77iL/3nv75b43jc5nUUsnT9a1clq6m8/m9K0QLJyQzuyzR7Hy1V28/a8NfOb/pqOknb8QQgyUfxJvJTQDuAL4FrA9mQGJ3qnsqOTGJTeyunE1HquHXxz7C84sOTPZYQ1ZWmv8K1fS/M//h3fhQtAaY3o6aVdfTdoXL8OUkZHsEI8KHZEoH7V5+bC9iw/bvKzp9O01Gm2WxcR5WSnMcTuZ63EyxWXv0+CUYnBEozEC3nD81ZV4da9HCHbFE8tAYhn0hQn4IkSChzdGnclqxGIz4kqzYbEZMduMmK2mxLoJc+K42br3a3eCabYktncvzYZBzQ96k4C+DMzaZ9+7+9k34mV+97t0LnyX9udfIOXMM0k57bQjvqfdaODvk0s4Z8UWrt9SyWyPkyJb32ox532mlJotbVSsb+HjtyqYdWbfalKFEEL0WqbW+h9Kqe9rrT9QSn0EfEB8cCIxBGmteXbbs9y77F58ER/HFxzPL+f/kiyHTGO2P1prvO++S/MDD3b377SMKSPjyitxn38+Bqs1yREObV2RKB+2d7G4tZP3W7184vXTs65pnMPGsalOjvE4meNxUmyzyFyoSaBjGr83jL8zhK8zhL8zhL8jvGe9M0zAm1gmksreUgaF1W7C6bFgtZuwOs3xpSP+sthNWO0mLA4TFluPbfueBNNwlFcmHTABTcxbZgEMSik78cGzADxA3yanHCEMVmu8Ke5lX6T2F7dgnzULU9qRN9uZ7LJzU1ket2yr4TsbdvH0jDF9mnvJYDRwxtcn8/gdy/jouR3klaWSV3bkTYSFEEJ8yu5hJ71KqWKgHpBMZohqCbRw69JbWVi5EJvRxo3H3Mhl4y+T//Dvh45E6Hj1NZoffJDglngXZ8fcuWR842qcJ54o/2YHEIrFWNHuY0lbJ0tavazq6Oqu4VTAFJed41JdHJvqZJ7HRaZFpg0eSNFoDH9HiK62EF3tQbragnS1B/F1hPC1hxLLIL7OMDp26GaoJosBu8uCO9OOPcWMzWXG5uzx6rFtdZiwOc2YbcYR//tysJ/ym4BbAA109djfAfx2IIM6mtmnTiXj6qtpvv9+6n/5Swp+97t+ue83CrNY2NzJu62d/Kminh+W9G3usZR0G6d9dSKv/G0dbzz0CZfcNHdAOxcLIcQI9Z5SKh24D1gJBIEnkxuS2J8Paj7gxiU30uRvYnLGZO468S5Ge6Sr7r5ioRDtzzxL8z/+QbiyEgDXKaeQcc03cMycmeTohqZd/iALWzpZ2NLBklYvXdE9ffTGOqycmJbCiWkujkt1kWqWhLO/RKMxulqDeFsDeFuDPV4Butri677OEBwsr1Rgd5lJz3PicJtxuK3YU8zYUyzYUyw43JbubZvLjNkiowofDqX1wbN7pdRftNbfOaybK3UlcA3xj/q7WutVPY7ZgH8AxUAF8HWtdUApdQ5wOxBI7P+q1vqQ9dpz5szRK1asOJww+10sFKL8CxcT3LKFgt/9Fve55/bLfeuDYU5Zvon2SJTnZ45ljsfZ53ssfXobH79ZQfGkdD7zHekPKoQQ+6OUWqm1nnOE9ygG3FrrT/oprAE3lMrSgRKOhfnrx3/ln5/8E4Crp17Nt2Z8C7PBnOTIhpZ44vkMTfc/QKS2FgwG3OeeS8Y3voFt/LhkhzekBKIxlrZ5ebu5g4Utnezw7xmlNttiYkF6CielpXBCWgq5Vvk5O1zRSIzOlgCdTQE6mv10NAXobAngbYkvu9qCHCytsaeYcaXZcHosOFKtOD1WnB5LfJlqxeG2YEsxY5Q+tv3mQGXpIb92OYLkMw24DjgWKAAeAU7occqVwCat9RVKqV8ktv8O/BK4SGu9Syn1MHAG8OrhxJAsBouF/HvvYecll1J72+3YZ8/ulzmvcqxm/jChmK+s28m3N+zi7bnj+zQ1C8Axnx1N3Y52Kja0sPK1Xcw5t+SI4xJCCLGHUmoKsCCxuTCJoYh9VHVW8dPFP2Vt41qy7dncfeLdzMubl+ywhhQdCtH2zLM03X9/PPE0mfB84SIyr7kGS3FxssMbMuqDYd5u7uCN5nYWtXjxx+K1nBalOCHVxSkZbk5JT2Gi0zbim1v2RdAfoaPRT1uDj/ZGPx2N/viy2U9X64ETTKvDRHqBi5R0GynpNlxp1sQrvu70WDGaJbEcKg7WB/RtrfVpSqlG9q6sVoDWWmcf4t7zgMVa6xCwUymVopSyaq13fy10MvGh6gFeBK4nnoCuB1KVUhXE+5s29vmphgDbxIlkXfddGn/7O2pvuJGihx5EGY78B//MTA9XFmTycHUTN2yp4i+T+jagkNFo4MyrJ/P4nctZ9uIOcss8FI6X4eWFEKI/KKX+D7iR+AB+AD9VSt2ltf5bEsMSwGvlr3Hb0tvwhr2cVHgSvzz+l6TbZH7s3boTzwfuJ1JTC0ZjPPG89losRUXJDi/ptNZs9gV4tbGd15raWdPp7z6WZzVzcUYap2e4OT7VhbOPlQMjTSQUpb3RT1u9j7YGH211iWWDn4A3vN9rLHYTGYUu3Bl2UjJtuDPsuDNt3QmnxS5NmY8mB/u0vpRYHm4TpAygtcd2G5AO1O7n+O5jAP8GXiPe13SN1vqAbYGUUtcQb+JL8RD8Vi7jqqvwLlpE19KltP7nv6R/5cv9ct9byvJZ2urlqfpWTs1w8/k+Ts3iSrNxxlWTePHPa3jjH+u59Ka5OD0yap0QQvSD7wEztdYNAEqpLOB9QBLQJAlEAtyz7B6e3vo0JoOJ6+dez5cmfklqpRJ0LEbHy6/Q+Kc/xft4SuLZTWvNx50+Xmls59XGdrb3aFo7PcXOmRkezsx0M8Vll5+n/Qj6wrTW+Wip7aK1tqt7vbMlsN9+mA63hbwxHjxZdjxZDjzZdjxZdtyZdmxOabo8nBwwAe0x96cPaE/UZKKUshCvmTyUFiC1x7YnsW9/x3seux+Yp7WuVEr9XSl1sdZ6vwM4aK0fAB6AeL+VXsQ0qJTRSP4997Lzwgtp+O1vcc4/DuuYMUd83/jULKM4Z+UWrt9cycwUB6WOviWQxZMymHNuCSteLueNh9Zz4fdnYJA270IIcaQ6diefAFrrRqVURzIDGskqOyr54aIfsqllE8Upxfz65F8zKWNSssMaErTWeBctovH3fyC4eTMohefCC8n8zv+N6MQzpjUrO3w839DKK43t1ATjNXImBSenpXBuloezMj3Sl7OHcChKa20XzdVdtNR4aa7poqXaS1d76FPnmqxGMgtdpOU4SM1xkJrrIDU7vm6xSS3mSNGbT/ol4BT2DC1vJt5k9thDXPcRcIdSygzkAd4ezW8BFgHnAqsTy0WJ/VH21Iw2sqdm9KhkKSwg5+abqb3hBmqu/ykl/3sMZTny0WcnuezcOqaAG7ZUce36cl6cPRZrH5v4zj2vlLrt7VRtamXZizs59rNlRxyXEEKMcG8qpR4iPsgexMc3eF0pNQlAa70hWYGNNG9XvM3Pl/ycznAnZ446k9vm34bL4kp2WEOCb+VKGn77O/yr4mNDuk47jazvXYdt3MgcXGh3TefzDW281NBGdSLptBsU52R6ODfLwxkZ7hE/Yq3WGl97iMbKTpqqvDRVemmu9tLW4PtUjabVYSKvzENanpO0XEf3MiXNJgNgil4loFattW/3hta6KzGC7UFprVuVUvcRTyw18D2l1AzgDK31r4GHgX8qpRYDVcDXEpfeDLyjlAoQb5p7b+8fZ2jyfPZCvO+8Q+ebb9L41/vI/sH3++W+V+ZnsKS1k5cb2/nl9hruGFvYp+sNBsUZV03m8TuXsfK1XeSM9lA6LbNfYhNCiBHqssTytH32X068LJR5PgZYOBbmT6v+xMPrH8akTPxs3s+4fMLl0kQSCO7YScNvfoP3nXcAcMybR/YPf4B9xozkBpYkm7r8PF3XyvMNbVQE4vUsNoPivCwPF2SncnqGG6dxZPbn1FrT0RSgsaKTxooOGnbFk859+2gaTQayilJIz3eSke8ivSC+dKZa5HdOHFBvpmFZTTxpbExsZwNvaa2nDXx4vTfUh46PtLay44ILiDa3MOo/j+CYNatf7tsejnD6ii1UBkL8vyklnJOV2ud71Gxr4/nffYzJauTiG+aQmu3ol9iEEOJo1R/TsByNhnpZeigNvgZ+sugnrGpYRY4jh9+c/BtmZM9IdlhJF2ltpekvf6X18cchEsE6YQLZP/4xzuPnj7gkoT4Y5tn6Vp6qb+UTb3wgIYtSLEhP4cLsVM7K9OAagYMIeVuD1Je301AeTzYbKzoJ+vaeBdHmMpNZ6CKzKCWxjDellS5c4kAOexoW4E/A+0qpfye2vwLc3Z/BjQSmtDTy77yTymuupeb6n1L63HMYXX2fx3NfHrOJ+yeN4oKPt/L9TZVMSXFQZOtbE9/8ManM/8IYljyxlVf/vo4v/HQOZuvI++MrhBD9JfFlbXdrIa11RRLDGRFW1q/kR+/+iOZAM/Pz53PPifeQZhvZo7zHgkFa//Mfmv5+P7HOTkzZ2WR9//t4LrwANYJq9nzRGC83tvFUXSuLWzuJJfbPT3XxhZw0zsvy4BlBzWvDwSgNuzqo39lBfXl82dUW3Osce4qZ4skZZI9KIas4hexRKThTrSPuCwsxMHozD+g/lVI7iPfTBPiG1nrRwa4R++c66STSLv8irY8+Rv0vf0n+vff0y31neZzcODqf27fX8K315Tw7cyzmPravn3ZKIfU7O9i6vJ6F/9nEGVdNkj8yQgjRR0qpU4F/ATnExzSwAM3AoaYuE4dJa83jmx/n3mX3EtERvjX9W1w77VqMhpGTYO1La03n62/Q8OtfE66uRjkcZF73XTKuvBKDY2S0ctJa83GHj8fqWni2vhVvNJ52jnPYuDg3jc/lpFHYxy/sj1ZdbUFqt7dTu72Nuu3tNFZ60bE9LSDNViMF41PJKfGQU+Imu0SSTTGwevV1j9b6XeDdAY1khMj+yU/oWraM9uefx3nC8XjOP79f7vvNoiyWtHbyTksn9+6s5eay/D5dr5TilC9NoKXGy9bl9eSUuJl+2sgdBU8IIQ7Tr4n3/3wcmAV8HShJZkDDWTAa5I4P7+C5bc/hMru4+8S7WVC0INlhJVVgyxbq77wL30cfgcFA6sVfIPO738WcPTK+A2kMhXmqrpXHalvY4gsAkGYy8o3CTC7JTR/2U6ZorWlv8FOztY3qra3Ubm2PT3vSQ3q+k9zRHnJK3eSUuEnLc2KQgYHEIDpkAqqU8gA/BWawd3OiUwcurOHLYLdT8NvfUn7xJdTdehv2GTP6Zbhzg1L8aeIoTl++mb9UNDA/1cWpGe4+3cNsNXLON6fy5N0reP/pbWQVu8gfO7KbLwkhRF9prbcopcw6PsjCQ0qpFcQH2DsopZSf+AjyAI9orf/R45iN+Mi6xUAF8HWtdSDRPWb3RNjTga9orV9USu0EdiX2v6m1vrNfHm4Iqeuq44fv/pB1Teso9ZTyx1P+SKmnNNlhJU20o4PGP/+F1kcfhWgU+5zZ5N50E7aJE5Md2oDTWvN+m5d/VTfzalMbEQ0KOCU9hS/mZXBWprvPMwUcLbTWtNb5qNnSSvXWNmq2tuHrMf2JyWygYFwquWUe8spSySl1y5yaIul6UwP6T2ADMA74OXAVsHIggxrubOPHk3399dTfcQfVP/oxJf/9D8p85H8MMi0m7ps0ii+s3sZ3Nu7izTnjKehj8xJPloPTvzaJl/+6ltceXM+lN87Fmdq3OUaFEGIE2z1EZLVS6nygnN5PJ1attV5wgGNXApu01lcopX6R2P671vorAEopK7AJeCNxfvQg9zrqraxfyQ/f/SEtgRYWFC3g7hPuHrFTrOhYjLann6bxd78n2tqKKTub7Ouvx33eucO6pg+gJRzhidoWHqlpZrs/3oexyGbhirx0Ls5N7/P/gY4WnS0Bqja1UrW5hepNrXvNt2m2GSmenEHBuFTyx6aSNSoFowwSJIaY3iSgY7TWFymlLtRaP6aUegZYONCBDXdpV1xO1/vv4124kMY//4XsH/6gX+47P83F9aW53LOzjmvWl/PszDFY+vitX8nUTOaeV8Lyl8t57YF1fPaHszCa5I+XEEL0wh+VUmnEazwfAzxAb//A5yqlFhHvM/pDrXV5j2MnA79KrL8IXA/8vcfx84C3e8y3rZRSC4Eg8DOt9erDeJYh6ektT3PHh3cQ0RG+Pf3bXDv9WgxqZJZRgQ0bqL31NgJr16LMZjK+8Q0yv3ktBueRD3I4lK3q6OKfVU282NhGMKYxAGdnuvlKfiYL0lMwDLPEOxSIULWplcoNLVRuaqG9wd99zGw1MmpqBgXj0igYl0pmoUtGpRVDXm8S0N2FWUgplQ60AlkDF9LIoJQi76472XnBhTQ/+CDO+cfhPPbYfrn3daNyWNHh463mDn65vYZf9nF+UIC555XSsKuTXZ80897/trDgivHD/ptUIYQ4UlrrxxKry4Exfby8RGvdpJQ6i3hz255ziWYQL38hPkf2vrWqXwL+3GP7mMS9pgP/BaYc6E2VUtcA1wAUFxcf6LSki8ai/Hblb3lkwyM4TA5+e+JvObV4ZPYGinq7aPrzn2h55D8Qi+E86URyb7wRS0lJskMbMKFYjJca23moqpFVHfHp6XMtZq7IT+eKvAzyh1Ftp9aapiovFeubqdzQQu22dmKJQYMMRkX+2FQKJ6RROCGd7BKp4RRHn94koFsSieejwIfECz5pgtsPTGlp5P/qXiqu+np8apbnn8OUduR9Lg1K8eeJxZyxYjMPVjUxx+Pkwuy+3VcZFGdcNYmn7l3JhiU1ZBa6mLqg74msEEKMJEqpnwIPaq1bEtsZwFVa618f6lqtdVNi+bpS6q/7HG4BUhPrnsT27vdMBabSY7DAHvdao5TyKaXStNa7E9h93/cB4AGIzwN66KccfN6Ql+vfu57F1YvJd+bz59P+zLi0cckOa9Bprel8803q77yLSH09puxscm66iZQzzxi2XxI3hsL8u7qZf9U00RCKz0t5XKqTrxdkcXamB9MwGTxndy1n+bomdq1rxtexp1ltao6D4snpFE/KIH9sqkyVJ456vZmG5UuJ1d8ppZYRLwBfG8igkik+ZgSD9ofcedxxZFz9dZoffIjam26m8K9/6Zf3TjObeGhyKRes2soPN1UyyWlnrNN26At7sDrMnPutqTx1zwoWP7GV9DwnBeNlUCIhhDiIL2qt7929obVuVkpdTnx03ANSSrkAv9Y6qpSaBjTtc8oi4tOhrU4se06HdgnwTGLQo939QVVikKIC4uV225E8VDJVdVbx3Xe+y7a2bczImsEfTvkDGfaMZIc16EJV1dT/8pd4Fy0Cg4G0r3yZrOuuw+gann1fN3X5+XtFI8/UtxLSGptBcXleOl8vzGKyy57s8PpFR7OfXeuaKV/bRNWWVmKR+P9BzVYjpdMzKZ6cQfGkdNyZw+N5/z979x0eVfE1cPw7W5Nseu8VCL1LU7CgoliwN+y9Yu+F196xoNjbT1ERxa6AYkEUpIReQiCk955ssn3ePzYgKkhIdjcbmM/z7HO33jmh7OTcmTmjKDt1aBsWIUQ0sHN+6DIppcN7IXWfeks99/9+P6PiR3HhgAt91m7MtGmYl/1Jy08/Uf/BbCIvOH/fH+qAoaFBPNw7iTu3lnD5xgK+G9Eb035uPB0Rb+KYywbw7ax1zH99A2fePVJ9ESqKouzdnq4gdqSv7Q+8JoRoBiRwlRBiKHBM++jpu8DbQojfgBLgkt0+ez5w3W6PY4EvhRBmQAtctTM57WlWVa7i5p9vpt5az0mZJzF93HSM2oOrMJ50Oqmf/SFVzz2HbGsjYOBA4h/8PwIHDOju0DxuZzXbV4qqWVTXBECCUc8lSdFMTYgiytChX1v9lpSSujIz+WuqyV9TTU1xy67XQqMDSB8UTfqgaBJ7h6PVq2m1yoFL7KtPEkKchntqzircHetQ4Eop5RfeDm5/jBw5Uq5cubJL5yhtKeXMr86kzdnGB5M/YECU777cbUVF7DjtdFxWK+kffkjgoL0u19kvUkpu2FzEp5X1nBEXwcx+qZ0aYc1ZUMjSz7cTlWTitNtHYAjo2Z2AoijKfxFCrJJSjuzE5z4F/gCew91n3gwcJqU81cMheoUn+lJP+Xr71zzwxwM4XU5uHH4jlw689ICdZro31u3bKb/3PtrWrEETFETMLbcQce45iP28mOzvHC7JN9UNzCquYl2zu8BOf1MA16TGMiU2fL+LKfoT6ZJU7GjalXQ2VbcXEBKQkBlG+hB30hkRH3TQ/ftWDnx760s7koBuBqZIKbe2P+4NfCWl9KuNpTzVaS4sWMitv95Kakgqn5z0CSa97yrJNc1fQOlNN6FPTiZj3mdoQ/dvH8+9MTudTF6VR67ZwpN9krkoKXq/zyGl5Ie3N5G3opLMYTEcd8VAxAGy7kJRFOWfupCAJgIfAONwj2T+AZwvpSz3cIhe4Q8JqJSS19a9xstrXiZAG8AT459gYtrEfX/wACLtdmrfeoual2ch7XZMhx1GwoP/hz4pqbtD8yiry8XcinpmFlZSaHGveTw8IoRrUmM4PCKkxyZk0iWpLGhi28oqtuVUYW5w1/PUaAXJfSPIHBpDxpAYgkIPnMJJirIne+tLOzKMZdmZfAJIKfPaN8s+IB2bfixnlp/J3K1zeXjZwzx+2OM++wIMPW4SrVOnUj97NuX33kfSiy94pG2TVstbA9OZtHIr9+eVMjA4kBFh+5dYCyE46oK+NFS2kr+6mpXfF3DICQfvht+Koih7IqUsA44SQpjaH5u7OaQexe6y8/DSh/l82+dEBkTy8sSXGRjtmRlBPUXbxo2U33sf1i1b0ISFEX/3XYRNmdJjk7E9aXW6mF1Wy6ziKsqtdjTAaXERXJsSw8CQoO4Or1OklFQVNJO3qpLtq6poqXcnnTq9hqxhMWQOjyFtYDTGQDWDTFE68r/gSyHEvbhLwgvc606+EEIE4h5BbfVmgN3hjkPuYHXVar7N/5axCWOZ0muKz9qOvfMO2lavpvmHH6h//wMiL7zAI+ftFRTAi/1SuWxDAZdtKGDhyD7EGvX7dQ6dQcvkawbxyeMrWf71DiITTGQNj/VIfIqiKAcSlXjuvxZbC7f+eit/lP1BRlgGsybOIjnk4Km+Lu12al55lZrXXgOnk5BjjyX+/vvQxRw4O981O5y8U1rDa8XV1Nod6IXg/IQork+LJT2wZ67tra8ws3V5JVuXV9BUYwFAq9eQOSyGXiNiSRsYpZYtKco/dGQKrus/XpZSSr9YiODpaUPb6rdx7rfnIoRgzolzyAjz3Wjf39eDziZw0CCPnfvx/HJeKKxkdJiJuUOzOrWuonx7I188l4NGCE69bTixaZ6ZKqwoiuIvOjsFt6frrim4FeYKrlt0HVvrtzIibgQvHPkCYcYwn8fRXax5eZTdeReWTZvQRkQQP306ocdN6u6wPKbF4eStkhpeKa6iweEkUCM4PzGKa1Jie+T+neZGK3krKtm6vJLqombAPb02dUAUvUfGkj44WiWdikIX1oD2FN7oNOdunctDSx+ib2RfZk+ejUHruy/JpgULKb3xRvRJSWR8Ps9j60GdUnL+unx+rmvmkqRoHu/TuavLuX9W8OM7mwgKM3DmXSMJjti/LV4URVH8mUpAfWdr/Vau+fEaqlqrmJwxmYcPfdin/W13kk4nde++R/ULLyBtNoKPnkjCgw+iizowtpkxO5y8XepOPOvsToK0Gi5NiuaqlBhiDPs3C6u7OexOdqypYcvScoo317Hz1+eEXmH0GRVPrxGxBJh61s+kKN7WlTWgB60zep/BsrJlLCxcyIxVM7hr1F0+azt00rG0nn8+9R98QPm995L04oseWf+hFYJX+qcxaeVW3imtYXBIIOcm7H9Hlz06nobKVlZ+V8C3s9Zx6q3D1dU+RVEUZb+srFjJtJ+m0Wxv5vJBl3PDsBvQiJ5b8XR/2IqLKbv7btpWrkITEkLCww8RevLJB8Raz1ani/dKa3ipqIpau4NAjeDalFiuTY0lugdtpSKlpKqwmS1/lJO3shJrq3sXwogEE9mj4+g9Mk5tTaconbDPbwEhxBDgVWAIsGuCvr9MvfUmIQTTx01nQ80GZm+ezej40RyZeqTP2o+943ba1qyh+YcfqXvvPaIuvtgj5w3X63h3UAaTV+Vx19YS+poCGRa6/4v+R52UQUNVK9tWVvHD25s4/upBaFRlXEVRDmJCiGzgPiCL3fpYKeWobgvKTy0qWsQdv96B3WXn3tH3ck7fc7o7JJ+QUtI473MqHn0U2dqKadw4Eh59BH1CQneH1mV2l+TD8lqeLaigyuYgQCO4KjmG61Jj97vuRHdqa7GRu6yCzX+UU1fmXs5tDNIxcEISfcclEJvWcyv0Koo/6Mga0N+BB4AZwHG4N7tullI+6f3wOs6b04bWVK3h4vkXY9KbmHvSXBKDE73Szp7YiovZcfoZuMxm0t57l6CRnpsR9mVVPVdtLCTRqGfByD6dmg7jsDn54rnVVO5oYujRKRx6Rm+PxacoitJdurANyzrgfdx7Zzt3Pi+l/NWD4XmNr6bgzt06l0eWPYJWaHli/BMcm36s19v0B87GRsqn/x/N8+cjAgKIveN2Is49t8cnMy4p+aqqgSd3lLOjzYZeCC5IjGJaWhzxPSTxlFJStrWBjUvK2L66CpdDgoDUfpH0HZdAxpBodPoDfuxFUTyqK/uArpJSjhBCrJdSDmp/boWU8hAvxdop3u4039nwDjNWzWBw9GDePe5d9FrffaE2//ILJVdfgzYmmozPPkMf67nKsw9vL+PloirGhJn4pJNFiVqbbMx9YgUtdVaOmJrNgPEH1j5liqIcfLqQgOZIKYd7IyZf8HZfuvsenya9iReOfIHRCaO91p4/MS9fTtkdd+KoqMDYrx9JzzyNMSuru8Pqsl/rmnl0exnrWtoQwOlxEdyeEU9aD6lq29ZiY8vSCjYtKaOh0r2xQ3CEkX6HJtJvXAIhkarGhaJ0VlfWgDraj3Xt03FLgGhPBtcTXDTgInIqc/il5BdmrJrBnaPu9FnbIUccQfS111Az6xVKb76FtHffQeg9kwDfk5nAhuY2fq1v5r68Up7sk7zfV2KDQg2ceN0QPntqFYs/2kpodCAp/SI9Ep+iKEoPM18IcbyU8vvuDsTfOF1OHl/+OHNy5xAVEMUrR79Cv6h+3R2W10m7neqZL1H7xhsgJZGXXELMzTehMfTsQksbmlt5aHsZi+tbAJgYGco9WQkMCO4ZayKrCptY/3MJeSurcDpcCAHpg6MZMD6R1AFRakmRonhRRxLQOUKIKOBxYAmgxT0l96CiERoeOewRzvr6LD7Y/AHD44ZzTNoxPms/+rrraFu3HvOSJVQ98wxxd9/tkfNqheDVAWlMXrWV/5XV0tcUwKXJ+7/nWFRSMMdePoDvZq1j/mvrOe32EUQlBXskRkVRlB7kR9z7Z7sAK+79s6WU8qDeNNnutHPPknuYXzCflJAUXjv6NVJCU7o7LK+zFRVReuttWNavRxcTQ8ITjxN86KHdHVaXVFrtPLGjnI/L65DAiNAg7stKZGy4//f5TruLbTlVrP+lhModTQCYwgz0H59E/0MTVEV/RfGR/dqGRQihBwKklM3eC6lzfLVuZUPNBi74/gICtAHMOXEOqaGpXm9zJ0d9PQWnn4G9rIykGc8SOnmyx86dZ7ZwQs5WzE4XHw3OYkJkSKfOs2FxKb9+mIsp3MgZd45QX+aKovRIXZiCuw24C8jh72tACz0Yntd4oy9tc7Rx6y+38lvpb/SN7MsrR79CdOCBP5Gqaf58yu+7H1dLC8FHHUXCo4+gi4jo7rA6rdXp4tXiKl4qqqLV6SI1wMD9WYmcGBPm92tYzQ1W1v9awqYlZbQ12wFI7B3OoCOSyRgajVZ7cFReVhRf68oa0AuAb6SU9e2PI4HjpZSzvRJpJ/ly77IPN3/I48sfp29kXz6Y/AFGre/WObRt2EjheeeBTkfGnI8x9vZc0Z+faps4f10+ITot34/oQ2ZQ536upV9sJ2d+IVFJJk69bQTGwJ5Tcl1RFAW6lIAu78kVbz3dl7bYWrj+p+tZVbmKoTFDefnolwk1eGZfa3/lslqpevJJ6j/8CPR64m6/nYgLzvf7JG1vXFLyWWU9j+WXU261E6LVcHN6PJclR2PsRN0IX6oubmbtomLyVlTickp0Bg3Zo+MZdESymqWlKD6wt760I98ct+1MPgGklHXAbZ4Mrqc5t++5HJt2LFvqtvDE8id82nbgwAHEP3A/srWVkhum4Wxp8di5j4oK5f96JdLocHLh+nwa7Y59f2gPxkzJpM+oOGpLzcx/bT1Oh8tjMSqKovi5L4QQVwshIoUQQTtv3R1Ud6i31HP5wstZVbmKcYnjeO2Y1w745NNWUEDBOedS/+FH6FNSSP/wQyIvvKDHJp9rm1s5KSePGzYXUWWzc0lSNEvH9Ofa1Fi/TT6lS1KwroYvnlvNJ4+uIHdZBUGhBsad1ouLnziUI6b2VcmnonSzzg5NHdR1qIUQPDjuQXLrc/l066cMjx3OSVkn+az98DPOoG3tWhrmfkrZXXeR/OKLCA91BFckx7DFbOHD8jqu3lTI+4My0e3nQnwhBEdd2A9zo5WSLfX8/MEWJl7Ur8d2wIqiKPvhkfbjLEDSvgaUg6zfrGqt4sqFV7K9cTtHpx7NkxOexKDt2UV39qXx22+peGA6LrOZkEmTSHjkYbQhnVvO0t1qbQ4ezy9ndnktEjgiIoSHeifRx+S/y2qcdhe5f1aw+oeiXdVsY9NCGHpMKpnDYtQ0W0XxIx1JQCuEEKdJKecBCCFOB6q8G5b/CzYE8+zhzzL1u6k8vOxhsiOz6RPRx2ftx913H5YtubT8uIiaWa8Qc/11HjmvEIIn+iST32rl57pmHt5exoO9939bFa1Ow/FXDWLeMznkLqsgJDKA0SdneiRGRVEUfyWlPOh/yy1uLuaKhVdQ2lLKyVkn8+C4B9FpDtylGNJmo/KJJ6n/8EOEXk/cA/f32L09HS7J/8pqeHJHBY0OJykBBh7qlchx0f67ztPW5mDDb6WsXVRMa6MNBGQOjWHo0SnEZ/lv3IpyMOvIGtC+wJf8dfXWAUyRUuZ6Obb94ss1oLv7YtsX3P/7/aSEpPDxiR/7dHqRvbKSHWecgbO6hqSZLxJ6jOeq8tbYHBy3KpcSi52ns5O5ILFzBSOa6yx89uRKzI02tUeooig9RhfWgO5xuq2UsrXrUXlfV/tSp8vJaV+dRn5jPuf1PY87R92JRhy4Obm9ooLSG2+ibe1a9CkpJD3/HIEDBnR3WJ2yotHMXVuL2dhiIUAjuCE1jmtTYwn005HD1iYb634qZv2vpdjaHGh0gr5jExh2dCrhcQflrHdF8TudLkLU/mEtkN3+MFdK6fyv93eH7kpAAR5Z9ghzcucwIXkCM4+a6dPOtnX1aoouvAj0etI/+oiAbM+Nwm5uaeOknDzaXC4+GJTJkVGdS66ri5v5/NkcHFYnk64cSNawg3o3AkVReoAuJKAu/j71FgApZY+YguuJvnRV5SqWVyzn6sFXH9CjT+Zlf1J66604a2sJPvxwEp96Em1YWHeHtd/q7Q4e3V7OB+W1AJwQE8b/9UoiJcA/p0y31FvIWVjEpiVlOO0uDAFaBh6exOCjUjCF+a4opKIo+9alBLQn6M4E1O60c/GCi1lXvY5rh1zLNUOv8Wn7DZ99Rvm996FPTiZ97iceLfP+c20T56/PJ1Cj4evhvenXyQ2mS3Lr+XrmGgBOumEoydk9txS9oigHvs4moP84RwBwHhAtpXzKM5F5V3f2pT2FlJK6t9+m6tkZICUx024g6qqrPFaLwVeklHxaWc//bSuj1u4gLcDA432SOaqTF5u9ram2jdULitj0RxkuhyQwRM/Qo1MZMCFJVdtXFD/VlSq4yj7otXpmHD6DqIAoZq2dxa/Fv/q0/fDTTyfi/POxl5RQesstSEfnqtfuyZFRoTzRJ5kWp4vz1+VTabV36jzJ2REce9kApFPy3SvrqC7yu61kFUVRPEpKaZFSvg2c2d2xKJ7hbDFTOu1Gqp5+Bm1ICCmvv070Ndf0uOQzz2zh9DXbuWFzEU0OJzelxfHLqL5+mXw2Vrfx8/ubmX3/MjYsLiXApOewM3tzwaPjGD4pTSWfitID9axvTD8WZ4rj2SOeRSd03P3b3RQ2+XbP8bg77yBo9Ghaly6j6umnPXruCxKjuTYlllKrnQvW52N2dm4GdtawWA4/Lxu7xcnXM9fQUNUjlkQpiqJ02O5brwghgoUQRwLh3R2X0nW2wkIKzjmb5h9+IKB/f9I/+4zg8Yd1d1j7xeZy8cyOCo5akcsfDS2MDTex6JBs7spM8Lu1nk21bfz0/mZmT1/Gpt/LCQozMOGcPlzwyFiGTExBb+gRs9oVRdkD//q26eFGxI3gtkNuo9nezE0/30Sr3XcJltDrSXr+OfRJSdS99z8a5n3u0fPfl5XACTFhrGtu47pNhTg7OXV7wPgkRp+cSVuzna9fXIO50erROBVFUbpZC9DcfqwHXgamdWtESpe1LPmdHWeehW3bdsJOOYW0jz7EkNyziurlNJo5ZuVWnimoIESn4YW+qcwb2svvtlZpqbfy60e5zH5gGZt/L8cUbuCIqdmc/9BYBh2RjE6vEk9F6en2OW9BCBELXA/02v39UsqzvBhXj3Ve3/PYULOBb/K/Yfof03lqwlM+K8Kgi4gg+eWXKDj3PMqnT8eQmkLQyC4tYdpFIwQv9Uuj3LqN+TVNPLStc9uzAIw4Po22Zhvrfi7h65lrOfWWYRiD9B6JU1EUpTupbVgOLFJK6t57j6qn3DOL4u6+i4gLL+xRxZXMTidP5Vfwekk1Ejg1NpyHeycTbfCvqautTTZyFhSy4ddSnA4XpjADIyen0+/QRLQ69d9KUQ4kHfn2+RLIAX4E/K76rb8RQvDA2AfIq89jfsF8siOzuXzQ5T5rP6BvX5KefoqSG6ZRcv0NpH8yB0NqqkfOHajV8N6gDCavyuO1kmqSAwxckRKz3+cRQnDYmb1pa7GTt6KSb2et46QbhqI3qquaiqL0TEIIo5TS2tO3YVH+4rJaqXhgOo1ffokmLIzk52ZgGjeuu8PaL7/VNXNrbjFFFhsJRj1P9knm2Gj/qtRrbXOwemEha38qwWF1EhiiZ/ikTAZOSEKnptkqygGpI/uArpVSDunUyYW4GLgSdyn6G6SUObu9FgC8BaQCRcBlUkpLe+f9IpCBe+/RU6WU9ftqy98q95W2lHLuN+fSYG3gxaNe5IiUI3zafu1bb1H19DMYMjNJ//gjtKGeKyyw1Wzh5Jw8Gh1OXh2QxpTYzlW0dTpcfPfKeoo21pLSL4ITrh2CVq+uciqK0v32twquECJHSjn8H9uw7CQPpm1YDgT2ykpKrr8By/r1GHv3Ivnllz12MdcXWhxO/m9b2a6tVS5MjOK+rERCdf7zz9Bpd7FhcSkrvyvAYrZjDNIx7NhUBh2RjCHAv0ZnFUXpnK5Uwf1TCDGoEw1G4F73cgRwPu6kcncXA1uklOOB3PbHANOBT6SUE6WUR3Qk+fRHScFJzDhiBlqh5c7Fd5JXn+fT9iMvvZSw00/Dlp9P6U03Ie2dq167J31MAfxvUAZGjeCGTUUsqe9cRVutTsPxVw0ksXc4xZvrWfDmBpxOl8fiVBRF8RUp5fD2o0ZKqW0/7rz5z2/9yj61bdxIwZlnYVm/nuCJE0n76OMelXwuqW/miBVb+KC8lvRAA/OG9uKp7BS/ST6lS5L7ZwWzpy9jydw87DYnwyelcv7DYxlxXLpKPhXlINCRBPRVYLEQYp0QYvnOWwc+Nwr4TUppk1LuAEKEELvvEHw48E37/a/bHwMcDRwnhPhFCPFgB38OvzQyfiT3jrmXVkcrN/x0A/UW3+XSQggSpk8naNQozH8speKRR/Hknq+jwoN5pX8aDim5ZP0ONrW0deo8OoOWE64bTFxGKDvW1rDo3c24XAfG3rSKohy8hBCG3Svidnc8Ssc0//gjhedfgKOqiqirriJ55otog03dHVaHmJ1O7tlawhlrtlNisXNpUjSLDslmXERwd4e2S9HGWuY8toIf39lES72FfocmcP5DYxh7ai8CTKoWhKIcLDqSgH4APArcBNy+221fonBXANypAYjcy+u7vzYQ+Ak4EugvhDhubw0IIa4UQqwUQqysrq7uQEi+d0afMzi377mUtpRy66+3Ynd5biRyX4TBQPKLL2BIS6Nhzhzq//c/j57/+JhwHu+TTLPTxblrt1NssXXqPIYAHSdeP4SopGDyVlTy6+wtHk2WFUVRfEUIcZoQogSw8Fc1XLXxsZ+TUlL71luU3DAN6XCQ8MTjxN58U4/Z33N5QwtHr8jl7dIakox6Ph2axWN9kjFp/WPUs7asha9nruHrmWupLWkhY0g05zwwmqMu6EdwhH9V4VUUxfs68s1qkVI+I6X8SUr5685bBz5Xx9/3Pgtrf25Pr+/+Wh0wX7ozkAXA4L01IKV8XUo5Uko5MiZm/4vh+Modh9zB6ITRrKhYwZPLn/Rp29rwcJJffQVNWBiVTzxJ888/e/T8FyVFc3NaHJU2B+et3U6d3dGp8wSY9Jx841DC44LY9Hs5v8/dppJQRVF6oqeAswDdblNx/SMLUPZI2myU338/VU8/gzYsjLR33ib8lFO6O6wOsbpcPLy9jCmrt7GjzcbUhEh+GdWXwyJCujs0wF3Z9pcPc5nz8HKKNtYRmxbCqbcNZ/I1g4lM6Bkjy4qieF5HEtD5/zUK+R/+BA4TQuiFEKlAi5Ry900ffwUmt9+f3P4Y4Bdg52LVkcC2TrTtV3QaHc8e/iypIanMyZ3DnC1zfNq+MSOD5BdeAK2W0ltvo23DRo+e/46MeM5NiCSv1cqF6/Jp7eQ6zqBQA1NuGkpIVABrfypm+dc7PBqnoiiKD9RJKf+QUu73F6EQoq19+ckvQojL/vFagBBithDit/ZjQPvz7wohVrd/Zu5u7z9OCLG0/Tap6z/WgcnZ2EjRFVfS+OlnGDIySJ/zsce2L/O2rWYLJ6zK4+WiKmINOmYPzuTZvqmE+MFaT4fdSc6CQmY/sJSNi0sxhRs55tL+nHHnSBJ7hXd3eIqidLOOVMGtxj1dthmw4q7sJ6WUsfs8uRCXApfjrgh4I+AAjpFSPi2ECATeBpKBEuCS9iq4acAbQACQB1zRkY68J1Tuy2/I57zvzsPisPDK0a8wNnGsT9tvmPc55ffcgzY6mvSPP/boJtoOl+TiDTv4sbaJo6NCeWdgBnpN5/ZJa6xu5fNncjA32hh1UgaHnJDhsTgVRVE6ohNVcHeu87wR97KSObin4QId24ZFCLFNStlrL69dDcRIKR8WQjwAVEkpXxVCvAu8KaVcstt7tcBqYEL7U78Cw6WU+9xKrSf0pZ5iKyml+MorseXnEzR2DMnPP482zL+2KNkTKSXvlNbw0PYyLC7JCTFhPJ2dQqS++4v3SCnZsbaG3z/No6nGgs6oZcSkNIYcnYJebamiKAedvfWlHUlA0/b0vJSy0EOxeURP6TQXlyzmhp9uIEgXxPvHv0+viD3+ruE11bNmUfPiTPeV3o8+RBse7rFzm51OzlmTz4omM6fGhvNS/zS0ndysu77CzBczVtPaZGP0yRmMnKySUEVRfKcTCeget1/hr4u2+/ztWwjRAqwCaoFbpJQFu732EfCUlHK1EGIYcIeU8tz2BLQ/7gvEL0kp5wghstvfO6X9s1+2vz93L+1eiXvLNFJTU0cUFvpV9+4VbRs2Unz11Thragg743QSpk9H6P2/CE61zc5Nm4tZVNeESavhkd5JnBMfiehkX+tJ9RVmlnySR9GmOhDQb2wCo6dkYgoz7vvDiqIckDq9DYuUsnBPN++EeeCbkDyBu0bdRYu9hesWXUdNW41P24++5hrCzzwD244dFF97HS6rdd8f6iCTVsv7gzPobwrg86oG7tla0ul1nBHxJqbcPIzAUAN/frWDVfMLPBanoiiKp+1t+5X9XAOaLqU8HHgN9z7Zu9tb4b7bpJSjgCnAXUKITPZdBPCfsfeIegqe0rJ4MYUXXoizpoboaTeQ8PDDPSL5/KGmkSOW57KorokRoUEsOiSbcxOiuj35tLU5+P2zbXz80HKKNtURlxHKGXeO5KgL+6nkU1GUPdpnAiqEqBZCVP3z5ovgDlTn9j2XC/pfQJm5jGk/TaPN0bktTDpDCEH89OmYJoynLSeHsjvuRLo8t/dmuF7Hx0OyyAg08F5ZLU/sqOj0uSITTJxy0zACQ/Qs+yKfnAXquoeiKP5NCPF8R57bEyllTftxAfDP2Ud7LNy322fqgB+AIey7COBBq+HTTym+5lqkzUbCY48Rc+213Z7A7YvN5eKBvFIuWL+DeruDW9Pj+HJYb9IDuze5k/Kv/TzX/FCE0aTjqAv7cfrtI4hLD+3W2BRF8W8dKUI0Ejik/TYe976gM70Z1MHg1hG3cmTKkayvWc89v92Da//rVXSa0OlIfu45Avr3p3nBAqqefMqj54816pkzJIsEo54XCiuZVdT56xWRiSam3DSMgGA9Sz/fzuofijwYqaIoisdN2MNzh+/hub8RQgS3r91ECDEY+Of0mD0W7hNChLcfDcChwFbc9RMyhBChQohQIIMDoKBfV0gpqX5xJuX33Y/GaCTllVcIP+3U7g5rnwrarJyYk8frJdUkGfV8MawXt2ckoOtkjQVPqSs38+Vzq/nxnU1YWuwMOTqFqQ+Npd+4BEQ3x6Yoiv/b5xrQPX5IiGVSyjFeiKfTesoa0N212lu5ZMElbKrdxCUDLuGWkbf4tH1HdTUF55yLvbSU2LvuJOriiz16/q1mC6eszqPO7uTZ7BSmJkZ1+ly1pS18MWM1FrOdQ8/oxdCjUz0YqaIoyt91Yg3ombi3X5kI/LjbS2FAsJTy0H18fhTuqbfNuNeOTsO9fnRfhfsWAMGAHvhASvli+/kmA/e3n/5hKeV3Hfk5emJfui/S4aD8gek0zpuHNiaa1NdeI6B//+4Oa5++qKznttxiWpwujo8OY0bfFCK6udCQ3eZk5XcFrPmhCJdTktg7nMPPzSYyUW2poijKv3W6CNEeTpQJ/CilzPRUcJ7QUzvN6tZqzvvuPCrMFdw/5n7Oyj7Lp+1b83dQeO65OBsbSXz6acJOOtGj51/T1MoZa7Zhdrp4dUAaU2IjOn2umpJmvnhuNVazg7GnZTH82D3Wx1IURemyTiSgQ4BhwIPAA7u91AQsklI2eThEr+ipfeneuCwWSm++hZaff8aQmUnqG6+jT/JcBXhvaHW6p9x+UF6LQQim90rk0qTobp8qXLC+hsUfb6W51kJgiJ5DT+9Fn9Hx3R6Xoij+qytVcKtxX40F95RdPXCjlPJdTwfZFT2509xav5ULv78Qi8PCzKNmMj55vE/bb129mqJLLkU6HKTMepngCXuaQdZ5v9c3M3VdPg4peXNABsfFdL7MfXVxM1+9sAZLi11t0aIoitfsbwK62+ci29dj9kg9uS/9J2dTE8XXXEvbqlUEDBlMyquvoovo/EVQX9jWauHyDQVsMVvICDTw+oB0BoUE7fuDXtRSb+W3T7aSv7oagAHjExlzShYBJv8v3KQoSvfy1DYsDqCiI3uJ+VpP7zT/KP2D6xZdh16r561j32JQzCCftt+yeDHF116H0OlIffttgoYP8+j5f6pt4uL1O5DAO4MyODqq8wUKasta+PL5NbQ12RhxfBqjT85UV2AVRfGoToyA3iilfEEIscdF9VLKOzwXnff09L50J3tVFcVXXIk1NxfTYYeR/OILaIK6N5Hbl6+qGrh5SxFmp4vT4iJ4qk8ywbru2ztTuiQbl5SxdN42bBYnUcnBHHFeNvGZ/r9XqqIo/sFT27CU+mPyeSAYlzSOhw59iDZHG9ctuo7CJt9WfA2eMIHEJ55AWq0UX301ltytHj3/UVGhvDkwHYnksg07+LWuudPnikoM5tRbhmEKM7Dq+0L+mLe909u9KIqieIil/Wjey03xEVtREYVTz8eam0voCSeQMutlv04+bS4X9+eVcOXGAuwuyZN9knm5X2q3Jp/1FWa+eG41v36Yi9MpGXtaFmfdPVIln4qieMReR0CFEIuklBP/MQUX/tpUO9YXAXbUgXLV9u0Nb/PcqudICk7ig8kfEB0Y7dP26z6YTeUjj6CLiSHtw9kYUlI8ev5vqhq4alMBBiGYPTiLcRHBnT5XY3UrXzy3mpY6K4OOTGb8Wb3VSKiiKB7R2Sm4PV1P70stmzdTdMWVOGtqiJg6lbh770FoOlLwv3uUWWxcubGAlU2tJAfoeXNABkNDuy9ZdjpdrF5YxMpvC3A6XCRlR3DE1GzCY/03gVcUxX/t9xRcIUSClLL8H1Nwd5FS+tWmjD2909xJSslTK57ig80f0C+yH29PeptgQ+eTtM6onvkSNS+/jD41lfTZH6Dz8MbkX1TWc+2mQgK0Gj4enMmo8M7/fE21bXz53Gqaaiz0H5/IEedmqxLwiqJ0WSem4F77X69LKWd1PSrv68l9aWtODsVXXoWrpYXoaTcQfc01fn1RcnFdM1dvKqDO7mRiZCgv9U/t1iq31UXNLHpvM7WlLRiDdIw7vZd7WxU//jNUFMW/7a0v3es3nZSyvP3oV4nmgU4Iwe2H3E51WzULChZw0y838crEV9BrfbfYP/r663DW11P/4YcUXXElae+9izbMc9NuTomLwCYlN24u4rx1+XwyNIvhoZ0r4R4aFciptw7ny+fXsOm3MuwWJxMv7odW679XvBVFOSAd8h+vqTUCXmb+4w+Kr7seabEQP/0BIs49t7tD2ispJS8XVfFYfjkAd2ckcENaLJpuSvScDhcrvytg1fxCpEuSNTyW8Wf3xhRm7JZ4FEU58O3zUpsQYhzwFJDZ/n6/nIJ7INEIDY8d9hj1lnr+LP+Te3+/lyfGP4FG+CapEkIQd9+9OBsbafr2W4quuJLUt99CG+y5kdiz4iOxuyS35hZz9prtfDwkixFhnUtCgyMCOPXW4Xz14hryVlRisziYdMVA9IbuWz+jKMrBRUp5SXfHcLBq/ulnSm+8Eel0kvjE44RNmdLdIe2V2enk5i3FfFXVQKRey2v90xkfGdJt8ew+6hkYoufw87LJGqZ+vVMUxbs6ktG8BcwCDsN9hXck/32lV/EAg9bA80c+T3ZENt/v+J6nVjzl00I7QqMh8YnHCZ44Ecu6dRRffTWu1laPtjE1MYqn+iTT7HRx9trtLG9o6fS5gkINnHrLMOIzwyhcX8vXL67B2ubwYLSKoiiKv2n67jtKpk1DAknPP+fXyWdhm5UTV+XxVVUDA4MDmT+iT7cln06Hiz+/zufTJ1ZSW9pCrxGxnPvAaJV8KoriEx1JQNuklB9KKfN3r4jr9cgUQgwhvHL0KyQFJzF782xmrfXtEiKh15P03AxMhx1G28pVlFx/PS6r1aNtXJgUzYzsFMxOF+esy2dpF5JQY5Cek28cSuqASMq3NfLFjBzamm0ejFZRFEXxFw2ffUbprbchtFpSZr1M6LHHdndIe/VLXROTVm5ls9nCaXERfDW8N6mB3TPFtaakhblPrGTltwUYgnRMumIgk64YSGCIoVviURTl4NORBPQ7IcTxXo9E2aOYoBjeOPYNYgNjeXXtq7y38T2ftq8xGEie+SJBo0Zh/mMppdNuRNo8m9SdlxjF831TaXO6OG9tPkvqO79Fi96oZfI1g8kaHktNcQvznsmhuc6y7w8qiqIoPUbd+x9Qfu99aAIDSXnjdYLHj+/ukPZISslLhZWctzafJoeT/8tK5OV+qQR1Q50Cl0uSs7CQuU+soLakhazhsZw3fTS9RqhRT0VRfGuv34BCiGohRBVwFfCtEKJRCFG12/OKj6SEpPD6sa8TbgznmZXP8NnWz3zaviYwkJRXZhE4bBgtv/5K6W23Ix2end56dkIkM/ulYnW5uGBdPou7sE+oVqfh2MsH0P+wRBoqW5n39CrqytU2fIqiKAeC2rfeovLRR9GEhZH67juYRo3q7pD2qM3p4rrNRTySX064XssnQ7O4OjW2W6rKNtW4K8YvnbcdvUHLMZf257gr1ainoijd47+2Ydnj9is7+ds03J5cOr6jNtZs5LKFl9Fqb+WpCU9xXMZxPm3f2dxM0SWXYtmwgdATTyTxyScQWs8W+vmisp7rNheiF4J3BmZwZFRop88lpWTp59tZvbAIo0nHCdcOISFLbaKtKMq+dWIbln/umf03PaVwn7/3pTWvv0H1jBloIyJIfedtAvr27e6Q9qjSaufi9TtY3dxKP1MA7w3K6JYpt1JKcpdVsHjOVuwWJ8l9Izjqwn6ERAb4PBZFUQ4+e+tL9zoCuttaz7N3X/u58zlvBqvs2YDoAbx01EsYtAbu/u1uFpcs9mn72pAQUt98A2N2Nk3ffEP5PfcgnU6PtnFKXASv9E/HLiUXrd/Bd9UNnT6XEIJxp/Xi0DN6YTU7+PL51eSvqfZcsIqiKH/ZWaDvVWAucAxwLDAHeKUb4zpg1Lz6qjv5jIwk9b13/Tb5XNfcyvGrtrK6uZVJ0aF83U3rPdtabMx/fQOL3tuMyyE57MzenDxtqEo+FUXpdh1ZhHBOB59TfGBk/EhmHDEDgFt+uYUVFSt82r42PJzUt9/C2Ls3jV9+Rdldd3s8CT05Npy3BmQAcMXGAj6pqOvS+YYencqxlw9ASsn819azYXGpJ8JUFEXZZbcLtJOllNdJKddKKddIKW8AJnd3fD1d9csvU/38C2ijokh7710C+vTp7pD26JuqBqbk5FFmtXNDaizvDMwgWOf7LcFKttQx5+Hl5K+uJjolmLPuOYQhE1MQmu7Za1RRFGV3/7UG9BghxNNAohDiqd1ur+HeC1TpJhOSJ/D4hMexu+xct+g6VlWu8mn7uqgoUt97F2OfPjR9/TVld9zp8TWhx8WE8cHgTAxCw7TNRbxd0rWRy94j4zjphqHojVp+/TCXP7/K9+m2NoqiHDTChBDROx+031dz/ztJSkn1izOpmfkS2pho0v73Hsbevbs7rH+RUjKjoILLNxbglDCzXyr3ZiWi8fF6T6fTxdIvtvPlC2swN9kYPimVM+4cSWRi5/bZVhRF8Yb/GgG1AS2417SYd7ttAU71fmjKfzku/TgePvRhLA4L1/54LWuq1vi0fV37FChjdjZN335L2R13eDwJnRAZwtyhWYTptNyTV8oLBZVdShqTsyM49bbhBIUZWPldAT+9vwWn0+XBiBVFUXgeWCuEeK39gu1q4LnuDalnklJS/cIL1MyahS4mhrT3/ocxK6u7w/oXq8vF9ZuLeGpHBdF6HfOG9eLM+Eifx9FY3cbnz+SQM7+QoFADJ984lLGn9kKr833FXUVRlP+y1yJEu94gxEAp5QYfxdNpniqc8MOmSgYmhZIQFuiBqLzv87zPmf7HdIL0Qbx2zGsMiRni0/Yd9fUUXXoZ1s2bCTnuOJKefgqh13u0jY0tbZy9Zjs1dgfXpcZyX2ZCl6oINtW28c3MtdRXtJLSP5JJVwzEGKjzYMSKovR0+1uE6B+fHQwc3v7wFynles9F5l3+VISo6oUXqH3lVXRxcaS99y6G9PTuDulf6u0OLlm/g2WNZvqZAnh/cCbJAb6vLLt1eQW/fJiL3eIkbVAUEy/spyrcKorS7fa7CJEQ4sz2uxOEENf+8+a1SLvR+pJGrv5gFee8vozyxrbuDqdDTu19KtPHTsdsN3P1D1ezoca31wp0ERGkvfM2xv79aJ4/n9Jbb0Pa7R5tY0BwIF8O70WSUc/LRVXcubUEZxdGQkOjAjnt9hEk9g6neFMd855eRVNtz/j7VhSlRygAfpdSzuxJyac/qZ41y518xsaS9r/3/DL5LGizcuKqPJY1mjkiIoSvhvf2efJptzpZ9N4mfnh7Ey6HZPzZvTnh2sEq+VQUxa/917yMge3HQ/Zw69RVYX83MCmUqaNTKaxt5dzXl1HRaOnukDrk9D6nc/+Y+2mxt3DlD1eysXajT9vXhoeT9vbbBAwYQPPChZTceBMuq9WjbWQFBfDl8N5kBhr5X1ktV24swNKF6bMBJj0nTxtKn9Fx1JWZ+fTJVVTuaPJgxIqiHIyEEJOBjcC89scjhRBfd29UPUvN629Q8+JMtDHRpL77Loa0/9wVrlusbDQzedVWtrdZOT8hivcHZxLi42JDdWVm5j6xki1LK4iID+KMu0Yw+MiUbtlnVFEUZX/scwpuT+GpaUNSSh74ciPvLyskPSqIj68cS3xYzyhZ/vGWj3n0z0cJNYTy5rFv0i+qn0/bdzY2UnTllVjWriNo7BhSXnoJjcmzhQ+qbXYuWLeDNc2tjAkz8e6gDML1nZ8+K6Vk5XcFLP96Bzq9hqMv6U/W8B6xXZ+iKF7U2Sm4QogVwEnA91LKYe3PbZJS9vd0jN7Q3VNwa995l6onn0QbGekuONSrV7fFsjdfVTVww+ZCrC7JvZkJXJ8a6/OkL3dZOb98mIvD5iJ7dDwTzu2DIUAtJVEUxb90Zgru5P+6eTfc7iOE4KEpAzh/TCoFta2c+0bPGQk9p+853DXqLppsTVy+8HKfT8fVhoWR+tbbBI0eTevSZRRddjnOJs+OKsYY9Hw2NIsjI0NY1mhmyuptlFlsnT6fEIJDTsjgmEv745KS+a9vIGdhoaqQqyhKp0kpK/7xlGenhByg6j6Y7U4+w8NJfecdv0w+Xymq4sqNBQC8NiCNG9LifJp82m1OfvrfZn58dzNSwpEX9GXixf1U8qkoSo/yX1Nwb/+P223eD637CCF46OSBTB2dyo4aM+e+sYzKpp6RhE7tN/VvSejqqtU+bV8bbCLl9dcIPuII2tasofDCi3DU1nq0DZNOy/8GZXJmfAS5Zgsn5uSxxdy1NZx9RsUz5aZhBJj0LJ23nZ/f34LTrirkKoqy35qFEHG4K8gjhDgCaOjGeHqE+o/nUPnII2hCQ0l9+y0Csv1rn0+XlEzPK+XB7WVE6rV8OrQXU2IjfBpDfYWZT59YyeY/ygmPC+KMO0fS/9BENeVWUZQeR03B/Q8ul+S+Lzfw4Z9FZESb+PjKMcSF9ozpuJ9u/ZSHlj5EgC6Al456iVEJo3zavrTbKbvzLpq++w5DRgapb7+FPiHBs21IyWP55cwsqiJMp+V/gzIYHR7cpXM2VLXy3ax11Fe0Ep8ZxnFXDcQUZvRQxIqi9BRdmII7GngFyADWAr2Bk6WUvt2wuZO6Ywpuw7zPKb/nHjTBwaS+8zaBgwb5tP19sbpc3Li5iC+qGkgJMPDRkEx6Bfn2d4Ftq6r46X+bsVud9D4kjiOmZqtRT0VR/N5+T8FVQKMRPDJlIOeOco+EnvP6Msoaeka11DP6nMGjhz2K1Wnl2kXXsqR0iU/bF3o9iU8/RfiZZ2DbsYPCqedjKyz0bBtCcG9WIo/0TqLJ4eSstdv5sqq+S+cMjw3i9DtHkjYwior8Rj59YiVVhao4kaIoHSOl/BM4EjgPeAoY0FOSz+7QNH8B5ffdhwgKIuWN1/0u+Wx2OJm6Np8vqhoYGBzIN8N7+zT5dDld/PHZNha8sQGnw8Xh52VzzKX9VfKpKEqPphLQfdBoBI+e8td03DNfXUphrbm7w+qQk7JO4qkJT+F0Obnhpxv4qegnn7YvtFriH3qIyIsvxl5WRsF5U2nb4PkKvZcnx/DagHQArtpYyMzCyi6t4TQG6ph87WCGT0qlpd7KvGdyyFtR6aFoFUU5kAkhnpdSNkopv5dSfielbBBCPN/dcfmjlt9+o/T22xE6HSmzXiZo2LDuDulvKq12Tlmdx5KGFsZHBPP5sF7EGT27z/V/aW2y8dULa1j9QxHBEUZOvW04AyckqSm3iqL0eCoB7QCNRvDIKQO57LAMShvaOOu1pWyraunusDpkUvokZhwxA4Hg1l9uZf6O+T5tXwhB7J13EHPTTThraym88EJalvzu8XZOjg3ns6G9iNRreTS/nFtzi7G7Op+EajSCsaf24uhL3IUrF761kWVfbEd24ZyKohwUJuzhucN9HoWfa12xgpIbpoGUJD3/PKYxY7o7pL/Z1mrhhJytbGyxcGpsOLN9vM1KRX4jnzy2gtKtDSRlh3Pm3YcQnxHms/YVRVG8SSWgHSSE4L4T+jHtqF5UNlk5+7WlbCrrGVMzj0w9kplHzUSr0XLH4jv4JPcTn7YvhCD66qtIePRRpNVK8dVX0/i157fFGxlm4vsRfegdZOTD8jrOW7edRrujS+fMHh3PqbcOxxRmYNX8Qr59ZR0Ws91DESuKcqAQQpwphJgLpAshPtnttgBo7e74/Enb+g0UX30N0mol8cknCDnqyO4O6W/WNbdyck4eJRY7V6fE8HL/NAwa3/y6JKVkw+JSPn82B3ODlWHHpnLytKEEhRp80r6iKIov7LUIUfteZnsd7pFS+raqzT74snDCrF+28dT8XEIDdPzvstEMTQn3SbtdtbJiJTf8dAMt9hauH3o9Vw6+0udTeZp/+YXSm25GWizE3nEHUZde4vE2GuwOLttQwO8NLfQOMvLB4EzSArtWSMjcaGX+a+upyG8iNDqA468eRHRyiIciVhTF3+xvESIhxBBgGPAg8MBuLzUBi6SUPeKKpbf7UmteHoUXXIizoYH4hx4k4qyzvNZWZ/xR38KF6/Npcbq4LzOB69PifNa20+Fi8ZytbPqtDL1Ry8SL+ql9qRVF6dH21pf+VwK6c8rQCUBf4K32x5cAuVLKO70RaGf5unLfu7/v4P++3kSwUcdbF41kdGaUz9ruii11W7j6h6uptdQytd9U7jjkDjTCtwPhbWvXUnzV1TgbGoi8+GJi77gd4eGryzaXiztyS/i4oo4ovY53BqYzqosVcp0OF7/PzWP9r6Xo9BqOOL8v2aPjPRSxoij+pAtVcCOllHXeiMkXvNmX2oqLKTxvKo7qamLvvJOoSy72SjudtbCmkSs2FmB3SZ7OTmFqou/69dYmG/NfW0/59kbCYgOZfM1gIhNMPmtfURTFG/Y7Ad3tg38CY2T7G4UQWuAPKeVor0TaSd1ROn7OiiLumrceg1bDrKnDmdjPd1dKu6K4qZgrfriC0pZSTsg8gYcPfRi9xneFFQCs+Tsovvxy7GVlhE4+noTHH0dj9Ox2J1JKZhZV8Vh+OXoheKJPskd+odiyrJxfZufitLsYdHgSh57ZG61OzWZXlANJFxJQHXApMBTYVS5VSnlpBz7bBvzZ/vB9KeVbu70WgPtCcCpQBFwmpbS0T/tNArTAK1LKd/d1rv/irb7UUV1NwXlTsRcXE33ttcRMu8HjbXTFpxV13LilCA2CWf3TOCk23GdtVxU28f2r62mpt5LaP5JjLhtAgMm3fbKiKIo3dGUblkh260QBY/tzHWn0YiHEH0KI34UQw//xWoAQYrYQ4rf2Y8A/Xv9FCPFmR9rpLmcfksqL5wzDJSVXvr+KuSuLuzukDkkJTeH949+nT0Qfvs3/lmk/TaPV7tslSsbMDNI++ghjv340ffc9RRddjKPOs4MGQgimpcXxzsB0DBrBrbnF3Lu1pEvFiQD6jkng9DtGEBodwPpfS/liRg4t9VYPRa0oSg/3GnAocCKQBxwCdHT/rlIp5RHtt38mjBcDW6SU44Hc9scA90gpx+EudHTfbn3pf53Lp5xNTRRdcSX24mIipk4l+obruzOcf3mzpJrrNxdhEBreH5zh0+Rz64oK5j3j7kOGHZPKCdcPUcmnoigHvI4koHOApUKIu4UQdwO/Ax/v60NCiAhgGnAEcD7w4j/ecjF77kwRQpwINHcgtm530pBE3r74EIw6Dbd/uo5Xf93epS1AfCUmKIZ3jnuH4bHDWVK6hCt/uJJ6S9f20Nxf+rhY0j94n+DDD6dtzRoKzjob6/btHm/n+Jhwvhnem9QAA2+V1nDu2u3UdbE4UUxKCGfefQhpg6KoyG/ik8eWU7Sp1kMRK4rSg42SUl4ENEgpHwcOAwZ08LPxQohfhRDzhBDp/3jtcOCb9vtftz9GSpnX/pwNcPJX7Yb/OtffCCGuFEKsFEKsrK6u7mCoHeOyWCi59jqsW7YQesIJxN17j99sIyKlZEZBBffllRKm0zJ3aBZHRIb6pG2XS7L082388NYmAI6+pD/jTu+FRuMffzaKoijetM8EVEp5H3A/EN1+u09KeX8Hzj0K+E1KaZNS7gBChBC7z7HcY2cqhNAA1wEv76sBb3aa+2N87xg+umIMkSYDT3y/hUe/3YyrB2zXEWoI5dVjXuWI5CNYW72WC76/gKKmIp/GoDGZSJ71MhEXXIC9pISCc87FvHSpx9vpFxzI/JF9OCw8mCUNLRy3ciubWzo6KLFnASY9J1wzmNEnZ2BpsfP1zLUs+3I7LqfLQ1EritID7fxicQohgqSUjUBHK8mkSykPxz2K+s9Ryyhg51XCBv49E+lu4GMp5c7pGP91rr+RUr4upRwppRwZExPTwVD3TToclN56G60rV2I67DASH3/M4+v9O0tKyaP55Ty1o4JYg44vhvViZJhv1lzaLA7mv7aenAXu/T1Pu224qiegKMpBpUM9gZTya+AJKeWtUspvO3ju3TtL+HeHubfO9CJgHmDpQFxe6TQ7Y0hKOJ9ePZak8EDeXLKDW+euxd4DEpFAXSDPHfkcZ/U5i8KmQs7/7nzWVK3xaQxCqyX+3nuIu+8+XGYzRVdcSf3cuR5vJ1Kv46MhWVyWFE2RxcYJOXl8U9XQpXMKjWDk5Aym3DSMoBADq74v5IvnVqspuYpy8KprnwE0H/heCPEZUNqRD0opa9qPC4C0f54XCG+/H9b+GAAhxIXAYNwVeDtyLq+TUlI+fTotixYRMGQwyS++gDD4x1YiLim5L6+Ul4qqSDLq+WJYb/oFB/qk7ZZ6C58/m8OOtTXEpodyxl0jiU3zzairoiiKv9hnAiqEGC2EKARy2h+PFEK83oFz795Zwj86TPbQmbavXZkKvNOB8/udzJhg5l07juy4ED5fXcrl763EbO3aVE9f0Gl03DfmPm4ecTP11nouW3AZCwsW+jyOyPOnkvLKLDQGAxX3P0Dl008jnU6PtqHXCB7tk8yM7BQcLsnlGwt4cFspji6OWCdlR3D2faNI6RdB+bZG5jy6nKKNakquohyETpBS1gP3Am8CPwOn7+tDQojg9iJ/CCEGAzX/eMuvwOT2+5PbHyOEmAKcB1wgpXR18FxeVz1jBo2fzcOQlUXKq6+iCQrydQh75JSS23OLeau0hrQAA58P60VmkGcL4O1NVWETc59YSU1xC71GxHLqLcMwhfmmbUVRFH/SkRHQGcDxtHdgUsqVuAss7MufwGFCCL0QIhVo2W1qEOy5M83AnZR+AzwFTBJCXN6BtvxGXGgAn1w1lkPSI/h1azVnvrqUisZ9DuZ2OyEElw68lKcPfxqA2369jfc2vufz9azBhx9O2kcfoktIoO6ttym+5hqcTZ7fPu+8xCi+GN6LJKOeV4qrOWPNNqqs9i6dMyjUwEk3DGX0yZlYze4puUs/346zB4yEK4riGVJKZ/vRJaV8X0r5Ugf3AO0PrBRCLAZmAlcJIYYKIW5vf/1dYJAQ4jdgUPtjgNm4l8csbC/el7Snc3nox+uQ2rffofaNN9ElJJD65hvoIiJ82fxeOVySGzcXMbu8jl5BRr4Y3ovULu4R3VHbVlXx+TM5tDbaGHlCOsdeNgCdQeuTthVFUfxNR7ZhWSGlPEQIsVpKOaz9uV339/HZS4HLcRdFuBFwAMdIKZ8WQgQCbwPJQAlwiZTSsttnjwDOl1J2KAHtjm1Y/ovF7uTWuWv5dl058aEBvH3xIfRP7BnTbHIqc5j28zQarY2ck30Od466E51G59MYHNXVlNx4E205ORjS00me9TLGzEyPt1Nrc3DtpkJ+rW8mzqDj9QHpjO7ifqEApVvr+eGtjZgbbcSmhXDMpQMIj/OPEQBFUfZtf7dhEUKs4K8CQP8ipRzlkcC8rKt9qXQ6Kb7iSiybNpH24WyvfG93hs3l4ppNhXxb3Ug/UwCfDM0ixuD9arNSSnIWFLLsi3w0OsFRF/RT6z0VRTlodGUf0CXAccBiKeVwIUR/4C0p5VjvhNo5/paAgrvK3TMLc5n1y3ZMBi0vTx3OEdkdrUXRvQoaC7jmx2soaSlhXOI4nj78aUINvk2gpc1GxcOP0DB3LprgYBKffoqQI4/0eDtOKXlmRwXPFVaiE/BAViJXJMd0uVJjW4uNn/63hYJ1NeiMWsaf1Zt+4xL8pgKkoih714kE9PD/el1K+WvXo/I+T/SlLpsNe0mJ3ySfVpeLKzYUsLC2icEhgXw8JItIvfcvqjqdLn79MJfNv5cTGKLn+KsHk5AV5vV2FUVR/EVXEtDjcVfBzcRdVOE43COTP3oj0M7yxwR0p4+XF3HvFxsAeGjKAKaO9nk9iE6ps9Rx8883k1OVQ3poOjOPmkl6WLpPY5BS0vDxx1Q8+hg4ncTceCNRV13plSRuYU0jN2wuotHh5ISYMGZkpxDWxV9SpJRs/K2M3+fm4bC7yBoewxFT+6p93hTFz+1vAnqg8Oe+tDOsLheXbSjgx9omRoYGMXtwZpe/1zvC1uZgwRsbKNpUR0R8ECdeP4TQaN8UOlIURfEXnU5A2z+cCUwCBLBQSrnN8yF2jb93mr/lVXPtBzk0Wx1cNSGTO4/r2yP2+7I77Tz656N8lvcZIYYQnpnwDOOSxvk8jtYVKyiZdiPO+npCjjuOhEceQRvs+ZL5hW1WrthQwLqWNpID9LzWP50RHijNX1du5oe3N1JT3EJwhJGJF/cnOds/1kUpivJvnU1AhRBhwJ3AUCBg5/NSyqM8F533+Htfuj92Tz4PCTXx4ZBMQnTeX3dpbrDy9UtrqS1pIbF3OMdfPUhddFQU5aC0t760I1Vw75BS5kspX5FSzpJSbhNC3OGdMA9c43vH8Ok140gMC+C1xflc+f5Kmi1dK3rjC3qtnuljp3PXqLtotbdyzaJr+GDTBz4vThR0yCFkfDoXY79+NM+fT8FZZ2HNy9v3B/dTWqCRr0f05vLkaEosdqaszuOlwkpcXfx5IxNMnHHHSIYek0pLvZUvn1/Nkrl5OGyerfKrKEq3extwAn2AN9rvL+/WiA5CVpeLS9f/lXx+5KPks7a0hU+fXEltSQu9R8Zy8rShKvlUFEX5h45UwT2ng88p+5AdH8IX1x3K0JRwftxcxWmz/qCgxtzdYe2TEIKp/aYy6+hZmPQmnlzxJA8ufRCb0+bTOPRJSaR/9CFhp52GLT+fHWedTePX33i8HaNGwyO9k3lvUAYhWi2P5JczdV0+1bauXTDQ6jUcenovTr5pKMHhRtYuKmbOoyuo3OH5Kr+KonSbXlLK+4FWKeVHwInAhG6O6aBicbqTz0V1fyWfwT5IPku21DHv6VW01FsZPimVYy4dgFbfoe3WFUVRDip7/WYUQhwjhHgaSBRCPLXb7TXcU3GVTogNDeDjK8dw+vBk8qpamPLy7yzJ8/kWbZ0yLnEcH07+kPTQdD7L+4xL5l9ChbnCpzFoAgJIfOxREh59BFwuym6/nYqHHsJl83wyPCk6jB8PyWZ0mImf65qZuCKXxXXNXT5vSt9IznlgNH3HxtNQ2cpnT61k2ZfbcTrUdi2KcgDYud2YTQgRCdiAmG6M56Bicbqn3S6qa2JUmO+Sz63LK/h65lrsVieHn9uHsaf2QvSAZTaKoijd4b8uzdmAFtxl5c273bYAp3o/tANXgF7LM2cO5r4T+tFssXPh23/y9pIdPp/W2hnpYenMPmE2RyQfwbqadZz9zdn8Wf6nz+MIP/100j/+CH1KCvUffkTh1POxl5Z6vJ2kAAOfDe3FzWlxVNscnLV2O/fnldDWxb09jYE6Jl7Un8nXDCIgxMCq7wuZ+/hKakq6nuAqitKttrYnnh8Cy3Dvib2qe0M6OOxc87kz+fxwsG+SzzU/FvHD25vQaATHXzOYgYcne71NRVGUnqwjVXAHSik3+CieTuuphRMWb63m+g9zaLI4OHNEMg+fMpAAvf9vTu2SLt5a/xYzV89ECMG0YdO4dOClPt9ixNnURNnd99CyaBHasDASHnuUkIkTvdLW7/XNTNtcRKnVTp+gAGb1T2VgSNf39mxrsbH4o61sW1WFRisYflwaI49LV1O3FKUbeaIKrhDiMCAcmC+ldHgkMC/rqX2p3SW5fOMOFtT4LvmUUrL08+2sXliE0aTjxOuGEJ+ptllRFEXZqatVcI/l3xX9HvJkgF3VUztNgB01Zi5/bwXbq80MSgpj1tThpER2PbHxhT9K/+DO3+6kwdrAxNSJPHzow4QYQnwag5SSurffpuq558HhIGLqVGLvuB2N0ejxthrtDu7JK+Wzynr0QnBnRjzXpMai9UDinbeyksUfb8XSYiciwcRRF/RVv8woSjfpQhXcgcAOKaW5/XEwkCal3OjpGL2hJ/alDpfkmk2FfF3dwPDQIOYMyfJ6wSGn08Uv729hy7IKgiOMnDRtKJEJnq/MriiK0pN1pQruE7hLyt8CJAHX4q7up3hIRrSJz687lGP7x7G+tJETZy7h59yq7g6rQ8YljWPOiXMYEDWARUWLOPfbc8mty/VpDEIIoi67jPQP3keflET97NkUnHU21u3bPd5WmF7Hy/3TeLV/Giathkfyyzl99TaK2qz7/vA+9B4Zx3nTR9NnVBz15WY+e3oVv83Zis3SIwZOFEVxew/3EpadbO3PKV7glJKbthTxdXUDg4ID+Wiw96vd2q1Ovn9lPVuWVRCRYOL0O0ao5FNRFGU/dGSO3wm49wCtlFJeBYwAIr0a1UEoNEDPaxeM4J7JfWmxOrj03RXMWJiL0+X/60ITgxN57/j3OKPPGRQ2FXLet+fxSe4nPl/TGjh0KBlffE7o5OOx5uay4/QzqJ871ytxnBIXwc+jspkQEcyyRjNHrsjl3dKaLm/XEhhi4JhLB3DCdYMJDjey7ucSPn5oOUUbaz0UuaIoXqaVUu4qmS2ltAG6bozngOWSkjtyi/m0sp6+pgA+HpJFmN67f9SWFjtfPr+awg21xGeGcdptwwmOCNj3BxVFUZRdOpKAWtrXrkghhF5KWQqoFfZeIITgyglZzL58NFEmIy/+tI2L31lOndm32510hlFrZPrY6Tx22GPoNDoeXvYwt/56K002324xog0JIfHZZ91VcoWg4v4HKL3lFpyNjR5vK8Fo4OMhWTzSOwmXhLu2lnDmmu0UemA0NH1QNOdOH82gw5NorrPw9cy1LHxrI+bGrp9bURSvsgshMnc+EEJk4d4LVPEgKSX35ZUyu7yOXkFG5g7NIsrg3eTT3GDl8xk5VO5oIn1QFCffpPb4VBRF6YyOJKDNQogg4A/gPSHEs0Cbd8M6uI3JjOK7aYcxKj2S3/JqOOHF31hVWN/dYXXISVknMefEOfSL7McPhT9w1tdnsa56nU9jEEIQfvrpZHz2Kca+fWn+fj75J0+hZcnvHm9LIwSXJ8fw86hsxoUH83tDC0csz+XNkuouj4YaAnRMODebU28bTkSCibwVlXw4fRnrfi7B1QNGxhXlIPUg8LsQ4k0hxJvAb8AD3RzTAUVKyUPby3i7tIa0AANzh2YRY/BuIthU08a8Z1ZRV2Yme0w8x189CL3B/wsGKoqi+KOOVMGNAxoALe51oOHAi1LKIm8Htz96YuGEfbE7XTy9IJfXF+ej1QhuOaYPVx+ehbYH7C1mc9qYsWoGszfPRid03DD8Bi4ecDEa4dvKri6bjeoXXqDu7XdASiLOO5fY225DE+T5Ik8uKXmvrJaHt5fR6nQxOszEc31TyQzqejEkp8PF2kXFrPhmBw67i5jUEA4/L5u49FAPRK4oyj91oQhRGBALHNP+1EIp5TaPBudFPaEvfXZHBU8XVJBk1PPF8N6kBBi82l5dmZmvXliNudHGoCOTGX9mb7XHp6IoSgd0qQpuT9ATOs3O+nFTJbd/upb6VjtjM6N47uyhxIf1jDUnPxX9xP2/30+TrYkxCWN4+NCHiTfF+zyO1pUrKbvrbuwlJRjS0kh88gkChw71SltFbVZuyy1mcX0LRo3g5rQ4rk2NxaDpevLdVNvGb3PyKFhXAwIGjk9i9JRMNQ1MUTysMwmocO9DtVFK2d9LYXmdv/elb5ZUc19eKbEGHV8O602GBy7w/Zeqwia+fnEtFrOdkSekM+rEDJ9vN6YoitJT7XcCKoSYC+w1O5VSnuW58LrO3zvNrqpotHDznDUsza8lIkjPM2cOYWK/uO4Oq0PKW8q567e7yKnKIcQQwv1j7uf4jON9HoezxUzVk0/SMHcuaDREXXkFMddeizB4/uq5lJIPy+t4aHsZjQ4nvYOMPJOdwujwYI+cf8faahbP2UpLnZUAk57RUzLpf1giGnVVXlE8ogsjoAuAc6SUPWPdxD/4c186p7yOG7cUEa7T8vmwXvQLDvRqe6Vb6/l21jrsFieHntGLoUenerU9RVGUA01nEtCL/uuEUkq/Kivvz52mpzhdkld/3c6MH7bidEkuHpfOXcf3JUDv/+tQnC4n7216j5mrZ+JwOTg+/XjuHXMvYUbf73PZ/PPPlN//AM6aGoy9e5Pw6CMEDh7slbaqbXambytjXqX7d9GpCZHcl5VIhAcqNdqtTlbNL2DND8U4HS6ikoIZf3ZvkvpEdPncinKw60IC+gkwCvgOaNn5vJTyDg+G5zX+2pd+V93A5RsKCNBqmDskixFh3t32pGB9DfNf34DL4eKI8/vS/9BEr7anKIpyIOrUFFwhRCSQAeRJKX1bznQ/+Wun6Q2rCuuY9tEaShva6BMXzIyzhjIwyfeJXGfk1uVy1293sa1hG7FBsTxy6COMTRzr8zgc9fVUPvIoTd9+CxoNkRdeSMy0G7yyNhTgl7om7swtodBiI1qv48FeiZwWF+GRqVyN1W38MW8b+aurAcgaHsu407MIjfLu6ICiHMi6kIBO39PzUsoHux6V9/ljX7q4rpnz1+UDMHtwJuMjQ7za3vbVVSx8cyMAx1w6gF4jYr3anqIoyoGqMyOgZwPvAM2AEThNSvmTV6PsAn/sNL2psc3O/V9s4Ku1Zeg0ghsn9uaaI7LQaX1b5KczrE4rL61+ifc2vodEck72Odw84maC9N5J/v5L808/U/HggzgqK9GnpJDw8EOYxozxSlttThfPF1byclElDgljwkw82ieZAR6aRla8pY4ln+RRV2ZGq9cw9OgUhh+bhiFQbUGoKPurswloT+dvfemqRjNnrt2O1eXijQHpTI4J92p7W1dU8OM7m9FoBMddNZD0QdFebU9RFOVA1pkEdB1woZRyjRDiSGC6lPII74bZef7WafrKN+vKuO+LDTS02hmSEs6Ms4aQFeOZdYbetqJiBfctuY8ycxmJpkSmj5vOuMRxPo/D2dxM1TPP0jBnDgDhZ55B7O23ow31ToXZXLOFe7eWsKShBQ1wUVI0d2TEe2RarsvpYuNvZfz5dT5Ws4PAED2HnJBB//GJaHvAxQlF8RddGAGNBWYAqVLKCUKIwcA4KeWrHg/SC/ypL93c0sapq7fR4HDyYr9UzoqP9G57f5Tz0/ub0ek0TL5mMCn9vdueoijKga4zCegaKeXQ3R7nSCmHey/ErvGnTtPXqpos3DVvPT9tqcKo03DX8X25aGx6jyhIY7abeX7V83yc+zEAp/Q6hdtG3tYta0PNfy6n/P77sRcVoY2KIu7OOwg96SSvVDyUUvJ1dSMPbiul1GonUq/l7swEzkuIQuuB9ixmOznzC1n3cwlOh4uw2EDGnppF5tAYVcFRUTqgCwnol8D3wLVSysFCCAOwUkrpnYXmHuYvfWmJxcZJOXmUW+080juJy5NjvNrext9K+WV2LjqjlhOvG6zW0iuKonhAZxLQTcDpwM7fVj/d/bGUcpN3Qu0cf+k0u4uUkk9WFvPQ15sw25yMzojkidMHkxHt3UINnrKyYiX/t/T/KGwqJCYwhvvG3MdRqUf5PA5XWxs1s16h9p13wOEgaNQo4qc/gDEryyvtmZ1OXiqsYlZxFVaXZHBwIP/XK4lxEZ4ZxW6qbePPL/PZurwSgPjMMMadlkVCr3CPnF9RDlRdSEBXSilHCiFWSymHtT/3twu6/swf+tI6u4MpOXnktVq5KS2OuzITvNre2p+KWfJJHoYALSfeMJSErJ5RU0FRFMXfdSYBLWDv27BIKWWm58LrOn/oNP1BcV0rt3+6lmX5dRh1Gm46ug9XjM/oEWtDLQ4Ls9bM4r1N7+GSLo5NO5Y7R91JbJDvC0BYt22j4sGHaF2xAvR6oi65hOhrrkYT6J3CPgVtVqZvK2VBjbvW13HRodyXlUivIM/s91pd1Mzvn22jNNddjTd1QBRjpmQSk+rdYh6K0lN1IQFdJqUcszMBFUKEA79KKYd4PkrP6+6+1Ox0cuaa7eQ0tXJeQiTPZqd4ddZGzsJCls7bjjFIx8k3DiU2zTtLLxRFUQ5GnaqC25N0d6fpT1wuyZyVxTz23WaaLQ4GJIby5OmDe0yl3A01G3jgjwfIq8/DpDdx/dDrOafvOeg0vi2mI6Wk6auvqHzyKZx1degTE4m7526CJ0702i9Ei+uaeXB7KRtbLOgEXJgYzS3p8UQbuv6zSykp2lTHn1/mU13UDEDWsBgOOSmDqMSesW5YUXylCwnoHUAWMBF4BLgWmC2lfMHDIXpFd/aldpfk4vU7WFTXxLFRobw9MAOdF5eS5CwoZOnn2wkI1nPyjUOJSVEX5BRFUTxJJaAHocomCw98uYEFGyvRagSXj8/g5qP79Ih9Q+0uOx9s+oBX1r5Cm6ONvpF9uW/MfQyJ8f0ggrOxkarnnqNhzicgJUFjxxB3190EZPfxTntSMreijifyK6iw2QnRargxLY7LkmMI9MBItpSS/DXV/PnVDurLzSCgz6g4Djkhg/BY31ciVhR/1JUquEKIqcDJuJesfCWl/MCjwXlRd/WlUkpu3FLEJxX1HBJqYs7QLIK8OHNn9+TzlJuHEZWkLsIpiqJ4mkpAD2LzN5Rz/5cbqW62khoZxIMnD+DIvj1jX7PylnKeXPEki4oWIRCc3ud0bhp+U7cUKWrbsJHKxx6jLScHNBrCzz6LmGnT0EV4p1iF2enkteJqXiqqotXpIsGo55b0OM6Jj0LvgVEBl0uSt6KS5d/soKm6DaER9DkkjhHHpxER3zPWDiuKt3RhBPSof25Ztqfn/FV39aWPbC/jpaIq+gQF8OXwXh6pCr43KvlUFEXxDZWAHuQaW+08/v1mPl5RDMAx/eN44MT+pET2jBGvX4t/5fHlj1PaUkq4MZwbht3Aab1P655pud99R9Uzz+IoL0cTGkrMddcScd55CL3eK21WWe08W1DB7PJaHBLSAw3ckZHAKbHhaDwwFdjpdJG7tIJV8wtoqrGAgF4jYhl5fLr6xUw5aHUhAf1XxXh/ryK/u+7oS98sqea+vFISjXq+Ht6bpACD19pSyaeiKIrvqARUASCnqJ77v9jAxrImAvQarj+yF1dMyMSo8/9puW2ONt5Y9wbvbXwPm8tG74je3HnInYxOGO3zWFxtbdS+/Ta1b7yJtFgwpKURc/PNhEw61mvrQwvbrDy9o4LPKuuRQD9TAHdnJnBMVKhH2nQ6XeQtr2Tl9wU0VrUBkDk0hpGT01WxIuWgs78JqBCiF9AHeBGYtttLYbj30e7r4RC9wtd96XfVDVy2oYBQnZavhvcm2+SZwmt7opJPRVEU31IJqLKL0yX58M9Cnl6QS5PFQXpUEP938gCOyO4Z03JLmkuYsWoGPxT+AMBRKUdx68hbSQ1N9Xks9ooKqmbMoOmrrwEIGDyY2FtvxTR6lNfa3NzSxlM7Kvi+phGAISGB3Joe77FE1OWSbFtVycrvCt1rRIHkvhEMPzaN5H4Rah9R5aDQiQT0IuBiYCSwe2fUBLwupfy2A+doA/5sf/i+lPKt3V4LAN4CUoEi4DIppUUIkQ68DRiBb6WUj7W//zhgevvH/09KuaAjP4cv+9KVjWbOWLMNl4Q5Q7MYG+69hHBntVuVfCqKoviOSkCVf6lpsfLk91uYu6oEgCOyY7h3cj96x/WM0a4VFSt4cvmT5NbnotfoOb/f+Vw26LJuWR9q2bSJqhnPYV6yBADThPHE3norAdnZXmszp8nMU/kV/FLvrmg7KDiQm9PjOC46zCNTc6VLsn11NasXFlJV6G4jOiWYYcem0mt4LJoesLWPonRWF6bgXiylfLeTbW6TUvbay2tXAzFSyoeFEA8AVVLKV4UQHwMvSyl/E0L8CFwP5AGrgQntH/8VGC6ldO4rBl/1pfmtVk7M2Uqd3cmr/dM4Jc47a+kB1i4qZsncPJV8Koqi+JhKQJW9WlVYx0Nfb2JtSSNajeDcUSncdHQfooON3R3aPjldTj7f9jkzV8+kzlJHqCGUywddzrl9zyVA572pXHtjXrqUqmeexbJxIwhB6IknEnPdtRjS073W5qpGMzMKKllU595DtL8pgJvT4zkhxkOJqJSUbW0gZ2ERRRtrAQiJDGDIxBT6HZqAIcC363AVxRe6WAU3C/dWLLv+c0gpv+vA51qAVUAtcIuUsmC31z4CnpJSrhZCDAPukFKeK4TYsnN6rxDiZqAV+KX9vVPan/+y/f25+4rBF31ptc3OSTl5FLTZuD8rketSvTf7ZsOvJfz60VaMQTpOuWUY0ck94wKroijKgUAloMp/crkkX60t48n5WyhvtBBi1HHdUb24eFx6j9i2pdnWzLsb3+X9Te/T5mgjLiiO64Zex0lZJ/m+UJHLRfOCBVQ9/zz2wiLQaAg7+WSir70GQ6r3pgmvaWplRkEFC2vdiWivICPXpsRyenwERo1nRitrSlpY80MReSsqcbkk+gAt/cYlMPjIZMJiekZBK0XpiC6MgD4GXAFsBnaOOEop5VEd+Gy0lLJGCDEJd8I4cbfXFgJXSikLhBAZwKtSyklCiK1Syj7t77kEiMc94nmllPLi9uffa3//0r20eyVwJUBqauqIwsLC/f2xO6zV6eKMNdvIaWrlkqRoHuud5LVp/Zt+L+Pn97dgCNAy5eZhxKaFeqUdRVEUZc9UAqp0SJvNyVtL8pn1y3ZabU6SIwK5+eg+nDIsCa0XNwT3lOrWal5d+yqf5X2GUzrJDMtk2vBpHJVylM/XLkq7ncavvqJm1ivYS0tBqyVsyhSir7kaQ0qK19pd39zK84WVfFfdiATiDDquSI7hwqRoQj1UbKq5zsL6n0vY9HsZ1lYHCEgfGMXgo1JI7qvWiSo9XxcS0G24p7s2dbH9v03HbZ9q+4SUco0QYihw5x5GQG8C2nAnoE9IKU9pf/6L9vd36wioU0ou27CD+TVNHBcdylsDM9B66bti6/IKfnhnE3qDlpNvHEp8pu+XZiiKohzsVAKq7JeqZgszFm7lk5XFuCT0jg3m1mOzmTQgrkckF4VNhcxcPZMFBe66G/2j+nPtkGuZkDzB94mozUbDF19Q8+qrOMrKQacjbMrJRF9xhVen5m5vtfBKUTWfVNRhk5IQrYYLk6K5PDmaBKNntjmwW53k/lnBup9LdhUsikw0MXBCEn1Gx2MMVNNzlZ6pCwnob1LK8Z34XDDQJqV0CiEG4y5cNGa3168BIqSUjwkh7gHq2teAzgFekFL+IYRYANwEbAVygJ1x/IYfrAG9L6+EN0tqGBYSxGfDehHkpXXk21ZVsfCtjWi1gpOmDSGxt/fWlyqKoih7pxJQpVO2VTUz44etfLe+AoAhyWHcPqkvh/WO7ubIOmZDzQZeWvMSv5f+DvhBIjrvc3ciWlEBQhAyaRLRV15BQP/+Xmu30mrnzZJq3iurocnhQifgpJhwrkiOYXiYySNtSCkp2VLPup+KKdhQCxJ0Bg19DoljwIQkNfVN6XG6kIA+BaQAcwHLzuf3tQZUCDEKeA1oBiTurVwEcIyU8mkhRCDuarfJQAlwSXsV3Ezc1XENwPdSykfazzcZuL/99A93ZA0qeK8v3bnXZ2qAgW9H9CbG4J19k3esq2H+q+sRGsEJ1w0mpV+kV9pRFEVR9q1bElAhxMW415VI4AYpZc5ur+2tpPxcIAnQAq90tJqgSkC9a31JI08vzGXx1moAxmZGcdPRvRmdGdXNkXXM2uq1vLL2lV2J6ICoAVwz5JpuSURdNhuNX35J3ZtvYWtfa2U67DCirriCoFGHeC2eZoeTD8pqeau0mhKLHYDhoUFckRzDiTHh6D00xbqppo2NS8rY/HsZbc3udmLTQhgwPoleI2NV0SKlR+hCAvrzHp7u0BpQf+CNvvSHmkYuWr+DEJ2Wb4b3preX9vos3lzHNy+vBQnHXz2I9EE940KpoijKgcrnCagQIgJYBIzBnVC+L6U8bLfX91ZSvreUMq89Qd0ADJRSWvbUxu5UAuoby/JreXpBLqsK6wEYlRHJtKN6c2ivqB4xNXdN1RpeXfsqv5e5E9HsiGwuGXgJk9In+b5YkdNJ8w8/UPP661g3bQYgcMgQIi+5mJCjj0bovBOPwyVZUNvIG8XVLGt0T5uNN+i5KCmK8xKiiDN6ZmTC6XCRv6aajb+VUprbAIDOqKXXiFj6jU0goVdYj/g3oxyculIFtyfzdF+6saWNk3PysLpcfDwki8MivFOFtiK/kS+fX43TITnuioFkDovxSjuKoihKx3VHAjoJmCylvLH98VpglJTS2v54jyXld/u8BncVwcE7P/NfVALqO1JKFufVMHNRHivbE9FhqeHccFQvjsyO7RFJxZqqNby+7nV+K/0NgKTgJC4acBGn9DqFQF2gT2ORUmL+/Q9qX3+d1uXLAdAlJhA59XzCzzwDbaj3pq9uaG7ljZIaPq+sxyYlOgGTosO4KDGawyKCPbKNC0B9hZlNS8rI/bNi16hoWEwgfccl0HdMPMERvt8yR1H+y/4moEKI/5xHL6Xc1PWovM+TfWmF1c7kVVsps9qZ0TeF8xK8M2OmpqSZL2asxtrqYOLF/eg7JsEr7SiKoij7pzsS0POAPlLK/2t//CtwjpSyvP3xHkvK7/b5ewGDlHL6f7Ths9Lxyr9JKVmWX8eLi/JYmu/eH3JgUihXH57FcQPi0XmpwIQnba3fyjsb3uH7Hd/jlE4ijBGc1+88zsk+h/CAcJ/H07Z+A3Xv/4+m774HhwMRFET4qacSeeEFGNLSvNZujc3Bx+W1vF9WS6HFBkBGoIELEqM5Oz6SKINnRmOdTheF62vZsrScgvW1SJdECEjKjiB7dDyZQ2MwqMJFih/oRAK64z9ellLKTA+E5XWeSkDNTien5mxjXUsbN6TGcm9Wogei+7eGylbmPbOKtmY748/uw+Ajk73SjqIoirL/uiMBPQ44Tkp5U/vjNcDo3UZA91hSvv21C4ETgHOllK6OtKdGQLvXyoI6Zv60jV/b14imRAZy2aEZnDkyBZPR/xOK0pZS/rfxf8zLm4fFacGoNXJi5omc1+88+kT08Xk89soq6j/6kIaP5+BsaAAhMB12GBHnnE3w4Yd7bXquS0oW1zfzflkt82sacUrQC8Gx0aGcEx/JkZGh6Dy0VrS1yUbunxVsWVpOXZl7KrBWryFjcDR9RsWROiAKrc7/L2IoByY1BbfznFJy6YYdLKhp4sSYMF4fkO6x2RS7a66zMO/pVbTUWxl9ciYjJ6d7vA1FURSl87prDegPwFggAfjwH2tA91ZSfgpwDXCylNLW0fZUAuofNpQ28sZv+XyzrhynSxIWqOf8MalcNDad2FD/n2ZZb6nn49yP+ST3E2raagAYHT+aqf2mMiF5AlqNZ/bR7ChXWxuNX39N/fsfYM3LA0AXH0/4WWcSfsYZ6GNjvdZ2hdXOh+W1fFReR3H7qGicQccZ8ZGcEx/psUIiUkpqS1vY+mclW1dUYm5wz7g3mnRkDYul14hYkvqEo+kBI+rKgUMloJ03Pa+U10qqGRYSxLxhvQj0wv/d1iYbnz+bQ0NlK8OOSWXsaVk9YvmHoijKwaS7quBeClyOuwrujYCDfZeUbwG2AC3tp5kqpSzdV1sqAfUvpQ1tvLNkBx+vKKbF6sCg1XDikAQuHJvO0JTw7g5vn+xOO/ML5jN782w21m4EIDk4mbOzz2ZKrylEBPh2XzkpJW2rV1P/0cc0z5+PtNtBpyNk4kTCzzgD07ixCK13kmOXlPzR0MLH5XV8W91Am8v9nTE8NIjT4iKYEhvusS0VXC5JWV4DW5dXsH1VFTaLe9vCgGA9mcNi6DVcJaOKb6gEtHOcUjJtcxF/Nrbw/Yg+Xtluxdpq5/MZq6ktaWHA+EQOPy9bJZ+Koih+SO0DqnSLJoudj5cX8c7vBZQ3uosZD0kO44Kx6Zw4OIEAvW9HFPeXlJK11WuZvXk2PxT+gFM60Wv0HJ16NGdmn8nIuJE+/8XHUVdH47x51M/5BHtxMeAeFQ07ZQrhp57q1bWizQ4nX1U18HF5HSua2qfNCpgQEcJpcREcHx1GsM4zf6dOu4uizXVsW1XJjrU12HdPRofGkDEkmuS+Eej8/N+Q0jOpBLTzpJRU2xzEeqii9u4cNidfvbiG8m2N9D4kjqMv6Y/GQ8sCFEVRFM9SCajSrRxOFz9squR/Swt3FSyKCNJz1iEpnD86jZTIoG6OcN8qzZV8vu1z5uXNo9xcDkB6aDpn9DmDk7JOIjLAtxueS5cL8x9LaZw3j+Yff0Ta3NNkA0eOIPzU0wiZNAltsMlr7Re0WZlXWc+8ynq2tbqnzQZqBMdEh3FyTDhHRYUS5KGRSofdSfGmOrblVP0tGdUZtaQNiCRjSAxpA6MIMHlnc3vl4KMSUP/jcrr4/rUNFKyrIXVAJJOvGazWiSuKovgxlYAqfmNrZTPvLy3ks5wSWm3uRGJcVhRnH5LCpAHxfj8q6nQ5+b3sd+ZuncviksW4pAud0DE+eTxTek1hQtIE9FrfJkLOxkaavvuOhs/mYdmwAQAREEDIUUcSeuKJBB92GMJg8ErbUko2tLTxWWU9X1Q2UGFzb7MSqNFwdFQoJ8aGcXRkKCYPjoyW5NaTv7aaHWtraGtyJ94ajSChdzjpg6JIGxhFeFyQmpandJpKQP2LlJKf39/C5j/KicsIZcpNw9Ab/buvUBRFOdipBFTxO80WO/NySvl4RTGby5sACAvUc8rQRM4+JJX+id7b/9JTKswVfL7tc77a9hUlLSUAhBvDmZwxmZN7nUz/yP4+T4IsW7fS+PkXNH37LY6qKgA0YWGEHnssoSeeSNAhIxEa74waOKXkzwYz31Q38G11A5U2BwABGsFRkaEcFxPGxMhQj23rIl2SyoImdqytJn9NDQ2VrbteC40OIG1gNGmDokjqHY7OoH5ZVTpOJaD+Zenn28lZUEhEfBCn3TaCgGA120FRFMXfqQRU8VtSSjaUNjFnZRFfrimj2eJOWgYkhnLqsCROGpJInJ9X0JVSklOVw1fbv2JBwQLMdvf6yMywTI7POJ7j0o8jPSzdtzE5nbSuXEXTN9/QtGABriZ3kq+LiSHkmKMJOfZYgkaO9OqWLisbzXxd3cA31Y2UW90joxpgVJiJSdFhTIoOIzPI6LE2GypbKdxQS+HGWkq31uNyuL/ftHoNSb3DSe4XSWr/SCITTWp0VPlPKgH1H2t+LOL3T7cRHGHktNtHEBLp3/2BoiiK4qYSUKVHaLM5+X5DOXNWFPPnjjoANALGZUVzyrAkJg2IIyTAv698tzna+KnoJ77a/hXLypfhat/Ktl9kP47LOI5J6ZNICk7yaUwumw3zkiU0ffMNzb/8imx1jxRqIyIIOXoiIcdOwjR6lNem6bqkZE1TKwtqm1hQ08gWs2XXa72DjEyMCmViZCijwk0YPTQ6a7M4KM2tp2BDLUUba2mps+56LSjUQEq/SFL6RZCUHUlwhOeSYOXAoBJQ/5C7rJwf392M0aTjtNtGEJngvXXtiqIoimepBFTpcUrqW/lqbRlfrC5la6V7Vx6jTsPR/eKYPCiBI/vGEOShqZzeUttWy4+FP/J9wffkVOYgcf9/Gxw9mKNSj2Ji6kSfj4y6LBZ3MrpwIS0//Yyrxf1nqwkOxjT+MEKOPBLT+PHoIry31Uxhm5WFNe5kdGljC872r6EgrYbDwoM5KiqUIyNDSAv0TGIopaSxqo2iTXUUb66jNLceu9W56/WwmECSsiNIyg4nqU8EpjCVkB7sVALa/Qo31vLdy+vQ6ARTbh5GfEZYd4ekKIqi7AeVgCo9lpSSzeXNfLmmlC/XlFHR5B49C9BrOKJPLMcPimdivziCjf6djFaaK1lYuJD5O+azrmbdruczwzKZmDqRiakT6R/l2zWjLpuN1qVL3cnoz7/grHOPOqPREDh8GCFHHknw4YdjyPLeJu+NdgeL61v4qa6Jn2ubdxUxAsgINDA+IoTxESEcGhFMpN4zf8dOp4vK/CZKttRRurWBih2Nu6brAoTHBZHYK4yE3uEkZIUTGh2gpuweZFQC2r2qCpv4fMZqXHYXk68bTNqAqO4OSVEURdlPKgFVDggul2RVUT3frS/n+/UVu5JRg07DhN7RHN0vjqP6xRIb4t9rhCrMFfxU9BM/Ff3EysqVOKV7NC4uKI7xyeMZnzSeMQljCNL7bnsa6XTStm4dLT//QsvPP2PNy9v1mi4hAdOh4wg+7DBMY8eiDfPOSISUks1mC4tqm/i5rpmVjWZs7d9RAhgYHLgrGR0VZiLEQ5V1HTYnFfmNlG5toDS3nsqCJlzOv74bg8IMJPYKJ6FXGPGZYUQlBavtHw5wKgHtPo3VbXz21Eramu1MvKgffccmdGs8iqIoSueoBFQ54LhcktXFDXy/vpzvN1RQ2tC267UhKeEc3TeWif3i6JcQ4tejVw2WBhaXLmZR4SL+KPsDi9OdVOs1ekbGjdyVkKaFpvn057CVlNLy88+0/LaY1uUrkJb2dZsaDYGDBmE69FCCRo8mcOgQNEbvTFltdbpY3tjC4roWltQ3s76ljZ3fWBpgYEggY8OCGRsezKhwk8dGSO02J1UFTZRva6R8WwPl+Y279h4Fd1Gj2NQQ4jJCicsIIy4jlOAIo1//O1P2j0pAu0dbi43PnlpFY1Ubo0/OZOTk9G6LRVEURekalYAqBzQpJRvLmvhxcyWLNlexvrRx12uJYQEcnh3D+N4xHJoVTViQ/xYxsjqtrKxYyW+lv7G4ZDHFzcW7Xks0JTImcQxjEsYwKn4UUYG+m5Lmslppy8mhZckSzEt+x5qbu+s1YTQSOGwYptGjCBo9hsBBAxF67/wZ19kd/FHfwtKGFpY1trCpxcLu32DZpgBGhgYxMszEyFATWUFGNB5ICl0uSW1pC+XbGqksaKQyv4nG6ra/vSco1EBsWggxaaHEpoUQmxZKUKh3ijop3qcSUN+z25x8+dxqKnc0MWB8Ioefl60u6iiKovRgKgFVDioVjRZ+2lLFos2VLNlWg9XhrkSrETA0JZzxvWOY0CeGIclh6LT+OZVSSklhUyG/lf7GktIl5FTm7BodBciOyGZ0wmgOiT+EYbHDCDP6rkCHvaqK1mXLMP/5J61/LsdeUrLrNREYSOCQIQQNH07g8OEEDh2CNjjYK3E02B0sbzSztKGFpQ1mNrS0sttSTsJ1WkaEmhgRFsTQkCCGhgZ5bJS0rdlGZUETlTuaqMhvpLqoGWur42/vCY4wEp0SQnRKMDHJ7mNIlFpP2hOoBNS3XE4X37+2gYJ1NaQPjub4qwai8dPvZkVRFKVjVAKqHLQsdicrC+pZnFfN4q3VbKlo3vWayaDlkIxIxmRGMSYzioGJoX6bkNqcNtZWr2Vp2VL+LP+TDbUbdm3xIhD0iejDyPiRjIgbwfDY4T4dIbWXlmL+czmtf/6JecVyHGXlf72o0WDsm03QMHcyGjh4MPrUVK8kYa1OF2ubW1nZaGZlk5mVja3U2v+eFKYGGBga2p6QhgQxMCSQUA+sJZVS0lTTRlVhM1WFzVQXNlFV1Py3qbsAhkAd0cnBRCUFE5VkIjIxmKhEE4ZA/y6idbBRCajvSCn59cNcNv5WRlxGKFNuHobe4Jn13YqiKEr3UQmoorSrarLwW14Nv+VVszS/lsqmv/aHDDbqOCQ9gkMyIhmZFsng5DAC9P75i1CTrYmVFStZWbmSVZWr2FK3ZVdCCpAWmsaQmCG7blnhWeg0vkly7OXltObk0LYqh9bVq7Fu2QK7fddow8MJGDyIwMFDCBw8iICBA9FFRno8DiklBW02cprMrGluZU1TGxtaWmlz/f17Ly3AwMCQQAYGu2+DQoKIM+i6nCRLl6Spto2a4hZqSlqoKW6mpqSFlnrrv94bHGkkKimYyAQTEfEmIhKCiIxXiWl3UQmo76yaX8CyL/IJiwnk9DtGEBiipq4riqIcCFQCqih7IKWkoLaVZfm1LN1ey9L8Wqqb/0oO9FrBgMQwRqZFMDI9gmGpEcSF+meF3WZbM2uq1rCqchWrKlexsXYjdtdfW5oE6gIZFD2IQdGDGBA9gAFRA0gwJfhkOqizuZm2tetoW7cWy9p1tK1bh7O+/m/v0SUkENC/PwH9+7UfB6CLjfF4fA6XJLfVwtqmVtY0t7KhpY3NLW3/Skoj9VqyTQH0MwXSL9h97GsKINgDo6VtLTZqS83UlrZQV9pCbZmZujLz3/Ym3ckUbiQiPoiIBBPhsUGExwUSHhtEcGQAGo2ayustKgH1jbwVlSx8ayOBIXpOv2MEYTG+q/ytKIqieJdKQBWlA6SU7Kgxs7KwnlUF9awsrGN7tflv74kPDWBwchhDUsIZkhzOoOQwwgL9r7CRzWljc91m1latZW31WtbVrKPCXPG390QYI+gf1X/XLTsim6SQJDTCu9OQpZTYS0tpW7sWy7p1WDZuwrJ5My7z3/+stRERGLOzCcjug7FPNsbsbIy9stAEePYigFNKtrda2dDSxvrmVja2tLHZbKHa5vjXe5OMenoHBdDbZKR3UAB9TAH0DgogytC1kUrpkjTXWagrN1Nf3kp9hdl9v6IVW9u/49DqNITFupPR0JhAwmICCYsOJDQmkJBIo1o/10UqAfW+8m0NfPn8GhBwys3DiM/03Tp2RVEUxftUAqoonVRntpFTWM+qonrWFjewrqSRFuvfE4L0qCD6J4YyIDGM/gmhDEgMJdYPR0orzBVsrN3IptpNbKzdyMaajTRYG/72HpPeRJ+IPvSJ6EN2ZDZ9IvqQFZZFsME7hYR2ki4X9qIiLJs2/XXbkouzru7vb9RoMKSmYsjKwpiVhbFXFobMLIyZGWiCPDt6UmNzsMXcxhazhc3tSWme2UKz0/Wv90botGQGGckMMpIVaCQzKICsICPpAQZMXRg1lVLS2mSjvqKVxqpWGipbaahqo6GylabqNlyuf3+HazSC4KgAwqIDCIkKJDQ6gJCoAEKjAgmJCiAoxIBQo6f/SSWg3tVQ1cpnT67CYrYz6YqB9BoR6/U2FUVRFN9SCaiieIjLJcmvMbcnow2sKWlkS3nTrkq7O0UHG+mXEEJ2XAh94t3H3nHBBHVxpMyTpJSUm8vZULOBLXVb2Fq/ldz63H+NlALEm+LJCs+iV1gv9zG8F+lh6YQYQrwao6OmBktuLtbcrVhzc7Hk5mLLz0fabP96ry4xAWN6Bob0dPctw33UJyYitJ5ZyyulpNLmYKvZwtZWd0Ka12olv9VKhc2+x89E63WkBxpICzSSFmggPdBIaoCBlAAD8UY92k5OM3Y5XTTVWmiqbqOxuo3GmrZd95uq23DY/50og3v01BRhJCTSSEhEAMGRAQRHGAmOcB9N4UaMQV1fA9uTqQTUeywtdj59aiWNVW2MPS2L4cemebU9RVEUpXuoBFRRvMjhdJFfY2ZTWRMbyxrZVN7ExrImGlr/nZCkRgbRJy6YrJhgMmNMZMW470eY/KfwRqO1kdy6XHLrc8mrz2N7w3a2N27HbDf/671RAVGkh6WTHppORlgG6aHppISkkBSShFFr9Ep80unEXlKCdXs+1u3bsG3bjnXbNmw7duBqbf33B/R6DImJ6FNSMKSmoE9JxZCSjD4lFX1Sose2iTE7nOS3WdneamVH+7GgzUpBm40a+7+n0QLoBCQZ3cloSqCBJKOBxAA9SUYDSQF6Eo0GgjoxnVZKSVuznabaNpprLTTVtB9rLbTUWWius+Cw7TlBBdDp3UlqcLg7IQ0KM2IKM2AKMxK021Fv1B6QiapKQL3DYXfy1QtrKN/WqPb6VBRFOcCpBFRRfExKSVWzldyKZrZWNpNb0Uxupfu+ZQ8jUxFBejJjgkmPMpEeFURadPsx0kRYUPevMZVSUtlaybaGbe6EtGE7BU0FFDQWUG+t/9f7BYI4UxwpISm7bknBSSQGJ5IUnERUQJTHf/GUUuKsqcFWUIB1xw5sBYXYCgqwFRZiLylBWv9dfRZAExaGPjERfVKi+5iYiD4+AX1CPLr4BHTRUV0eQTU7nBRabLsS0mKL+1ZisVFksdG6h2m9O0XotCQY9cTvfjP8dT/WoCdar0O3H9NqpZRYWx001+1MSK2YG6y0NFjcx3or5nrrXkdRd9LpNQSGGghqvwWGGggKMRAYYiAwRP/XMdhAQLC+xxROUgmo50mX5Id3NpG3opLUAZGccO1gtVZZURTlAKYSUEXxEy6XpLShje3VLWyvNrO9uoX89vu7V+DdXXiQnpSIIJIjAkmJdB+TIwJJiQgiITyQYGP3TutttDayo3EHBU0FFDYVUtxcTFFTESXNJTTbm/f4GaPWSIIpgcTgRBJMCcSZ4ogPiifeFL/rfpDec2s6pcuFo7oGe3ERtuKSv45lZdhLS3FUVv5tq5i/0enQxcagj4tHFxfnvh8bi273W3Q0mpCQTiXVUkrqHc5dCWmZxU6p1UaZ1U6ZxX2ssNr5r1RQAFF6HbEGHXFGPTEGHTHtiWm0QffX0aAjUq/DqNn3L/47k1Rzo5XWRttfxwYr5kYbrU1WWptstDXZsFn+XcF3T0EGBOkJCNYTYHIfA4P/emwM0rmfN+kxmvQEmHQYAnXdMsqqElDP+/OrfFZ+V0BUUjCn3TZcbTGkKIpygFMJqKL0AM0WO4W1rRTWtlJQa6aw1kxBbSuFtea/7Vf6TyEBOpLCA0kICyAhPJDEsADiwwKJCzUSFxpAXEgAoYG+X9MnpaTR2uhOSJuLKDeXU9pSSllL2a6bzfXvtZw7hRhCiA2MJSYohtigWGICY4gJiiE6MJrowGiiAqKIDozGpDd1fc9Omw17ZSX2UndCaq+swFFRib2ivP1Ygaup6T/PIQwGtNFR6KKi0UVHo4uOQhsZhS4qEm1EJNrICHSRke7nIsIRho5Pu3ZKSbXNQUV7Mlphs++6X2WzU21zUGmzU2Nz/GeiulOwVkOU3p2MRup1RBq0ROp0ROi1hOvdxwidjnC9lnCd+7kQrWavf84Om5PWJtuum6XFTluLjbam9mOzjbYWe/vzdpz7GFndnUYrMAbpMAbp2486jIHu5NQY1H5sfxwcGUBir/AOn3tvVALqWbl/VvDjO5sICjNw5l0jCY7wvyJtiqIoimepBFRRejiL3Ul5o4XiulZK6tsorncfyxvaKG+0UNFkwbmHiqg7GXUa4kIDiA0xEh1sJOZvRwNRwe5jpMlAsNE3yapLuqhtq6XCXEFFawWV5spd9yvMFVS3VlPVVoXDtef1k7t+Nq2R6MBoIgMiiQiI+OtodB8jAiIIN4YTbgwnzBhGiCGkU1vNuMxmHNXV2KuqcFRV46iqar9V4qipxVFTg6O2FldjY4fOpwkKQhsejjYiwn3ceQsLRRsWhiY0DG1YmPtxaCia0FC0ISGIwMC9/v04paTO7qDSaqfW7qSmPTmtsTuosTmotjmos/9121NF373GC4TptITpte6jTkto+y1kt8chWi0hOg0hWi3But3vawjSuJNYu82JpT0htbTYsbTasZrtWMx2LGbHrvvWNgfWVvdja6tjn1OCE3uHc+qtwzv8M+2NSkA9p3xbA188vxqNEJx623Bi00I9en5FURTFP+2tL1XzXxSlhwjQa8mINpERbdrj606XpKrZQlmDhfLGNiqbrFQ1WahscienVU1WKposFNXtoUjPPxi0GiJNhl238CA9EUEGIoL0hAcZiDDpCQ80EBqoJ2y3m0G3f0mdRmiICXKPag5i0B7f45IuGq2NVLVWUd1WTVVrFTVtNdS21bqPllpq29y30pbSDrWrFVpCDaGEGcMINYYSZnAfQw1/3UIMIYQYQgg2BLvv6933g1MSMaWn/+f5XTYbztq/ElJnXT3O+joctXU46+pw1NfhrG/AWV+Ps6EBe1nZfv25odOhDQlBExqCNjgETUgImmATWlPwrvsJwcEkmUxo/nkLMaEJCkUTFIQmMBCbVku93Umt3UG93UG93UmDw0GD3Uld++NGh/u5xvb71TYHBW17H7n+LxrApNUQrNNi0mrc97VaTAYNpkANpjgdQVoDJq2GoPZboFZDuMZ93ygFeocLrdWF1iYRVicaqwvR6sBlcWIK807hK18QQvQBNgJHSimX/OP519sf5gC3SimlEGIhsHMYfTQwSkq5XgjRBvzZ/vz7Usq3fPMT/FtjdRvfvboel0Ny7FUDVPKpKIqiqARUUQ4UWo0gISyQhLBAIGKv7zNbHdS0WKlpsVLdbKW6xUZ1s5XaFit1Zhu1Zht17bfNFU17XRa5J4F6LWGBekICdIS2H0MCdh51hBh1BBt1mIzux6bdHgcZtJgM7vu7J7Iaodk1iplN9n+2b3PaqLPUUW+pp95ST531r/sN1gYarA00Wht33S83l1PQVNDxH7CdXqMnWB+MSW8i2OA+mvQmgnRBBOmDdh1NehOB8YEEJgcSpEshUNeHQF0ggfpAAnWBmLSBBOgCMKJD12JBNjbhbGjA2diEs7ERV1Pj/7d3/0GS1OUdx9+f7p6ZvePu8OAwgAYvYCGYoKDnb9RDYhBNrMSYqEGsQw2mkpSapGIZiRWTaPxdqdJoShRDaaDEEKMlxKAV5URR8TDAoZHSCFogWhzcsbt3Oz/7yR/dszt37h3H7c7M7vTnVTXV3d9v//g+17v73DPd0zM/35ueJp+ZoTczQz49TW9mht59u+j8+CcPe/yDVKuRrF1L/ai1nDC1hketWYPWTJGsWUsyNUWydg2aWkMy1dhvGo0GzVqdZq3GXFZjX63GbJoxm9WYSTNm04zpJGU6zdijlN1Jygxitttjby9ntpezu9OmeYir9g9XWoezG+u5atn2OHJvBbYv0v4e4M0R8U1J/wz8OvCliPgNAEknlMs7y/XviYitoxjwobTmulz74dtoznZ4+m+fzCln+bs+zczMBahZ5RxVFnyPOXbxK6mDenmwZ1+b3fs67NnXZs++DrsHptPNDg/OdXlwrsODcx2my+ld9++l0zvywqKWirX1oihdU0tZU0+L+XrGmlrC2nrGVC1hqlb0L0wTGrWURraBqdpG1mWPZdNUytT6lHqa0KglNLKEepbQyFIaWULQYbYzw3R7mun2NA+2HmS6Pc1Me4bZ9mwx7RTTmfYMe7t72dvey2xnlj2tPdwzew/B8hRRU+kUjaxBI20U8xsbTG2aopE2ivakQSM7kUb6KzTSBvW0Tl01aj2odYOsE2TtHrV2TtbqkrW7pM0uWbNL2myT7muTzrVI59okcy3SvS3SuSbJbJN0dh/6+f2kc23SvHio0eHKgA3l65CSBNXrqNFA9RpJrQ71OlGrEfU6eZbRq9XopRndrEY3y+hkGd0so52kdLKUdpLRSjPaaUI7SWmmKS2ltJKEZpKy4VEnwpmnHPE5GBdJTwN+Biz2NKdTgf59sTcB5wBfGuh/BfCpgeXjJW0H7gf+PCLuWvYBP4S8l/PFj93O7nv38rinH8+TzvN3fZqZWcEFqJkdVJqIY9c1OHbdw7utMSJodXOmmx1mml1mml2m5zrsbXWZLV97W11mWl1mm13m2j32trvsbRXTfeV0rt1jz74Oc53DeMLqEmSJqKVFYVrPEurpOurZBmpp0T7flybUUrEpSTgxE1mSUFsjUA7qAV2CLvn8q0MebXrRIadDN9r0ok23/8rbdKNFJ1p08jaddpNOs8V03uL+aNHJW+RMI+VAADmoP80RURx7ftrvj3Kbsm+qfG2Mcj+BdLCiuUgLqRIy1ciUkpGQkZIiMhLSSEgDshBJLrIckoC0B0keZOU06QVJLyftBUm3mE+65as3h7qz5XKvWLe/nzxIApIWJM0ipCSY79fAfBKwNmBd2f5L9z4OXvX7Q/15GZJLgIuA9y/StxN4gaRrgfOBBw7ovwB46cDy5ojYJek84DLg3EMdWNLFwMUAJ5100pGN/gBfu/qH/OS7D3DCY4/mnAtO83d9mpnZPBegZrbsJDFVXpl85Pql7y/Pg2a3x752j7l2j7lOj2ZncD6nWba1uvmi03Y3p9XNy2nR3urmdHpF23x/L2e21aW7L6fTC9oP4yE9i6uVr5WoX4guTBFFYUvMF7MLr5wYaIt+gdu/AqxY2LZc3q+fsmKsDyzPr1eOZ5G2hWmUl2YP7FuYP/GoJi8+wn+NcZH0ImBHRNx/kELtL4APAm8Efgj8dGDb04G5iLiz3xYRu8rpdZI+9FDHj4hLKT9jumXLliVfzt95/d3s/MrdbNg0xfl/dAZpzd/1aWZmC1yAmtmKlyT9W3JH/ycrIujmMV+odnrFfLcsTrt5TqcbdPKcXr7Q182LdXt5sX1vcLmXl22D/f35nF4OeSy09dfJ86AXC9NeHvPr5cFCfznf74so91duGzC/TcTAejC/bh5BXo4joiw359cv1zlgmyCI/jZle5Tzy/hRz0OaWp3PuDkT2CrpmcAZwGmSXhYRPwaIiLuB31FRnX4C+MzAthcCV/QXJK2jKEh7kp4A7BpRDEDxc/eDHT+nPpXyoj9+ImvWHf5XDZmZWTW4ADUzOwRJ87firvX/pZcsIgYKVuaL2/7DrvbvW1iHgfX6fcB++yAgS1ff1baIeAfwDgBJlwMfA06X9JyI+KSkPwD+kCLcT0bE7eW6orj19hkDu3s88BFJM+X6rxtZIBRvFr34DWey+959HHPiQ3/O3MzMqscFqJmZjYwkJEge1mOOqiMiti3SdiVw5SLtQfGAosG2m4CzhjW+w5HVUo47aRnuvTczs4m0+t4qNjMzMzMzs1XJBaiZmZmZmZmNhAtQMzMzMzMzGwkXoGZmZmZmZjYSLkDNzMzMzMxsJIZagEraJulGSV+X9KQD+qYkXSHphnI6VbZvlvTlcpu3DHN8ZmZmZmZmNjpDK0AlbQReD2wFXgl84IBVtgHfj4hnA3eUywDvAv4mIp4FPE/SacMao5mZmZmZmY3OMK+APhW4ISLaEXEnsF5SY6D/ucA15fzny2WAMyPihnL+2oF2MzMzMzMzW8WGWYAeC+weWN4DHHOQ/sG+wTEduM1+JF0saYekHffdd98Sh2tmZmZmZmbDNMwC9AHgEQPLR5dti/UP9uWH2GY/EXFpRGyJiC3HHXfcUsdrZmZmZmZmQ5QNcd/fAt4uqQacAMxGRGugfzvwQuCWcrq9bL9V0jMj4kbgfOCNh3Owm2++eZekHy/T2DcBu5ZpX6tFFWOGasbtmKujinEvV8yPWYZ9rDrLmEur+LMH1YzbMVdHFeN2zEuzaC5VRCzT/hfZufRq4LVAAG8AusDzI+K9ktYAHwceDdwNXBQRTUknA5cBdeALEfH2oQ3w4OPeERFbRn3ccapizFDNuB1zdVQx7irGvBJV9TxUMW7HXB1VjNsxD8cwr4ASER+nKDIH3VL2zQGvWGSbHwHnDHNcZmZmZmZmNnpD/R5QMzMzMzMzsz4XoIu7dNwDGIMqxgzVjNsxV0cV465izCtRVc9DFeN2zNVRxbgd8xAM9TOgZmZmZmZmZn2+AmpmZmZmZmYj4QLUzMzMzMzMRsIFqJmZmZmZmY2EC1AzMzMzMzMbCRegB5C0TdKNkr4u6UnjHs8wSLpO0n2S/rpclqQPSrpB0jWSjhn3GJebpLPKc/pVSV+WdLKkKUlXlHFfIWlq3ONcbpI2lD/P10u6SdK5VTjfAJJOldSRdHZFzvVceZ6vl/SaCp3nJ0v6oqSvSHpPVeJeyaqQR8G5tCq51Hm0OnkUqplLx5FHXYAOkLQReD2wFXgl8IGxDmh4XgP85cDyecDaiHg28GngTWMZ1XDdC7wgIp4DvA/4W2Ab8P0y7jvK5UkzC+oges0AAAY3SURBVDwnIrYCLwfeRTXON8Bbge3l/DYm/1zfExFby9dlVOA8S6pT/Ez/bkScExFvogJxr2QVyqPgXFqVXOo8WtjGZJ/nvkrl0nHlUReg+3sqcENEtCPiTmC9pMa4B7XcIuLuA5qeC1xTzn++XJ4oEfGziJgpF1tAl2rEnUdEt1zcANxGBeKW9DTgZ0D/Z33iYwaOl7Rd0mckbaYaMT+D4j+HV5ZXY55NNeJeySqRR8G5lIrkUufRSuVRqF4uHUsedQG6v2OB3QPLe4CJu9S+iMG49wAbxzeU4ZJ0FPB24L38YtwTea4lPUrS14AvAv9BNc73JRTv6PVV4VxvjojnAh8BLqMa5/lE4InABcCFwEeBTUx+3CtZVfMoVON3DqheLnUeBSpwnktVy6VjyaMuQPf3APCIgeWjy7ZJNxj30ez/n4eJIakGXAW8OyK+xy/GPZHnOiLuiYizKa5M/BMTfr4lvQjYERH3DzRP/LmOiF3l9DrgMUz4eS49ANwYEdMRcQ+wC0iZ/LhXsqrmUajG71wlc6nzKFCB8wyVzKVjyaMuQPf3LeBsSTVJJwGzEdEa96BGYDvwwnL+hSzc7z8xJCXAvwKfjYjPls1ViHvw1rdpYIbJj/tMYKuk/wKeT/E5pf9lgmOWtE5SWs4/gSKBTPp5huJv9qmSMknrgUcC/87kx72SVTWPQgV+56qYS51Hq5FHobK5dCx5VBGx3Ptc1SS9GngtEMAbImLHmIe07CR9FHgm0ABuB14CfBB4AsUf11cd8K7XqifppcDlQP987qT4UPXHgUdTfMbhoohojmWAQyLpycA/Aj0gA94GfIUJP999ki4HPgbczASfa0lPpbhdaIbib9frKX7GJ/48S7oQeB1Qo7hd7HNUIO6VrAp5FJxLy6aJz6XOo9XIo1DdXDqOPOoC1MzMzMzMzEbCt+CamZmZmZnZSLgANTMzMzMzs5FwAWpmZmZmZmYj4QLUzMzMzMzMRsIFqJmZmZmZmY2EC1AzMzMzMzMbCRegZstE0rck3SLpe5K65fwtkv5F0t9JetkQj71N0h5JNx7BtlskXbHE42+WdPEy7OMWSW1Jv7aUfZmZ2erjPOo8atXg7wE1W2aSNgM7ImLTCI+5DfjNiHjpqI55wPG3Au+LiC3LsK+7KGK5fan7MjOz1cd5dMn7ugvnUVvBfAXUbAQkXS7pT8v5t0n6lKT/lPRDSVdJOkvSlyX9n6T3Dmx3gqSrJd0kaaektzyMY4akSyR9W9KPJJ0r6Z2S/kfS7ZJOL9fbKmlHOb9Z0i5J7yjXu0PS2Qeut8jyh4DHl++8Xl32P07SF8rj3yrporJ9raR/K9/hvlXSp5f2r2tmZpPOedR51CZHNu4BmFXUk4EtwCzwHeBdwPkUv5N3Sro0In4AfAL4+4j4qqQ68N+Svh0RXzrM4+yJiKdI+j3gc8DLI+KvJL0JuAR45SLbHAt8IyIukXQB8G7gWQ9xnD9h4J1bSRlwJXBBRHxf0npgh6RvAKcDGyLi8eW6Gw8zFjMzsz7nUedRW6VcgJqNx3UR8SCApNuAWyOiBbQk3QGcIumnwFbgOEn97dZTJJ7DTZxXldPvABER15TLNwMvOcg2swPrfRN4/2Eea9Cp5Tg/NTD2Rtl2K3C6pA8B1wPXHsH+zcys2pxHnUdtlXIBajYezYH53iLLGcUt8gE8JSI6SzxOD2gtcozFHGy9Lvvftj91iOMK2BURZy7aKf0qcC7Fu9X/IOmMiGgutq6ZmdkinEedR22V8mdAzVaoiJgBbgDe3G+T9MuSjh/TkH4EnCxpo4q3Y18x0DcNHD2wfAewT9KF/QZJp0naIOnRQC8iPgv8GXAccMzQR29mZpXiPGq2MrkANVvZLqB4KMFOSTspbgV6xDgGEhE/pbiN6GbgRuDege7bgDvKhzJcHRFd4LeAl0u6TdJ3gQ8DdeAM4BuSbgVuAt5Z7tvMzGy5OY+arTD+GhazCaAxPz5+OcmPjzczsxFzHjUbHV8BNZsMc8AWHcEXaK8U5aPrbwFqwJF+VsfMzOxIOI+ajYivgJqZmZmZmdlI+AqomZmZmZmZjYQLUDMzMzMzMxsJF6BmZmZmZmY2Ei5AzczMzMzMbCT+H0EV/S5TIVuxAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -331,7 +341,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7f8bc446ed10>"
+       "<pybamm.solvers.solution.Solution at 0x7f3428ad3350>"
       ]
      },
      "execution_count": 8,
@@ -417,7 +427,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -515,9 +525,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "PyBaMM development (env)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pybamm-dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
index 1133e1aa51..0658664705 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/plating_exchange_current_density_OKane2020.py
@@ -26,4 +26,4 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
 
     k_plating = 1e-10
 
-    return constants.F * k_plating * c_e ** 0.5
+    return constants.F * k_plating * c_e
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
index 98022ee856..cad0f0ad5a 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/okane2020_Li_plating/stripping_exchange_current_density_OKane2020.py
@@ -32,4 +32,4 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
 
     k_plating = 1e-10
 
-    return constants.F * k_plating * c_Li ** 0.5
+    return constants.F * k_plating * c_Li

From 38e262ba02ae1b6e9fe8b5fcf31ad6a188c42e18 Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Thu, 18 Mar 2021 14:01:05 +0000
Subject: [PATCH 29/46] Changes for tests

---
 .../nmc_Yang2017/README.md                    | 12 ++++
 .../measured_nco_diffusivity_Ecker2015.csv    | 15 +++++
 .../nmc_Yang2017/nco_diffusivity_Ecker2015.py | 37 ++++++++++++
 ...lyte_exchange_current_density_Ecker2015.py | 48 +++++++++++++++
 .../nmc_Yang2017/nco_ocp_Ecker2015.csv        | 43 ++++++++++++++
 .../nco_ocp_Ecker2015_function.py             | 56 ++++++++++++++++++
 .../nmc_Yang2017/parameters.csv               | 26 ++++++++
 pybamm/parameters/parameter_sets.py           |  2 +-
 .../test_base_battery_model.py                |  6 ++
 .../test_lithium_ion/test_Yang2017.py         | 21 +++++++
 .../test_parameter_sets/test_Yang2017.py      | 59 +++++++++++++++++++
 11 files changed, 324 insertions(+), 1 deletion(-)
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/measured_nco_diffusivity_Ecker2015.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_diffusivity_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_electrolyte_exchange_current_density_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015_function.py
 create mode 100644 pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv
 create mode 100644 tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py
 create mode 100644 tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py

diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md
new file mode 100644
index 0000000000..5c0e90bb7d
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md
@@ -0,0 +1,12 @@
+# Lithium Nickel Cobalt Oxide positive electrode parameters
+
+Parameters for a Lithium Nickel Cobalt Oxide positive electrode, from the papers
+
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery i. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
+
+>Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+
+The fits to data for the electrode and electrolyte properties are those provided
+by Dr. Simon O’Kane in the paper:
+
+> Richardson, Giles, et. al. "Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation." Electrochemica Acta 339 (2020): 135862
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/measured_nco_diffusivity_Ecker2015.csv b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/measured_nco_diffusivity_Ecker2015.csv
new file mode 100644
index 0000000000..72c9f0796c
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/measured_nco_diffusivity_Ecker2015.csv
@@ -0,0 +1,15 @@
+0.13943217665615135,1.8256540288152508e-13
+0.2,3.329858559514642e-13
+0.2618296529968453,3.0801228532525374e-13
+0.32239747634069404,2.6333920293821577e-13
+0.38675078864353307,1.9811969945521707e-13
+0.4460567823343848,1.418878868334243e-13
+0.5078864353312302,7.201182417750914e-14
+0.5684542586750789,2.857708698828691e-14
+0.6315457413249213,4.541608403537004e-15
+0.6933753943217664,5.479444754774098e-14
+0.7526813880126184,2.0296886656972416e-13
+0.8157728706624605,1.5882865084008814e-13
+0.8750788643533123,1.3446092039291705e-13
+0.9406940063091483,2.0545053272039484e-14
+1.0,5.4462929807295865e-15
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_diffusivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_diffusivity_Ecker2015.py
new file mode 100644
index 0000000000..b30be3138f
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_diffusivity_Ecker2015.py
@@ -0,0 +1,37 @@
+from pybamm import exp, constants
+
+
+def nco_diffusivity_Ecker2015(sto, T):
+    """
+    NCO diffusivity as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_ref = 3.7e-13 - 3.4e-13 * exp(-12 * (sto - 0.62) * (sto - 0.62))
+    E_D_s = 8.06e4
+    arrhenius = exp(-E_D_s / (constants.R * T)) * exp(E_D_s / (constants.R * 296.15))
+
+    return D_ref * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_electrolyte_exchange_current_density_Ecker2015.py b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_electrolyte_exchange_current_density_Ecker2015.py
new file mode 100644
index 0000000000..e804f00ceb
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_electrolyte_exchange_current_density_Ecker2015.py
@@ -0,0 +1,48 @@
+from pybamm import exp, constants, Parameter
+
+
+def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between NCO and LiPF6 in
+    EC:DMC [1, 2, 3].
+
+    References
+    ----------
+       .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_ref = 5.196e-11
+
+    # multiply by Faraday's constant to get correct units
+    m_ref = constants.F * k_ref  # (A/m2)(mol/m3)**1.5 - includes ref concentrations
+
+    E_r = 4.36e4
+    arrhenius = exp(-E_r / (constants.R * T)) * exp(E_r / (constants.R * 296.15))
+
+    c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]")
+
+    return (
+        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5
+    )
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015.csv b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015.csv
new file mode 100644
index 0000000000..eb8776016f
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015.csv
@@ -0,0 +1,43 @@
+0.001066078485433497, 4.584263211444843
+0.041273779983457604, 4.542437342855614
+0.065398400882272, 4.52965007211667
+0.08722543883834222, 4.523324753404195
+0.11154152498238534, 4.4989323580008005
+0.13547468063597112, 4.46985870879382
+0.15940783628955701, 4.429538485743097
+0.18174544823290373, 4.389207099839924
+0.2056786038864893, 4.3421389324829525
+0.228016215829836, 4.29955823181103
+0.25115159962830247, 4.2547337977965825
+0.275084755281888, 4.216662889514609
+0.2982201390803543, 4.178586399806408
+0.3221532947339403, 4.147263435830681
+0.3452886785324063, 4.111436260891228
+0.3692218341859923, 4.082362611684249
+0.3939527616946974, 4.055543858672244
+0.41469482992780504, 4.030946513297861
+0.4402235292916299, 4.006382656480829
+0.46176336937985707, 3.9840402073014207
+0.4864942968885624, 3.9684680281331595
+0.5088319088319091, 3.9528791046862226
+0.5327650644854949, 3.941799973629234
+0.5566982201390807, 3.923972898265999
+0.5782380602273078, 3.919624967236581
+0.598182356605296, 3.913016558585964
+0.6484419834778261, 3.911118873669402
+0.6723751391314119, 3.9112863164561573
+0.6971060666401172, 3.897963452056645
+0.7194436785834639, 3.882374528609708
+0.7433768342370495, 3.860048823708975
+0.7649166743252769, 3.817462541610827
+0.7880520581237431, 3.7883833109776224
+0.8127829856324482, 3.759315243196868
+0.8351205975757949, 3.732479745906187
+0.8590537532293809, 3.7079047262367046
+0.8821891370278472, 3.6810748103722486
+0.9053245208263132, 3.649746264970295
+0.9300554483350187, 3.6116809381145454
+0.9515952884232457, 3.5780919150913926
+0.9683484973807557, 3.557965292123382
+0.9827083907729073, 3.5423205544141934
+0.9994615997304173, 3.5199446166774333
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015_function.py b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015_function.py
new file mode 100644
index 0000000000..008c173edb
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/nco_ocp_Ecker2015_function.py
@@ -0,0 +1,56 @@
+from pybamm import tanh
+
+
+def nco_ocp_Ecker2015_function(sto):
+    """
+    NCO OCP as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto : :class:`pybamm.Symbol`
+       Stochiometry of material (li-fraction)
+
+    """
+
+    # LiNiCo from Ecker, Kabitz, Laresgoiti et al.
+    # Analytical fit (WebPlotDigitizer + gnuplot)
+    a = -2.35211
+    c = 0.0747061
+    d = 31.886
+    e = 0.0219921
+    g = 0.640243
+    h = 5.48623
+    i = 0.439245
+    j = 3.82383
+    k = 4.12167
+    m = 0.176187
+    n = 0.0542123
+    o = 18.2919
+    p = 0.762272
+    q = 4.23285
+    r = -6.34984
+    s = 2.66395
+    t = 0.174352
+
+    u_eq = (
+        a * sto
+        - c * tanh(d * (sto - e))
+        - r * tanh(s * (sto - t))
+        - g * tanh(h * (sto - i))
+        - j * tanh(k * (sto - m))
+        - n * tanh(o * (sto - p))
+        + q
+    )
+    return u_eq
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv
new file mode 100644
index 0000000000..0ed194bc51
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv
@@ -0,0 +1,26 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrode properties,,,
+Positive electrode conductivity [S.m-1],68.1,,
+Maximum concentration in positive electrode [mol.m-3],48580,,
+Measured positive electrode diffusivity [m2.s-1],[data]measured_nco_diffusivity_Ecker2015,,
+Positive electrode diffusivity [m2.s-1],[function]nco_diffusivity_Ecker2015,,
+Measured positive electrode OCP [V],[data]nco_ocp_Ecker2015,,
+Positive electrode OCP [V],[function]nco_ocp_Ecker2015_function,,
+,,,
+# Microstructure,,,
+Positive electrode porosity,0.33,Yang 2017,
+Positive electrode active material volume fraction,0.40832,,
+Positive particle radius [m],5.00E-06,Yang 2017,
+Positive electrode Bruggeman coefficient (electrolyte),1.544226719,Solve for permeability factor B=0.1526=eps^b,
+Positive electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to solid conductivity,
+Positive electrode exchange-current density [A.m-2],[function]nco_electrolyte_exchange_current_density_Ecker2015,,
+,,,
+# Interfacial reactions,,,
+Positive electrode cation signed stoichiometry,-1,,
+Positive electrode electrons in reaction,1,,
+,,,
+,,,
+# Thermal parameters,,,
+Positive electrode OCP entropic change [V.K-1],0,,
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 563fbf07fe..de3ad30946 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -111,7 +111,7 @@
     "cell": "Yang2017",
     "negative electrode": "graphite_Yang2017",
     "separator": "separator_Ecker2015",
-    "positive electrode": "LiNiCoO2_Ecker2015",
+    "positive electrode": "nmc_Yang2017",
     "electrolyte": "lipf6_Ecker2015",
     "experiment": "1C_discharge_from_full_Ecker2015",
     "sei": "yang2017_sei",
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 4eb52c97bf..6ca7d9869d 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -237,6 +237,12 @@ def test_options(self):
                 }
             )
 
+        with self.assertRaisesRegex(
+            pybamm.OptionError,
+            "lithium plating porosity change must now be given in string format",
+        ):
+            pybamm.BaseBatteryModel({"lithium plating porosity change": True})
+
     def test_build_twice(self):
         model = pybamm.lithium_ion.SPM()  # need to pick a model to set vars and build
         with self.assertRaisesRegex(pybamm.ModelError, "Model already built"):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py
new file mode 100644
index 0000000000..8dfe6a98ce
--- /dev/null
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py
@@ -0,0 +1,21 @@
+#
+# Tests for the lithium-ion DFN model
+#
+import pybamm
+import unittest
+
+
+class TestYang2017(unittest.TestCase):
+    def test_well_posed(self):
+        model = pybamm.lithium_ion.Yang2017()
+        model.check_well_posedness()
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py b/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py
new file mode 100644
index 0000000000..2030cbb851
--- /dev/null
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py
@@ -0,0 +1,59 @@
+#
+# Tests for  Yang2017 parameter set loads
+#
+import pybamm
+import unittest
+
+
+class TestYang2017(unittest.TestCase):
+    def test_load_params(self):
+        negative_electrode = pybamm.ParameterValues({}).read_parameters_csv(
+            pybamm.get_parameters_filepath(
+                "input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/"
+                "parameters.csv"
+            )
+        )
+        self.assertEqual(negative_electrode["Negative electrode porosity"], "0.32")
+
+        positive_electrode = pybamm.ParameterValues({}).read_parameters_csv(
+            pybamm.get_parameters_filepath(
+                "input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/"
+                "parameters.csv"
+            )
+        )
+        self.assertEqual(positive_electrode["Positive electrode porosity"], "0.33")
+
+        electrolyte = pybamm.ParameterValues({}).read_parameters_csv(
+            pybamm.get_parameters_filepath(
+                "input/parameters/lithium-ion/electrolytes/lipf6_Ecker2015/"
+                + "parameters.csv"
+            )
+        )
+        self.assertEqual(electrolyte["Cation transference number"], "0.26")
+
+        cell = pybamm.ParameterValues({}).read_parameters_csv(
+            pybamm.get_parameters_filepath(
+                "input/parameters/lithium-ion/cells/Yang2017/parameters.csv"
+            )
+        )
+        self.assertAlmostEqual(cell["Negative current collector thickness [m]"], 25e-6)
+
+    def test_standard_lithium_parameters(self):
+
+        chemistry = pybamm.parameter_sets.Yang2017
+        parameter_values = pybamm.ParameterValues(chemistry=chemistry)
+
+        model = pybamm.lithium_ion.DFN()
+        sim = pybamm.Simulation(model, parameter_values=parameter_values)
+        sim.set_parameters()
+        sim.build()
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()

From 3b1811eeb14f22ba151653fb21fa84b852cb7ed0 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Thu, 18 Mar 2021 14:15:49 +0000
Subject: [PATCH 30/46] Update README.md

---
 .../lithium-ion/positive_electrodes/nmc_Yang2017/README.md      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md
index 5c0e90bb7d..508d87c3f2 100644
--- a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/README.md
@@ -4,7 +4,7 @@ Parameters for a Lithium Nickel Cobalt Oxide positive electrode, from the papers
 
 > Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery i. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
 
->Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
 
 The fits to data for the electrode and electrolyte properties are those provided
 by Dr. Simon O’Kane in the paper:

From 386e135471e58fb0e5b68a6ccca34fd3f1d6540a Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Thu, 18 Mar 2021 17:44:30 +0000
Subject: [PATCH 31/46] Changes for tests

---
 .../electrolyte_conductivity_PeymanMPM.py     |  33 ++++
 .../electrolyte_diffusivity_PeymanMPM.py      |  34 ++++
 .../LiPF6_Mohtat2020/parameters.csv           |  10 ++
 .../README.md                                 |   5 +
 ...ctrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py |  32 ++++
 .../electrolyte_base_Landesfeind2019.py       | 155 ++++++++++++++++++
 ...conductivity_EC_DMC_1_1_Landesfeind2019.py |  32 ++++
 ..._diffusivity_EC_DMC_1_1_Landesfeind2019.py |  32 ++++
 ...rence_number_EC_DMC_1_1_Landesfeind2019.py |  44 +++++
 .../parameters.csv                            |   9 +
 .../README.md                                 |   5 +
 ...ctrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py |  32 ++++
 .../electrolyte_base_Landesfeind2019.py       | 155 ++++++++++++++++++
 ...conductivity_EC_EMC_3_7_Landesfeind2019.py |  32 ++++
 ..._diffusivity_EC_EMC_3_7_Landesfeind2019.py |  32 ++++
 ...rence_number_EC_EMC_3_7_Landesfeind2019.py |  44 +++++
 .../parameters.csv                            |   9 +
 .../README.md                                 |   5 +
 ...rolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py |  32 ++++
 .../electrolyte_base_Landesfeind2019.py       | 155 ++++++++++++++++++
 ...nductivity_EMC_FEC_19_1_Landesfeind2019.py |  32 ++++
 ...iffusivity_EMC_FEC_19_1_Landesfeind2019.py |  32 ++++
 ...nce_number_EMC_FEC_19_1_Landesfeind2019.py |  34 ++++
 .../parameters.csv                            |   9 +
 .../electrolytes/lipf6_Ecker2015/README.md    |  12 ++
 .../electrolyte_conductivity_Ecker2015.py     |  44 +++++
 .../electrolyte_diffusivity_Ecker2015.py      |  40 +++++
 .../lipf6_Ecker2015/parameters.csv            |   9 +
 .../lipf6_Enertech_Ai2020/README.md           |   9 +
 .../lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py |  34 ++++
 .../electrolyte_conductivity_Ai2020.py        |  40 +++++
 .../electrolyte_diffusivity_Ai2020.py         |  28 ++++
 .../lipf6_Enertech_Ai2020/parameters.csv      |   9 +
 .../electrolytes/lipf6_Kim2011/README.md      |   7 +
 .../electrolyte_conductivity_Kim2011.py       |  35 ++++
 .../electrolyte_diffusivity_Kim2011.py        |  35 ++++
 .../electrolytes/lipf6_Kim2011/parameters.csv |   9 +
 .../electrolytes/lipf6_Marquis2019/README.md  |   7 +
 .../electrolyte_conductivity_Capiglia1999.py  |  40 +++++
 .../electrolyte_diffusivity_Capiglia1999.py   |  34 ++++
 .../lipf6_Marquis2019/parameters.csv          |   9 +
 .../electrolytes/lipf6_Nyman2008/README.md    |   7 +
 .../electrolyte_conductivity_Nyman2008.py     |  29 ++++
 .../electrolyte_diffusivity_Nyman2008.py      |  27 +++
 .../lipf6_Nyman2008/parameters.csv            |   9 +
 .../electrolytes/lipf6_Ramadass2004/README.md |   8 +
 .../electrolyte_conductivity_Ramadass2004.py  |  43 +++++
 .../electrolyte_diffusivity_Ramadass2004.py   |  32 ++++
 .../lipf6_Ramadass2004/parameters.csv         |   9 +
 .../graphite_Yang2017/parameters.csv          |   2 -
 .../nmc_Yang2017/parameters.csv               |   3 +-
 .../test_lithium_ion/test_Yang2017.py         |   8 +
 .../test_parameter_sets/test_Yang2017.py      |   2 +-
 53 files changed, 1569 insertions(+), 5 deletions(-)
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py
 create mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv

diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py
new file mode 100644
index 0000000000..229d22cea2
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py
@@ -0,0 +1,33 @@
+from pybamm import exp, constants
+
+
+def electrolyte_conductivity_PeymanMPM(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC as a function of ion concentration. The original
+    data is from [1]. The fit is from Dualfoil [2].
+
+    References
+    ----------
+    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
+    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
+    Journal of power sources 81 (1999): 859-862.
+    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+
+    sigma_e = 1.3
+    E_k_e = 34700
+    arrhenius = exp(E_k_e / constants.R * (1 / 298.15 - 1 / T))
+
+    return sigma_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py
new file mode 100644
index 0000000000..3014398635
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py
@@ -0,0 +1,34 @@
+from pybamm import exp, constants
+
+
+def electrolyte_diffusivity_PeymanMPM(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration. The original data
+    is from [1]. The fit from Dualfoil [2].
+
+    References
+    ----------
+    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
+    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
+    Journal of power sources 81 (1999): 859-862.
+    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+
+    D_c_e = 5.35 * 10 ** (-10)
+    E_D_e = 37040
+    arrhenius = exp(E_D_e / constants.R * (1 / 298.15 - 1 / T))
+
+    return D_c_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv
new file mode 100644
index 0000000000..528ec51dab
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv
@@ -0,0 +1,10 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,Peyman MPM,from MPM code
+Cation transference number,0.38,Peyman MPM,
+1 + dlnf/dlnc,1,,
+Typical lithium ion diffusivity [m2.s-1],5.34E-10,Scott Moura FastDFN,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_PeymanMPM,, 
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_PeymanMPM,, 
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md
new file mode 100644
index 0000000000..07aa16c3ab
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md
@@ -0,0 +1,5 @@
+# LiPF6 in EC:DMC (1:1 w:w) electrolyte parameters
+
+Parameters for a LiPF6 in EC:DMC (1:1 w:w) electrolyte, from the paper
+
+> Johannes Landesfeind and Hubert A. Gasteiger, ["Temperature and Concentration Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes."](https://iopscience.iop.org/article/10.1149/2.0571912jes) Journal of the Electrochemical Society 166 (2019): A3079.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py
new file mode 100644
index 0000000000..c57bfb0013
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import electrolyte_TDF_base_Landesfeind2019
+import numpy as np
+
+
+def electrolyte_TDF_EC_DMC_1_1_Landesfeind2019(c_e, T):
+    """
+    Thermodynamic factor (TDF) of LiPF6 in EC:DMC (1:1 w:w) as a function of ion
+    concentration and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte thermodynamic factor
+    """
+    coeffs = np.array(
+        [-5.58, 7.17, 3.80e-2, 1.91, -6.65e-2, -5.08e-5, 1.1e-1, -6.10e-3, 1.51e-4]
+    )
+
+    return electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
new file mode 100644
index 0000000000..70946112b9
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
@@ -0,0 +1,155 @@
+from pybamm import exp, sqrt
+
+
+def electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Conductivity of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6 = coeffs
+    A = p1 * (1 + (T - p2))
+    B = 1 + p3 * sqrt(c) + p4 * (1 + p5 * exp(1000 / T)) * c
+    C = 1 + c ** 4 * (p6 * exp(1000 / T))
+    sigma_e = A * c * B / C  # mS.cm-1
+
+    return sigma_e / 10
+
+
+def electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Diffusivity of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4 = coeffs
+    A = p1 * exp(p2 * c)
+    B = exp(p3 / T)
+    C = exp(p4 * c / T)
+    D_e = A * B * C * 1e-10  # m2/s
+
+    return D_e
+
+
+def electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Thermodynamic factor (TDF) of LiPF6 in solvent_X as a function of ion concentration
+    and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte thermodynamic factor
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
+    tdf = (
+        p1
+        + p2 * c
+        + p3 * T
+        + p4 * c ** 2
+        + p5 * c * T
+        + p6 * T ** 2
+        + p7 * c ** 3
+        + p8 * c ** 2 * T
+        + p9 * c * T ** 2
+    )
+
+    return tdf
+
+
+def electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Transference number of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte transference number
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
+    tplus = (
+        p1
+        + p2 * c
+        + p3 * T
+        + p4 * c ** 2
+        + p5 * c * T
+        + p6 * T ** 2
+        + p7 * c ** 3
+        + p8 * c ** 2 * T
+        + p9 * c * T ** 2
+    )
+
+    return tplus
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py
new file mode 100644
index 0000000000..52a7be3034
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_conductivity_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC (1:1 w:w) as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+    coeffs = np.array([7.98e-1, 2.28e2, -1.22, 5.09e-1, -4e-3, 3.79e-3])
+
+    return electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py
new file mode 100644
index 0000000000..4e2680c0ee
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_diffusivity_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC (1:1 w:w) as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+    coeffs = np.array([1.47e3, 1.33, -1.69e3, -5.63e2])
+
+    return electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py
new file mode 100644
index 0000000000..4be6c301ef
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py
@@ -0,0 +1,44 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_transference_number_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019(c_e, T):
+    """
+    Transference number of LiPF6 in EC:DMC (1:1 w:w) as a function of ion
+    concentration and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte transference number
+    """
+    coeffs = np.array(
+        [
+            -7.91,
+            2.45e-1,
+            5.28e-2,
+            6.98e-1,
+            -1.08e-2,
+            -8.21e-5,
+            7.43e-4,
+            -2.22e-3,
+            3.07e-5,
+        ]
+    )
+
+    return electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv
new file mode 100644
index 0000000000..46f3fc5232
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,default,
+Cation transference number,[function]electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019,
+1 + dlnf/dlnc,[function]electrolyte_TDF_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019," "
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019," "
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md
new file mode 100644
index 0000000000..2406cfaf76
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md
@@ -0,0 +1,5 @@
+# LiPF6 in EC:EMC (3:7 w:w) electrolyte parameters
+
+Parameters for a LiPF6 in EC:EMC (3:7 w:w) electrolyte, from the paper
+
+> Johannes Landesfeind and Hubert A. Gasteiger, ["Temperature and Concentration Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes."](https://iopscience.iop.org/article/10.1149/2.0571912jes) Journal of the Electrochemical Society 166 (2019): A3079.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py
new file mode 100644
index 0000000000..1feec60ba1
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import electrolyte_TDF_base_Landesfeind2019
+import numpy as np
+
+
+def electrolyte_TDF_EC_EMC_3_7_Landesfeind2019(c_e, T):
+    """
+    Thermodynamic factor (TDF) of LiPF6 in EC:EMC (3:7 w:w) as a function of ion
+    concentration and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte thermodynamic factor
+    """
+    coeffs = np.array(
+        [2.57e1, -4.51e1, -1.77e-1, 1.94, 2.95e-1, 3.08e-4, 2.59e-1, -9.46e-3, -4.54e-4]
+    )
+
+    return electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py
new file mode 100644
index 0000000000..70946112b9
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py
@@ -0,0 +1,155 @@
+from pybamm import exp, sqrt
+
+
+def electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Conductivity of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6 = coeffs
+    A = p1 * (1 + (T - p2))
+    B = 1 + p3 * sqrt(c) + p4 * (1 + p5 * exp(1000 / T)) * c
+    C = 1 + c ** 4 * (p6 * exp(1000 / T))
+    sigma_e = A * c * B / C  # mS.cm-1
+
+    return sigma_e / 10
+
+
+def electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Diffusivity of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4 = coeffs
+    A = p1 * exp(p2 * c)
+    B = exp(p3 / T)
+    C = exp(p4 * c / T)
+    D_e = A * B * C * 1e-10  # m2/s
+
+    return D_e
+
+
+def electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Thermodynamic factor (TDF) of LiPF6 in solvent_X as a function of ion concentration
+    and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte thermodynamic factor
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
+    tdf = (
+        p1
+        + p2 * c
+        + p3 * T
+        + p4 * c ** 2
+        + p5 * c * T
+        + p6 * T ** 2
+        + p7 * c ** 3
+        + p8 * c ** 2 * T
+        + p9 * c * T ** 2
+    )
+
+    return tdf
+
+
+def electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Transference number of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte transference number
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
+    tplus = (
+        p1
+        + p2 * c
+        + p3 * T
+        + p4 * c ** 2
+        + p5 * c * T
+        + p6 * T ** 2
+        + p7 * c ** 3
+        + p8 * c ** 2 * T
+        + p9 * c * T ** 2
+    )
+
+    return tplus
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py
new file mode 100644
index 0000000000..b0581eba78
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_conductivity_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:EMC (3:7 w:w) as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+    coeffs = np.array([5.21e-1, 2.28e2, -1.06, 3.53e-1, -3.59e-3, 1.48e-3])
+
+    return electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py
new file mode 100644
index 0000000000..b8fd6ea266
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_diffusivity_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:EMC (3:7 w:w) as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+    coeffs = np.array([1.01e3, 1.01, -1.56e3, -4.87e2])
+
+    return electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py
new file mode 100644
index 0000000000..b2333ab2a4
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py
@@ -0,0 +1,44 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_transference_number_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019(c_e, T):
+    """
+    Transference number of LiPF6 in EC:EMC (3:7 w:w) as a function of ion
+    concentration and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte transference number
+    """
+    coeffs = np.array(
+        [
+            -1.28e1,
+            -6.12,
+            8.21e-2,
+            9.04e-1,
+            3.18e-2,
+            -1.27e-4,
+            1.75e-2,
+            -3.12e-3,
+            -3.96e-5,
+        ]
+    )
+
+    return electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv
new file mode 100644
index 0000000000..4f3ae13761
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,default,
+Cation transference number,[function]electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019,
+1 + dlnf/dlnc,[function]electrolyte_TDF_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019," "
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019," "
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md
new file mode 100644
index 0000000000..5fb2efc1ce
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md
@@ -0,0 +1,5 @@
+# LiPF6 in EMC:FEC (19:1 w:w) electrolyte parameters
+
+Parameters for a LiPF6 in EMC:FEC (19:1 w:w) electrolyte, from the paper
+
+> Johannes Landesfeind and Hubert A. Gasteiger, ["Temperature and Concentration Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes."](https://iopscience.iop.org/article/10.1149/2.0571912jes) Journal of the Electrochemical Society 166 (2019): A3079.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py
new file mode 100644
index 0000000000..1eebc7f4e2
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import electrolyte_TDF_base_Landesfeind2019
+import numpy as np
+
+
+def electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019(c_e, T):
+    """
+    Thermodyamic factor (TDF) of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion
+    concentration and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte thermodynamic factor
+    """
+    coeffs = np.array(
+        [3.22, -1.01e1, -1.58e-2, 6.12, 2.96e-2, 2.42e-5, -2.22e-1, -1.57e-2, 6.30e-6]
+    )
+
+    return electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
new file mode 100644
index 0000000000..70946112b9
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
@@ -0,0 +1,155 @@
+from pybamm import exp, sqrt
+
+
+def electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Conductivity of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6 = coeffs
+    A = p1 * (1 + (T - p2))
+    B = 1 + p3 * sqrt(c) + p4 * (1 + p5 * exp(1000 / T)) * c
+    C = 1 + c ** 4 * (p6 * exp(1000 / T))
+    sigma_e = A * c * B / C  # mS.cm-1
+
+    return sigma_e / 10
+
+
+def electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Diffusivity of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4 = coeffs
+    A = p1 * exp(p2 * c)
+    B = exp(p3 / T)
+    C = exp(p4 * c / T)
+    D_e = A * B * C * 1e-10  # m2/s
+
+    return D_e
+
+
+def electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Thermodynamic factor (TDF) of LiPF6 in solvent_X as a function of ion concentration
+    and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte thermodynamic factor
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
+    tdf = (
+        p1
+        + p2 * c
+        + p3 * T
+        + p4 * c ** 2
+        + p5 * c * T
+        + p6 * T ** 2
+        + p7 * c ** 3
+        + p8 * c ** 2 * T
+        + p9 * c * T ** 2
+    )
+
+    return tdf
+
+
+def electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs):
+    """
+    Transference number of LiPF6 in solvent_X as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+    coeffs: :class:`pybamm.Symbol`
+        Fitting parameter coefficients
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte transference number
+    """
+    c = c_e / 1000  # mol.m-3 -> mol.l
+    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
+    tplus = (
+        p1
+        + p2 * c
+        + p3 * T
+        + p4 * c ** 2
+        + p5 * c * T
+        + p6 * T ** 2
+        + p7 * c ** 3
+        + p8 * c ** 2 * T
+        + p9 * c * T ** 2
+    )
+
+    return tplus
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py
new file mode 100644
index 0000000000..8a533ed14b
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_conductivity_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019(c_e, T):
+    """
+    Conductivity of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte conductivity
+    """
+    coeffs = np.array([2.51e-2, 1.75e2, 1.23, 2.05e-1, -8.81e-2, 2.83e-3])
+
+    return electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py
new file mode 100644
index 0000000000..98f175d222
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py
@@ -0,0 +1,32 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_diffusivity_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019(c_e, T):
+    """
+    Diffusivity of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion concentration and
+    temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte diffusivity
+    """
+    coeffs = np.array([5.86e2, 1.33, -1.38e3, -5.82e2])
+
+    return electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py
new file mode 100644
index 0000000000..3821293af4
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py
@@ -0,0 +1,34 @@
+from electrolyte_base_Landesfeind2019 import (
+    electrolyte_transference_number_base_Landesfeind2019,
+)
+import numpy as np
+
+
+def electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019(c_e, T):
+    """
+    Transference number of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion
+    concentration and temperature. The data comes from [1].
+
+    References
+    ----------
+    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
+    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
+    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Electrolyte transference number
+    """
+    coeffs = np.array(
+        [-1.22e1, -3.05, 8.38e-2, 1.78, 1.51e-3, -1.37e-4, -2.45e-2, -5.15e-3, 2.14e-5]
+    )
+
+    return electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv
new file mode 100644
index 0000000000..2239aebfb4
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,default,
+Cation transference number,[function]electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019,
+1 + dlnf/dlnc,[function]electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019," "
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019," "
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md
new file mode 100644
index 0000000000..a7524227f2
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md
@@ -0,0 +1,12 @@
+# LiPF6 electrolyte parameters
+
+Parameters for a LiPF6 electrolyte, from the papers
+
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery i. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
+
+>Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+
+The fits to data for the electrode and electrolyte properties are those provided
+by Dr. Simon O’Kane in the paper:
+
+> Richardson, Giles, et. al. "Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation." Electrochemica Acta 339 (2020): 135862
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py
new file mode 100644
index 0000000000..a2ed7745fc
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py
@@ -0,0 +1,44 @@
+from pybamm import exp, constants
+
+
+def electrolyte_conductivity_Ecker2015(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3].
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    # mol/m^3 to mol/l
+    cm = 1e-3 * c_e
+
+    # value at T = 296K
+    sigma_e_296 = 0.2667 * cm ** 3 - 1.2983 * cm ** 2 + 1.7919 * cm + 0.1726
+
+    # add temperature dependence
+    E_k_e = 1.71e4
+    C = 296 * exp(E_k_e / (constants.R * 296))
+    sigma_e = C * sigma_e_296 * exp(-E_k_e / (constants.R * T)) / T
+
+    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py
new file mode 100644
index 0000000000..b933363165
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py
@@ -0,0 +1,40 @@
+import pybamm
+from pybamm import constants
+
+
+def electrolyte_diffusivity_Ecker2015(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3].
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    # The diffusivity epends on the electrolyte conductivity
+    inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
+    sigma_e = pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs)
+
+    D_c_e = (constants.k_b / (constants.F * constants.q_e)) * sigma_e * T / c_e
+
+    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv
new file mode 100644
index 0000000000..7ef0733506
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,,
+Cation transference number,0.26,,
+1 + dlnf/dlnc,1,,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Ecker2015,,
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Ecker2015,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md
new file mode 100644
index 0000000000..a81234481f
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md
@@ -0,0 +1,9 @@
+# LiPF6 electrolyte parameters
+
+Parameters for a LiPF6 electrolyte, from the paper
+
+> Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512. DOI: 10.1149/2.0122001JES.
+
+> Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016). A new method to model the thickness change of a commercial pouch cell during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575.
+
+and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py
new file mode 100644
index 0000000000..51ae614ec1
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py
@@ -0,0 +1,34 @@
+from pybamm import Parameter
+
+
+def dlnf_dlnc_Ai2020(c_e, T, T_ref=298.3, t_plus=0.38):
+    """
+    Activity dependence of LiPF6 in EC:DMC as a function of ion concentration.
+
+    References
+    ----------
+    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
+    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity
+    in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society,
+    167(1), 013512. DOI: 10.1149/2.0122001JES.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration, mol/m^3
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature, K
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        1 + dlnf/dlnc
+    """
+    T_ref = Parameter("Reference temperature [K]")
+    t_plus = Parameter("Cation transference number")
+    dlnf_dlnc = (
+        0.601
+        - 0.24 * (c_e / 1000) ** 0.5
+        + 0.982 * (1 - 0.0052 * (T - T_ref)) * (c_e / 1000) ** 1.5
+    ) / (1 - t_plus)
+    return dlnf_dlnc
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py
new file mode 100644
index 0000000000..a6d0bf07dc
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py
@@ -0,0 +1,40 @@
+def electrolyte_conductivity_Ai2020(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC as a function of ion concentration.
+    Concentration should be in dm3 in the function.
+
+    References
+    ----------
+    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
+    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity
+    in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society,
+    167(1), 013512. DOI: 10.1149/2.0122001JES.
+    .. [2] Torchio, Marcello, et al. "Lionsimba: a matlab framework based
+    on a finite volume model suitable for li-ion battery design, simulation,
+    and control." Journal of The Electrochemical Society 163.7 (2016): A1192.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    sigma_e = (
+        1e-4
+        * c_e
+        * (
+            (-10.5 + 0.668 * 1e-3 * c_e + 0.494 * 1e-6 * c_e ** 2)
+            + (0.074 - 1.78 * 1e-5 * c_e - 8.86 * 1e-10 * c_e ** 2) * T
+            + (-6.96 * 1e-5 + 2.8 * 1e-8 * c_e) * T ** 2
+        )
+        ** 2
+    )
+
+    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py
new file mode 100644
index 0000000000..3201839779
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py
@@ -0,0 +1,28 @@
+def electrolyte_diffusivity_Ai2020(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration.
+
+    References
+    ----------
+    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
+    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity
+    in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society,
+    167(1), 013512. DOI: 10.1149/2.0122001JES.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration, mol/m^3
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature, K
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = 10 ** (-8.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
+
+    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv
new file mode 100644
index 0000000000..5d18c26c9a
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,Ai 2020,
+Cation transference number,0.38,Ai 2020,
+1 + dlnf/dlnc,[function]dlnf_dlnc_Ai2020,,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Ai2020,, 
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Ai2020,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md
new file mode 100644
index 0000000000..d7db16c804
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md
@@ -0,0 +1,7 @@
+# LiPF6 electrolyte parameters
+
+Parameters for a LiPF6 electrolyte, from the paper
+
+> Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A. (2011). Multi-domain modeling of lithium-ion batteries encompassing multi-physics in varied length scales. Journal of The Electrochemical Society, 158(8), A955-A969.
+
+and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py
new file mode 100644
index 0000000000..bbec2015bb
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py
@@ -0,0 +1,35 @@
+from pybamm import exp
+
+
+def electrolyte_conductivity_Kim2011(c_e, T):
+    """
+    Conductivity of LiPF6 in EC as a function of ion concentration from [1].
+
+    References
+    ----------
+    .. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
+    (2011). Multi-domain modeling of lithium-ion batteries encompassing
+    multi-physics in varied length scales. Journal of The Electrochemical
+    Society, 158(8), A955-A969.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    sigma_e = (
+        3.45 * exp(-798 / T) * (c_e / 1000) ** 3
+        - 48.5 * exp(-1080 / T) * (c_e / 1000) ** 2
+        + 244 * exp(-1440 / T) * (c_e / 1000)
+    )
+
+    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py
new file mode 100644
index 0000000000..2e1f586ac9
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py
@@ -0,0 +1,35 @@
+from pybamm import exp
+
+
+def electrolyte_diffusivity_Kim2011(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC as a function of ion concentration from [1].
+
+     References
+    ----------
+    .. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
+    (2011). Multi-domain modeling of lithium-ion batteries encompassing
+    multi-physics in varied length scales. Journal of The Electrochemical
+    Society, 158(8), A955-A969.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = (
+        5.84 * 10 ** (-7) * exp(-2870 / T) * (c_e / 1000) ** 2
+        - 33.9 * 10 ** (-7) * exp(-2920 / T) * (c_e / 1000)
+        + 129 * 10 ** (-7) * exp(-3200 / T)
+    )
+
+    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv
new file mode 100644
index 0000000000..c0a0b0d75b
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1200,,
+Cation transference number,0.4,Reported as a function in Kim2011 (Implement later),
+1 + dlnf/dlnc,1,,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Kim2011,,
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Kim2011,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md
new file mode 100644
index 0000000000..07936c3d92
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md
@@ -0,0 +1,7 @@
+# LiPF6 electrolyte parameters
+
+Parameters for a LiPF6 electrolyte, from the paper
+
+> Marquis, S. G., Sulzer, V.,  Timms, R.,  Please, C. P.,  &  Chapman, S. J. (2019). “An  asymptotic derivation of a single particle model with electrolyte”. [Journal of The Electrochemical Society](https://doi.org/10.1149/2.0341915jes), 166(15), A3693-A3706. 
+
+and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py
new file mode 100644
index 0000000000..5e080d3f33
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py
@@ -0,0 +1,40 @@
+from pybamm import exp, constants
+
+
+def electrolyte_conductivity_Capiglia1999(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC as a function of ion concentration. The original
+    data is from [1]. The fit is from Dualfoil [2].
+
+    References
+    ----------
+    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
+    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
+    Journal of power sources 81 (1999): 859-862.
+    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    sigma_e = (
+        0.0911
+        + 1.9101 * (c_e / 1000)
+        - 1.052 * (c_e / 1000) ** 2
+        + 0.1554 * (c_e / 1000) ** 3
+    )
+
+    E_k_e = 34700
+    arrhenius = exp(E_k_e / constants.R * (1 / 298.15 - 1 / T))
+
+    return sigma_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py
new file mode 100644
index 0000000000..a0308fb81b
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py
@@ -0,0 +1,34 @@
+from pybamm import exp, constants
+
+
+def electrolyte_diffusivity_Capiglia1999(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration. The original data
+    is from [1]. The fit from Dualfoil [2].
+
+    References
+    ----------
+    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
+    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
+    Journal of power sources 81 (1999): 859-862.
+    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = 5.34e-10 * exp(-0.65 * c_e / 1000)
+    E_D_e = 37040
+    arrhenius = exp(E_D_e / constants.R * (1 / 298.15 - 1 / T))
+
+    return D_c_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv
new file mode 100644
index 0000000000..ed7e67cff3
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,Scott Moura FastDFN,
+Cation transference number,0.4,Scott Moura FastDFN,
+1 + dlnf/dlnc,1,,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Capiglia1999,, 
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Capiglia1999,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md
new file mode 100644
index 0000000000..4c8b672be2
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md
@@ -0,0 +1,7 @@
+# LiPF6 electrolyte parameters
+
+Parameters for a LiPF6 electrolyte, from the paper
+
+> A. Nyman, M. Behm, and G. Lindbergh, ["Electrochemical characterisation and modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"](https://www.sciencedirect.com/science/article/pii/S0013468608005045) Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+
+and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py
new file mode 100644
index 0000000000..f8885591f4
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py
@@ -0,0 +1,29 @@
+def electrolyte_conductivity_Nyman2008(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
+    comes from [1].
+    References
+    ----------
+    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
+    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
+    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    sigma_e = (
+        0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000)
+    )
+
+    # Nyman et al. (2008) does not provide temperature dependence
+
+    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py
new file mode 100644
index 0000000000..2a832539d6
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py
@@ -0,0 +1,27 @@
+def electrolyte_diffusivity_Nyman2008(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
+    comes from [1]
+    References
+    ----------
+    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
+    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
+    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10
+
+    # Nyman et al. (2008) does not provide temperature dependence
+
+    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv
new file mode 100644
index 0000000000..8354b0b9c7
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,Chen 2020,
+Cation transference number,0.2594,Chen 2020,
+1 + dlnf/dlnc,1,,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Nyman2008,Nyman 2008, 
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Nyman2008,Nyman 2008, 
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md
new file mode 100644
index 0000000000..b7cef62955
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md
@@ -0,0 +1,8 @@
+# LiPF6 electrolyte parameters
+
+Parameters for a LiPF6 electrolyte, from the paper
+
+> Marquis, S. G., Sulzer, V.,  Timms, R.,  Please, C. P.,  &  Chapman, S. J. (2019). “An  asymptotic derivation of a single particle model with electrolyte”. [Journal of The Electrochemical Society](https://doi.org/10.1149/2.0341915jes), 166(15), A3693-A3706. 
+> P. Ramadass, Bala Haran, Parthasarathy M. Gomadam, Ralph White, and Branko N. Popov. "Development of First Principles Capacity Fade Model for Li-Ion Cells." (2004)
+
+and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py
new file mode 100644
index 0000000000..a3ffb2fa60
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py
@@ -0,0 +1,43 @@
+from pybamm import exp, constants
+
+
+def electrolyte_conductivity_Ramadass2004(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC as a function of ion concentration.
+    Concentration should be in dm3 in the function.
+
+    References
+    ----------
+    .. [1] P. Ramadass, Bala Haran, Parthasarathy M. Gomadam, Ralph White, and Branko
+    N. Popov. "Development of First Principles Capacity Fade Model for Li-Ion Cells."
+    (2004)
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+    # mol.m-3 to mol.dm-3, original function is likely in mS/cm
+    # The function is not in Arora 2000 as reported in Ramadass 2004
+
+    cm = 1e-6 * c_e  # here it should be only 1e-3
+
+    sigma_e = (
+        4.1253 * (10 ** (-4))
+        + 5.007 * cm
+        - 4.7212 * (10 ** 3) * (cm ** 2)
+        + 1.5094 * (10 ** 6) * (cm ** 3)
+        - 1.6018 * (10 ** 8) * (cm ** 4)
+    ) * 1e3  # and here there should not be an exponent
+
+    E_k_e = 34700
+    arrhenius = exp(E_k_e / constants.R * (1 / 298.15 - 1 / T))
+
+    return sigma_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py
new file mode 100644
index 0000000000..22134a70b7
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py
@@ -0,0 +1,32 @@
+from pybamm import exp, constants
+
+
+def electrolyte_diffusivity_Ramadass2004(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration.
+
+    References
+    ----------
+    .. [1] P. Ramadass, Bala Haran, Parthasarathy M. Gomadam, Ralph White, and Branko
+    N. Popov. "Development of First Principles Capacity Fade Model for Li-Ion Cells."
+    (2004)
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = 7.5e-10
+    E_D_e = 37040
+    arrhenius = exp(E_D_e / constants.R * (1 / 298.15 - 1 / T))
+
+    return D_c_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv
new file mode 100644
index 0000000000..c10a6d80d8
--- /dev/null
+++ b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv
@@ -0,0 +1,9 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+# Electrolyte properties,,,
+Typical electrolyte concentration [mol.m-3],1000,Ramadass,
+Cation transference number,0.363,Ramadass,
+1 + dlnf/dlnc,1,,
+Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Ramadass2004,, 
+Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Ramadass2004,,
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
index 7b57d24ce8..2174b08160 100644
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/parameters.csv
@@ -4,9 +4,7 @@ Name [units],Value,Reference,Notes
 # Electrode properties,,,
 Negative electrode conductivity [S.m-1],14,,
 Maximum concentration in negative electrode [mol.m-3],31920,,
-Measured negative electrode diffusivity [m2.s-1],[data]measured_graphite_diffusivity_Ecker2015,,
 Negative electrode diffusivity [m2.s-1],[function]graphite_diffusivity_Ecker2015,,
-Measured negative electrode OCP [V],[data]graphite_ocp_Ecker2015,,
 Negative electrode OCP [V],[function]graphite_ocp_Ecker2015_function,,
 ,,,
 # Microstructure,,,
diff --git a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv
index 0ed194bc51..94a185fb70 100644
--- a/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/positive_electrodes/nmc_Yang2017/parameters.csv
@@ -4,9 +4,7 @@ Name [units],Value,Reference,Notes
 # Electrode properties,,,
 Positive electrode conductivity [S.m-1],68.1,,
 Maximum concentration in positive electrode [mol.m-3],48580,,
-Measured positive electrode diffusivity [m2.s-1],[data]measured_nco_diffusivity_Ecker2015,,
 Positive electrode diffusivity [m2.s-1],[function]nco_diffusivity_Ecker2015,,
-Measured positive electrode OCP [V],[data]nco_ocp_Ecker2015,,
 Positive electrode OCP [V],[function]nco_ocp_Ecker2015_function,,
 ,,,
 # Microstructure,,,
@@ -17,6 +15,7 @@ Positive electrode Bruggeman coefficient (electrolyte),1.544226719,Solve for per
 Positive electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to solid conductivity,
 Positive electrode exchange-current density [A.m-2],[function]nco_electrolyte_exchange_current_density_Ecker2015,,
 ,,,
+,,,
 # Interfacial reactions,,,
 Positive electrode cation signed stoichiometry,-1,,
 Positive electrode electrons in reaction,1,,
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py
index 8dfe6a98ce..11640258ae 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py
@@ -10,6 +10,14 @@ def test_well_posed(self):
         model = pybamm.lithium_ion.Yang2017()
         model.check_well_posedness()
 
+    def test_default_parameter_values(self):
+        model = pybamm.lithium_ion.Yang2017()
+        chemistry = pybamm.parameter_sets.Yang2017
+        parameter_values = pybamm.ParameterValues(chemistry=chemistry)
+        self.assertDictEqual(
+            parameter_values._dict_items, model.default_parameter_values._dict_items
+        )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py b/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py
index 2030cbb851..e82ce18cf8 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Yang2017.py
@@ -43,7 +43,7 @@ def test_standard_lithium_parameters(self):
         chemistry = pybamm.parameter_sets.Yang2017
         parameter_values = pybamm.ParameterValues(chemistry=chemistry)
 
-        model = pybamm.lithium_ion.DFN()
+        model = pybamm.lithium_ion.Yang2017()
         sim = pybamm.Simulation(model, parameter_values=parameter_values)
         sim.set_parameters()
         sim.build()

From a331badef0a4f4376e069424a0d8d68eee7e2e4e Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Thu, 18 Mar 2021 18:02:58 +0000
Subject: [PATCH 32/46] Update README.md

---
 .../lithium-ion/electrolytes/lipf6_Ecker2015/README.md          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/input/parameters/lithium-ion/electrolytes/lipf6_Ecker2015/README.md b/pybamm/input/parameters/lithium-ion/electrolytes/lipf6_Ecker2015/README.md
index a7524227f2..323ef23cda 100644
--- a/pybamm/input/parameters/lithium-ion/electrolytes/lipf6_Ecker2015/README.md
+++ b/pybamm/input/parameters/lithium-ion/electrolytes/lipf6_Ecker2015/README.md
@@ -4,7 +4,7 @@ Parameters for a LiPF6 electrolyte, from the papers
 
 > Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery i. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
 
->Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
 
 The fits to data for the electrode and electrolyte properties are those provided
 by Dr. Simon O’Kane in the paper:

From 1f27f3b44dda26d2e4f8eaf46f4013072747efac Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 19 Mar 2021 09:50:13 +0000
Subject: [PATCH 33/46] Changes for tests

---
 .../electrolyte_conductivity_PeymanMPM.py     |  33 ----
 .../electrolyte_diffusivity_PeymanMPM.py      |  34 ----
 .../LiPF6_Mohtat2020/parameters.csv           |  10 --
 .../README.md                                 |   5 -
 ...ctrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py |  32 ----
 .../electrolyte_base_Landesfeind2019.py       | 155 ------------------
 ...conductivity_EC_DMC_1_1_Landesfeind2019.py |  32 ----
 ..._diffusivity_EC_DMC_1_1_Landesfeind2019.py |  32 ----
 ...rence_number_EC_DMC_1_1_Landesfeind2019.py |  44 -----
 .../parameters.csv                            |   9 -
 .../README.md                                 |   5 -
 ...ctrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py |  32 ----
 .../electrolyte_base_Landesfeind2019.py       | 155 ------------------
 ...conductivity_EC_EMC_3_7_Landesfeind2019.py |  32 ----
 ..._diffusivity_EC_EMC_3_7_Landesfeind2019.py |  32 ----
 ...rence_number_EC_EMC_3_7_Landesfeind2019.py |  44 -----
 .../parameters.csv                            |   9 -
 .../README.md                                 |   5 -
 ...rolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py |  32 ----
 .../electrolyte_base_Landesfeind2019.py       | 155 ------------------
 ...nductivity_EMC_FEC_19_1_Landesfeind2019.py |  32 ----
 ...iffusivity_EMC_FEC_19_1_Landesfeind2019.py |  32 ----
 ...nce_number_EMC_FEC_19_1_Landesfeind2019.py |  34 ----
 .../parameters.csv                            |   9 -
 .../electrolytes/lipf6_Ecker2015/README.md    |  12 --
 .../electrolyte_conductivity_Ecker2015.py     |  44 -----
 .../electrolyte_diffusivity_Ecker2015.py      |  40 -----
 .../lipf6_Ecker2015/parameters.csv            |   9 -
 .../lipf6_Enertech_Ai2020/README.md           |   9 -
 .../lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py |  34 ----
 .../electrolyte_conductivity_Ai2020.py        |  40 -----
 .../electrolyte_diffusivity_Ai2020.py         |  28 ----
 .../lipf6_Enertech_Ai2020/parameters.csv      |   9 -
 .../electrolytes/lipf6_Kim2011/README.md      |   7 -
 .../electrolyte_conductivity_Kim2011.py       |  35 ----
 .../electrolyte_diffusivity_Kim2011.py        |  35 ----
 .../electrolytes/lipf6_Kim2011/parameters.csv |   9 -
 .../electrolytes/lipf6_Marquis2019/README.md  |   7 -
 .../electrolyte_conductivity_Capiglia1999.py  |  40 -----
 .../electrolyte_diffusivity_Capiglia1999.py   |  34 ----
 .../lipf6_Marquis2019/parameters.csv          |   9 -
 .../electrolytes/lipf6_Nyman2008/README.md    |   7 -
 .../electrolyte_conductivity_Nyman2008.py     |  29 ----
 .../electrolyte_diffusivity_Nyman2008.py      |  27 ---
 .../lipf6_Nyman2008/parameters.csv            |   9 -
 .../electrolytes/lipf6_Ramadass2004/README.md |   8 -
 .../electrolyte_conductivity_Ramadass2004.py  |  43 -----
 .../electrolyte_diffusivity_Ramadass2004.py   |  32 ----
 .../lipf6_Ramadass2004/parameters.csv         |   9 -
 ...ting_exchange_current_density_OKane2020.py |  30 ----
 ...ting_exchange_current_density_OKane2020.py |  29 ----
 .../full_battery_models/base_battery_model.py |   6 -
 .../lithium_ion/basic_dfn_half_cell.py        |   3 +-
 53 files changed, 1 insertion(+), 1626 deletions(-)
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py
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 delete mode 100644 pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv
 delete mode 100644 pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
 delete mode 100644 pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py

diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py
deleted file mode 100644
index 229d22cea2..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_conductivity_PeymanMPM.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_conductivity_PeymanMPM(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:DMC as a function of ion concentration. The original
-    data is from [1]. The fit is from Dualfoil [2].
-
-    References
-    ----------
-    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
-    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
-    Journal of power sources 81 (1999): 859-862.
-    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-
-    sigma_e = 1.3
-    E_k_e = 34700
-    arrhenius = exp(E_k_e / constants.R * (1 / 298.15 - 1 / T))
-
-    return sigma_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py
deleted file mode 100644
index 3014398635..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/electrolyte_diffusivity_PeymanMPM.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_diffusivity_PeymanMPM(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration. The original data
-    is from [1]. The fit from Dualfoil [2].
-
-    References
-    ----------
-    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
-    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
-    Journal of power sources 81 (1999): 859-862.
-    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-
-    D_c_e = 5.35 * 10 ** (-10)
-    E_D_e = 37040
-    arrhenius = exp(E_D_e / constants.R * (1 / 298.15 - 1 / T))
-
-    return D_c_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv
deleted file mode 100644
index 528ec51dab..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/LiPF6_Mohtat2020/parameters.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,Peyman MPM,from MPM code
-Cation transference number,0.38,Peyman MPM,
-1 + dlnf/dlnc,1,,
-Typical lithium ion diffusivity [m2.s-1],5.34E-10,Scott Moura FastDFN,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_PeymanMPM,, 
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_PeymanMPM,, 
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md
deleted file mode 100644
index 07aa16c3ab..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# LiPF6 in EC:DMC (1:1 w:w) electrolyte parameters
-
-Parameters for a LiPF6 in EC:DMC (1:1 w:w) electrolyte, from the paper
-
-> Johannes Landesfeind and Hubert A. Gasteiger, ["Temperature and Concentration Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes."](https://iopscience.iop.org/article/10.1149/2.0571912jes) Journal of the Electrochemical Society 166 (2019): A3079.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py
deleted file mode 100644
index c57bfb0013..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_TDF_EC_DMC_1_1_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import electrolyte_TDF_base_Landesfeind2019
-import numpy as np
-
-
-def electrolyte_TDF_EC_DMC_1_1_Landesfeind2019(c_e, T):
-    """
-    Thermodynamic factor (TDF) of LiPF6 in EC:DMC (1:1 w:w) as a function of ion
-    concentration and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte thermodynamic factor
-    """
-    coeffs = np.array(
-        [-5.58, 7.17, 3.80e-2, 1.91, -6.65e-2, -5.08e-5, 1.1e-1, -6.10e-3, 1.51e-4]
-    )
-
-    return electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
deleted file mode 100644
index 70946112b9..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from pybamm import exp, sqrt
-
-
-def electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Conductivity of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6 = coeffs
-    A = p1 * (1 + (T - p2))
-    B = 1 + p3 * sqrt(c) + p4 * (1 + p5 * exp(1000 / T)) * c
-    C = 1 + c ** 4 * (p6 * exp(1000 / T))
-    sigma_e = A * c * B / C  # mS.cm-1
-
-    return sigma_e / 10
-
-
-def electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Diffusivity of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4 = coeffs
-    A = p1 * exp(p2 * c)
-    B = exp(p3 / T)
-    C = exp(p4 * c / T)
-    D_e = A * B * C * 1e-10  # m2/s
-
-    return D_e
-
-
-def electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Thermodynamic factor (TDF) of LiPF6 in solvent_X as a function of ion concentration
-    and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte thermodynamic factor
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
-    tdf = (
-        p1
-        + p2 * c
-        + p3 * T
-        + p4 * c ** 2
-        + p5 * c * T
-        + p6 * T ** 2
-        + p7 * c ** 3
-        + p8 * c ** 2 * T
-        + p9 * c * T ** 2
-    )
-
-    return tdf
-
-
-def electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Transference number of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte transference number
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
-    tplus = (
-        p1
-        + p2 * c
-        + p3 * T
-        + p4 * c ** 2
-        + p5 * c * T
-        + p6 * T ** 2
-        + p7 * c ** 3
-        + p8 * c ** 2 * T
-        + p9 * c * T ** 2
-    )
-
-    return tplus
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py
deleted file mode 100644
index 52a7be3034..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_conductivity_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:DMC (1:1 w:w) as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-    coeffs = np.array([7.98e-1, 2.28e2, -1.22, 5.09e-1, -4e-3, 3.79e-3])
-
-    return electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py
deleted file mode 100644
index 4e2680c0ee..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_diffusivity_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:DMC (1:1 w:w) as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-    coeffs = np.array([1.47e3, 1.33, -1.69e3, -5.63e2])
-
-    return electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py
deleted file mode 100644
index 4be6c301ef..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_transference_number_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019(c_e, T):
-    """
-    Transference number of LiPF6 in EC:DMC (1:1 w:w) as a function of ion
-    concentration and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte transference number
-    """
-    coeffs = np.array(
-        [
-            -7.91,
-            2.45e-1,
-            5.28e-2,
-            6.98e-1,
-            -1.08e-2,
-            -8.21e-5,
-            7.43e-4,
-            -2.22e-3,
-            3.07e-5,
-        ]
-    )
-
-    return electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv
deleted file mode 100644
index 46f3fc5232..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_DMC_1_1_Landesfeind2019/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,default,
-Cation transference number,[function]electrolyte_transference_number_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019,
-1 + dlnf/dlnc,[function]electrolyte_TDF_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019," "
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_EC_DMC_1_1_Landesfeind2019,Landesfeind 2019," "
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md
deleted file mode 100644
index 2406cfaf76..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# LiPF6 in EC:EMC (3:7 w:w) electrolyte parameters
-
-Parameters for a LiPF6 in EC:EMC (3:7 w:w) electrolyte, from the paper
-
-> Johannes Landesfeind and Hubert A. Gasteiger, ["Temperature and Concentration Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes."](https://iopscience.iop.org/article/10.1149/2.0571912jes) Journal of the Electrochemical Society 166 (2019): A3079.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py
deleted file mode 100644
index 1feec60ba1..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_TDF_EC_EMC_3_7_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import electrolyte_TDF_base_Landesfeind2019
-import numpy as np
-
-
-def electrolyte_TDF_EC_EMC_3_7_Landesfeind2019(c_e, T):
-    """
-    Thermodynamic factor (TDF) of LiPF6 in EC:EMC (3:7 w:w) as a function of ion
-    concentration and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte thermodynamic factor
-    """
-    coeffs = np.array(
-        [2.57e1, -4.51e1, -1.77e-1, 1.94, 2.95e-1, 3.08e-4, 2.59e-1, -9.46e-3, -4.54e-4]
-    )
-
-    return electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py
deleted file mode 100644
index 70946112b9..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_base_Landesfeind2019.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from pybamm import exp, sqrt
-
-
-def electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Conductivity of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6 = coeffs
-    A = p1 * (1 + (T - p2))
-    B = 1 + p3 * sqrt(c) + p4 * (1 + p5 * exp(1000 / T)) * c
-    C = 1 + c ** 4 * (p6 * exp(1000 / T))
-    sigma_e = A * c * B / C  # mS.cm-1
-
-    return sigma_e / 10
-
-
-def electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Diffusivity of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4 = coeffs
-    A = p1 * exp(p2 * c)
-    B = exp(p3 / T)
-    C = exp(p4 * c / T)
-    D_e = A * B * C * 1e-10  # m2/s
-
-    return D_e
-
-
-def electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Thermodynamic factor (TDF) of LiPF6 in solvent_X as a function of ion concentration
-    and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte thermodynamic factor
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
-    tdf = (
-        p1
-        + p2 * c
-        + p3 * T
-        + p4 * c ** 2
-        + p5 * c * T
-        + p6 * T ** 2
-        + p7 * c ** 3
-        + p8 * c ** 2 * T
-        + p9 * c * T ** 2
-    )
-
-    return tdf
-
-
-def electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Transference number of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte transference number
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
-    tplus = (
-        p1
-        + p2 * c
-        + p3 * T
-        + p4 * c ** 2
-        + p5 * c * T
-        + p6 * T ** 2
-        + p7 * c ** 3
-        + p8 * c ** 2 * T
-        + p9 * c * T ** 2
-    )
-
-    return tplus
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py
deleted file mode 100644
index b0581eba78..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_conductivity_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:EMC (3:7 w:w) as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-    coeffs = np.array([5.21e-1, 2.28e2, -1.06, 3.53e-1, -3.59e-3, 1.48e-3])
-
-    return electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py
deleted file mode 100644
index b8fd6ea266..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_diffusivity_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:EMC (3:7 w:w) as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-    coeffs = np.array([1.01e3, 1.01, -1.56e3, -4.87e2])
-
-    return electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py
deleted file mode 100644
index b2333ab2a4..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_transference_number_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019(c_e, T):
-    """
-    Transference number of LiPF6 in EC:EMC (3:7 w:w) as a function of ion
-    concentration and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte transference number
-    """
-    coeffs = np.array(
-        [
-            -1.28e1,
-            -6.12,
-            8.21e-2,
-            9.04e-1,
-            3.18e-2,
-            -1.27e-4,
-            1.75e-2,
-            -3.12e-3,
-            -3.96e-5,
-        ]
-    )
-
-    return electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv
deleted file mode 100644
index 4f3ae13761..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EC_EMC_3_7_Landesfeind2019/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,default,
-Cation transference number,[function]electrolyte_transference_number_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019,
-1 + dlnf/dlnc,[function]electrolyte_TDF_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019," "
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_EC_EMC_3_7_Landesfeind2019,Landesfeind 2019," "
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md
deleted file mode 100644
index 5fb2efc1ce..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# LiPF6 in EMC:FEC (19:1 w:w) electrolyte parameters
-
-Parameters for a LiPF6 in EMC:FEC (19:1 w:w) electrolyte, from the paper
-
-> Johannes Landesfeind and Hubert A. Gasteiger, ["Temperature and Concentration Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes."](https://iopscience.iop.org/article/10.1149/2.0571912jes) Journal of the Electrochemical Society 166 (2019): A3079.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py
deleted file mode 100644
index 1eebc7f4e2..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import electrolyte_TDF_base_Landesfeind2019
-import numpy as np
-
-
-def electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019(c_e, T):
-    """
-    Thermodyamic factor (TDF) of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion
-    concentration and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte thermodynamic factor
-    """
-    coeffs = np.array(
-        [3.22, -1.01e1, -1.58e-2, 6.12, 2.96e-2, 2.42e-5, -2.22e-1, -1.57e-2, 6.30e-6]
-    )
-
-    return electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
deleted file mode 100644
index 70946112b9..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_base_Landesfeind2019.py
+++ /dev/null
@@ -1,155 +0,0 @@
-from pybamm import exp, sqrt
-
-
-def electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Conductivity of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6 = coeffs
-    A = p1 * (1 + (T - p2))
-    B = 1 + p3 * sqrt(c) + p4 * (1 + p5 * exp(1000 / T)) * c
-    C = 1 + c ** 4 * (p6 * exp(1000 / T))
-    sigma_e = A * c * B / C  # mS.cm-1
-
-    return sigma_e / 10
-
-
-def electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Diffusivity of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4 = coeffs
-    A = p1 * exp(p2 * c)
-    B = exp(p3 / T)
-    C = exp(p4 * c / T)
-    D_e = A * B * C * 1e-10  # m2/s
-
-    return D_e
-
-
-def electrolyte_TDF_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Thermodynamic factor (TDF) of LiPF6 in solvent_X as a function of ion concentration
-    and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte thermodynamic factor
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
-    tdf = (
-        p1
-        + p2 * c
-        + p3 * T
-        + p4 * c ** 2
-        + p5 * c * T
-        + p6 * T ** 2
-        + p7 * c ** 3
-        + p8 * c ** 2 * T
-        + p9 * c * T ** 2
-    )
-
-    return tdf
-
-
-def electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs):
-    """
-    Transference number of LiPF6 in solvent_X as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-    coeffs: :class:`pybamm.Symbol`
-        Fitting parameter coefficients
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte transference number
-    """
-    c = c_e / 1000  # mol.m-3 -> mol.l
-    p1, p2, p3, p4, p5, p6, p7, p8, p9 = coeffs
-    tplus = (
-        p1
-        + p2 * c
-        + p3 * T
-        + p4 * c ** 2
-        + p5 * c * T
-        + p6 * T ** 2
-        + p7 * c ** 3
-        + p8 * c ** 2 * T
-        + p9 * c * T ** 2
-    )
-
-    return tplus
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py
deleted file mode 100644
index 8a533ed14b..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_conductivity_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019(c_e, T):
-    """
-    Conductivity of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte conductivity
-    """
-    coeffs = np.array([2.51e-2, 1.75e2, 1.23, 2.05e-1, -8.81e-2, 2.83e-3])
-
-    return electrolyte_conductivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py
deleted file mode 100644
index 98f175d222..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_diffusivity_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019(c_e, T):
-    """
-    Diffusivity of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion concentration and
-    temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte diffusivity
-    """
-    coeffs = np.array([5.86e2, 1.33, -1.38e3, -5.82e2])
-
-    return electrolyte_diffusivity_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py
deleted file mode 100644
index 3821293af4..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from electrolyte_base_Landesfeind2019 import (
-    electrolyte_transference_number_base_Landesfeind2019,
-)
-import numpy as np
-
-
-def electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019(c_e, T):
-    """
-    Transference number of LiPF6 in EMC:FEC (19:1 w:w) as a function of ion
-    concentration and temperature. The data comes from [1].
-
-    References
-    ----------
-    .. [1] Landesfeind, J. and Gasteiger, H.A., 2019. Temperature and Concentration
-    Dependence of the Ionic Transport Properties of Lithium-Ion Battery Electrolytes.
-    Journal of The Electrochemical Society, 166(14), pp.A3079-A3097.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Electrolyte transference number
-    """
-    coeffs = np.array(
-        [-1.22e1, -3.05, 8.38e-2, 1.78, 1.51e-3, -1.37e-4, -2.45e-2, -5.15e-3, 2.14e-5]
-    )
-
-    return electrolyte_transference_number_base_Landesfeind2019(c_e, T, coeffs)
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv
deleted file mode 100644
index 2239aebfb4..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_EMC_FEC_19_1_Landesfeind2019/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,default,
-Cation transference number,[function]electrolyte_transference_number_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019,
-1 + dlnf/dlnc,[function]electrolyte_TDF_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019," "
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_EMC_FEC_19_1_Landesfeind2019,Landesfeind 2019," "
\ No newline at end of file
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md
deleted file mode 100644
index a7524227f2..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# LiPF6 electrolyte parameters
-
-Parameters for a LiPF6 electrolyte, from the papers
-
-> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery i. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
-
->Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
-
-The fits to data for the electrode and electrolyte properties are those provided
-by Dr. Simon O’Kane in the paper:
-
-> Richardson, Giles, et. al. "Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation." Electrochemica Acta 339 (2020): 135862
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py
deleted file mode 100644
index a2ed7745fc..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_conductivity_Ecker2015.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_conductivity_Ecker2015(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3].
-
-    References
-    ----------
-    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
-    a lithium-ion battery i. determination of parameters." Journal of the
-    Electrochemical Society 162.9 (2015): A1836-A1848.
-    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
-    a lithium-ion battery ii. model validation." Journal of The Electrochemical
-    Society 162.9 (2015): A1849-A1857.
-    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
-    high-rate operation of graded lithium-ion electrodes: Systematic derivation
-    and validation." Electrochemica Acta 339 (2020): 135862
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    # mol/m^3 to mol/l
-    cm = 1e-3 * c_e
-
-    # value at T = 296K
-    sigma_e_296 = 0.2667 * cm ** 3 - 1.2983 * cm ** 2 + 1.7919 * cm + 0.1726
-
-    # add temperature dependence
-    E_k_e = 1.71e4
-    C = 296 * exp(E_k_e / (constants.R * 296))
-    sigma_e = C * sigma_e_296 * exp(-E_k_e / (constants.R * T)) / T
-
-    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py
deleted file mode 100644
index b933363165..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/electrolyte_diffusivity_Ecker2015.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import pybamm
-from pybamm import constants
-
-
-def electrolyte_diffusivity_Ecker2015(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3].
-
-    References
-    ----------
-    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
-    a lithium-ion battery i. determination of parameters." Journal of the
-    Electrochemical Society 162.9 (2015): A1836-A1848.
-    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
-    a lithium-ion battery ii. model validation." Journal of The Electrochemical
-    Society 162.9 (2015): A1849-A1857.
-    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
-    high-rate operation of graded lithium-ion electrodes: Systematic derivation
-    and validation." Electrochemica Acta 339 (2020): 135862
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    # The diffusivity epends on the electrolyte conductivity
-    inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
-    sigma_e = pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs)
-
-    D_c_e = (constants.k_b / (constants.F * constants.q_e)) * sigma_e * T / c_e
-
-    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv
deleted file mode 100644
index 7ef0733506..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ecker2015/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,,
-Cation transference number,0.26,,
-1 + dlnf/dlnc,1,,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Ecker2015,,
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Ecker2015,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md
deleted file mode 100644
index a81234481f..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# LiPF6 electrolyte parameters
-
-Parameters for a LiPF6 electrolyte, from the paper
-
-> Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512. DOI: 10.1149/2.0122001JES.
-
-> Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016). A new method to model the thickness change of a commercial pouch cell during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575.
-
-and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py
deleted file mode 100644
index 51ae614ec1..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/dlnf_dlnc_Ai2020.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from pybamm import Parameter
-
-
-def dlnf_dlnc_Ai2020(c_e, T, T_ref=298.3, t_plus=0.38):
-    """
-    Activity dependence of LiPF6 in EC:DMC as a function of ion concentration.
-
-    References
-    ----------
-    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
-    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity
-    in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society,
-    167(1), 013512. DOI: 10.1149/2.0122001JES.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration, mol/m^3
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature, K
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        1 + dlnf/dlnc
-    """
-    T_ref = Parameter("Reference temperature [K]")
-    t_plus = Parameter("Cation transference number")
-    dlnf_dlnc = (
-        0.601
-        - 0.24 * (c_e / 1000) ** 0.5
-        + 0.982 * (1 - 0.0052 * (T - T_ref)) * (c_e / 1000) ** 1.5
-    ) / (1 - t_plus)
-    return dlnf_dlnc
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py
deleted file mode 100644
index a6d0bf07dc..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_conductivity_Ai2020.py
+++ /dev/null
@@ -1,40 +0,0 @@
-def electrolyte_conductivity_Ai2020(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:DMC as a function of ion concentration.
-    Concentration should be in dm3 in the function.
-
-    References
-    ----------
-    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
-    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity
-    in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society,
-    167(1), 013512. DOI: 10.1149/2.0122001JES.
-    .. [2] Torchio, Marcello, et al. "Lionsimba: a matlab framework based
-    on a finite volume model suitable for li-ion battery design, simulation,
-    and control." Journal of The Electrochemical Society 163.7 (2016): A1192.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    sigma_e = (
-        1e-4
-        * c_e
-        * (
-            (-10.5 + 0.668 * 1e-3 * c_e + 0.494 * 1e-6 * c_e ** 2)
-            + (0.074 - 1.78 * 1e-5 * c_e - 8.86 * 1e-10 * c_e ** 2) * T
-            + (-6.96 * 1e-5 + 2.8 * 1e-8 * c_e) * T ** 2
-        )
-        ** 2
-    )
-
-    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py
deleted file mode 100644
index 3201839779..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/electrolyte_diffusivity_Ai2020.py
+++ /dev/null
@@ -1,28 +0,0 @@
-def electrolyte_diffusivity_Ai2020(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration.
-
-    References
-    ----------
-    .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
-    Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity
-    in Lithium-Ion Pouch Cells. Journal of The Electrochemical Society,
-    167(1), 013512. DOI: 10.1149/2.0122001JES.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration, mol/m^3
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature, K
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    D_c_e = 10 ** (-8.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
-
-    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv
deleted file mode 100644
index 5d18c26c9a..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Enertech_Ai2020/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,Ai 2020,
-Cation transference number,0.38,Ai 2020,
-1 + dlnf/dlnc,[function]dlnf_dlnc_Ai2020,,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Ai2020,, 
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Ai2020,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md
deleted file mode 100644
index d7db16c804..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# LiPF6 electrolyte parameters
-
-Parameters for a LiPF6 electrolyte, from the paper
-
-> Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A. (2011). Multi-domain modeling of lithium-ion batteries encompassing multi-physics in varied length scales. Journal of The Electrochemical Society, 158(8), A955-A969.
-
-and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py
deleted file mode 100644
index bbec2015bb..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_conductivity_Kim2011.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pybamm import exp
-
-
-def electrolyte_conductivity_Kim2011(c_e, T):
-    """
-    Conductivity of LiPF6 in EC as a function of ion concentration from [1].
-
-    References
-    ----------
-    .. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
-    (2011). Multi-domain modeling of lithium-ion batteries encompassing
-    multi-physics in varied length scales. Journal of The Electrochemical
-    Society, 158(8), A955-A969.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    sigma_e = (
-        3.45 * exp(-798 / T) * (c_e / 1000) ** 3
-        - 48.5 * exp(-1080 / T) * (c_e / 1000) ** 2
-        + 244 * exp(-1440 / T) * (c_e / 1000)
-    )
-
-    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py
deleted file mode 100644
index 2e1f586ac9..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/electrolyte_diffusivity_Kim2011.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pybamm import exp
-
-
-def electrolyte_diffusivity_Kim2011(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC as a function of ion concentration from [1].
-
-     References
-    ----------
-    .. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
-    (2011). Multi-domain modeling of lithium-ion batteries encompassing
-    multi-physics in varied length scales. Journal of The Electrochemical
-    Society, 158(8), A955-A969.
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    D_c_e = (
-        5.84 * 10 ** (-7) * exp(-2870 / T) * (c_e / 1000) ** 2
-        - 33.9 * 10 ** (-7) * exp(-2920 / T) * (c_e / 1000)
-        + 129 * 10 ** (-7) * exp(-3200 / T)
-    )
-
-    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv
deleted file mode 100644
index c0a0b0d75b..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Kim2011/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1200,,
-Cation transference number,0.4,Reported as a function in Kim2011 (Implement later),
-1 + dlnf/dlnc,1,,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Kim2011,,
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Kim2011,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md
deleted file mode 100644
index 07936c3d92..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# LiPF6 electrolyte parameters
-
-Parameters for a LiPF6 electrolyte, from the paper
-
-> Marquis, S. G., Sulzer, V.,  Timms, R.,  Please, C. P.,  &  Chapman, S. J. (2019). “An  asymptotic derivation of a single particle model with electrolyte”. [Journal of The Electrochemical Society](https://doi.org/10.1149/2.0341915jes), 166(15), A3693-A3706. 
-
-and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py
deleted file mode 100644
index 5e080d3f33..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_conductivity_Capiglia1999.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_conductivity_Capiglia1999(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:DMC as a function of ion concentration. The original
-    data is from [1]. The fit is from Dualfoil [2].
-
-    References
-    ----------
-    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
-    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
-    Journal of power sources 81 (1999): 859-862.
-    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    sigma_e = (
-        0.0911
-        + 1.9101 * (c_e / 1000)
-        - 1.052 * (c_e / 1000) ** 2
-        + 0.1554 * (c_e / 1000) ** 3
-    )
-
-    E_k_e = 34700
-    arrhenius = exp(E_k_e / constants.R * (1 / 298.15 - 1 / T))
-
-    return sigma_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py
deleted file mode 100644
index a0308fb81b..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/electrolyte_diffusivity_Capiglia1999.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_diffusivity_Capiglia1999(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration. The original data
-    is from [1]. The fit from Dualfoil [2].
-
-    References
-    ----------
-    .. [1] C Capiglia et al. 7Li and 19F diffusion coefficients and thermal
-    properties of non-aqueous electrolyte solutions for rechargeable lithium batteries.
-    Journal of power sources 81 (1999): 859-862.
-    .. [2] http://www.cchem.berkeley.edu/jsngrp/fortran.html
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    D_c_e = 5.34e-10 * exp(-0.65 * c_e / 1000)
-    E_D_e = 37040
-    arrhenius = exp(E_D_e / constants.R * (1 / 298.15 - 1 / T))
-
-    return D_c_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv
deleted file mode 100644
index ed7e67cff3..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Marquis2019/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,Scott Moura FastDFN,
-Cation transference number,0.4,Scott Moura FastDFN,
-1 + dlnf/dlnc,1,,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Capiglia1999,, 
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Capiglia1999,,
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md
deleted file mode 100644
index 4c8b672be2..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# LiPF6 electrolyte parameters
-
-Parameters for a LiPF6 electrolyte, from the paper
-
-> A. Nyman, M. Behm, and G. Lindbergh, ["Electrochemical characterisation and modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"](https://www.sciencedirect.com/science/article/pii/S0013468608005045) Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
-
-and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py
deleted file mode 100644
index f8885591f4..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_conductivity_Nyman2008.py
+++ /dev/null
@@ -1,29 +0,0 @@
-def electrolyte_conductivity_Nyman2008(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
-    comes from [1].
-    References
-    ----------
-    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
-    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
-    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    sigma_e = (
-        0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000)
-    )
-
-    # Nyman et al. (2008) does not provide temperature dependence
-
-    return sigma_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py
deleted file mode 100644
index 2a832539d6..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/electrolyte_diffusivity_Nyman2008.py
+++ /dev/null
@@ -1,27 +0,0 @@
-def electrolyte_diffusivity_Nyman2008(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
-    comes from [1]
-    References
-    ----------
-    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
-    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
-    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10
-
-    # Nyman et al. (2008) does not provide temperature dependence
-
-    return D_c_e
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv
deleted file mode 100644
index 8354b0b9c7..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Nyman2008/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,Chen 2020,
-Cation transference number,0.2594,Chen 2020,
-1 + dlnf/dlnc,1,,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Nyman2008,Nyman 2008, 
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Nyman2008,Nyman 2008, 
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md
deleted file mode 100644
index b7cef62955..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# LiPF6 electrolyte parameters
-
-Parameters for a LiPF6 electrolyte, from the paper
-
-> Marquis, S. G., Sulzer, V.,  Timms, R.,  Please, C. P.,  &  Chapman, S. J. (2019). “An  asymptotic derivation of a single particle model with electrolyte”. [Journal of The Electrochemical Society](https://doi.org/10.1149/2.0341915jes), 166(15), A3693-A3706. 
-> P. Ramadass, Bala Haran, Parthasarathy M. Gomadam, Ralph White, and Branko N. Popov. "Development of First Principles Capacity Fade Model for Li-Ion Cells." (2004)
-
-and references therein.
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py
deleted file mode 100644
index a3ffb2fa60..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_conductivity_Ramadass2004.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_conductivity_Ramadass2004(c_e, T):
-    """
-    Conductivity of LiPF6 in EC:DMC as a function of ion concentration.
-    Concentration should be in dm3 in the function.
-
-    References
-    ----------
-    .. [1] P. Ramadass, Bala Haran, Parthasarathy M. Gomadam, Ralph White, and Branko
-    N. Popov. "Development of First Principles Capacity Fade Model for Li-Ion Cells."
-    (2004)
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-    # mol.m-3 to mol.dm-3, original function is likely in mS/cm
-    # The function is not in Arora 2000 as reported in Ramadass 2004
-
-    cm = 1e-6 * c_e  # here it should be only 1e-3
-
-    sigma_e = (
-        4.1253 * (10 ** (-4))
-        + 5.007 * cm
-        - 4.7212 * (10 ** 3) * (cm ** 2)
-        + 1.5094 * (10 ** 6) * (cm ** 3)
-        - 1.6018 * (10 ** 8) * (cm ** 4)
-    ) * 1e3  # and here there should not be an exponent
-
-    E_k_e = 34700
-    arrhenius = exp(E_k_e / constants.R * (1 / 298.15 - 1 / T))
-
-    return sigma_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py
deleted file mode 100644
index 22134a70b7..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/electrolyte_diffusivity_Ramadass2004.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from pybamm import exp, constants
-
-
-def electrolyte_diffusivity_Ramadass2004(c_e, T):
-    """
-    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration.
-
-    References
-    ----------
-    .. [1] P. Ramadass, Bala Haran, Parthasarathy M. Gomadam, Ralph White, and Branko
-    N. Popov. "Development of First Principles Capacity Fade Model for Li-Ion Cells."
-    (2004)
-
-    Parameters
-    ----------
-    c_e: :class:`pybamm.Symbol`
-        Dimensional electrolyte concentration
-    T: :class:`pybamm.Symbol`
-        Dimensional temperature
-
-
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Solid diffusivity
-    """
-
-    D_c_e = 7.5e-10
-    E_D_e = 37040
-    arrhenius = exp(E_D_e / constants.R * (1 / 298.15 - 1 / T))
-
-    return D_c_e * arrhenius
diff --git a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv
deleted file mode 100644
index c10a6d80d8..0000000000
--- a/pybamm/input/parameters/lithium-ion/cells/electrolytes/lipf6_Ramadass2004/parameters.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-Name [units],Value,Reference,Notes
-# Empty rows and rows starting with ‘#’ will be ignored,,,
-,,,
-# Electrolyte properties,,,
-Typical electrolyte concentration [mol.m-3],1000,Ramadass,
-Cation transference number,0.363,Ramadass,
-1 + dlnf/dlnc,1,,
-Electrolyte diffusivity [m2.s-1],[function]electrolyte_diffusivity_Ramadass2004,, 
-Electrolyte conductivity [S.m-1],[function]electrolyte_conductivity_Ramadass2004,,
diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
deleted file mode 100644
index b4414f3c61..0000000000
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/plating_exchange_current_density_OKane2020.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from pybamm import Scalar
-
-
-def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
-    """
-    Exchange-current density for Li plating reaction [A.m-2].
-    References
-    ----------
-    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
-    Monica Marinescu. "Physical origin of the differential voltage minimum associated
-    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
-    167, no. 9 (2020): 090540.
-    Parameters
-    ----------
-    c_e : :class:`pybamm.Symbol`
-        Electrolyte concentration [mol.m-3]
-    c_Li : :class:`pybamm.Symbol`
-        Plated lithium concentration [mol.m-3]
-    T : :class:`pybamm.Symbol`
-        Temperature [K]
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Exchange-current density [A.m-2]
-    """
-
-    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
-    # "Exchange-current density for plating [A.m-2]"
-
-    return Scalar(0.001)
diff --git a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py b/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
deleted file mode 100644
index d24d282db2..0000000000
--- a/pybamm/input/parameters/lithium-ion/negative_electrodes/graphite_Yang2017/plating_exchange_current_density_OKane2020.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from pybamm import Scalar
-
-
-def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
-    """
-    Exchange-current density for Li plating reaction [A.m-2].
-    References
-    ----------
-    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
-    Monica Marinescu. "Physical origin of the differential voltage minimum associated
-    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
-    167, no. 9 (2020): 090540.
-    Parameters
-    ----------
-    c_e : :class:`pybamm.Symbol`
-        Electrolyte concentration [mol.m-3]
-    c_Li : :class:`pybamm.Symbol`
-        Plated lithium concentration [mol.m-3]
-    T : :class:`pybamm.Symbol`
-        Temperature [K]
-    Returns
-    -------
-    :class:`pybamm.Symbol`
-        Exchange-current density [A.m-2]
-    """
-
-    # k_plating = Parameter("Lithium plating kinetic rate constant [m.s-1]")
-
-    return Scalar(0.001)
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index d36b9a8578..609b9f0971 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -271,12 +271,6 @@ def __init__(self, extra_options):
                 "('true' or 'false')"
             )
 
-        if options["lithium plating porosity change"] in [True, False]:
-            raise pybamm.OptionError(
-                "lithium plating porosity change must now be given in string format "
-                "('true' or 'false')"
-            )
-
         if options["dimensionality"] == 0:
             if options["current collector"] not in ["uniform"]:
                 raise pybamm.OptionError(
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
index 9f7c4389ac..13b41c69be 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
@@ -62,7 +62,7 @@ def __init__(self, name="Doyle-Fuller-Newman half cell model", options=None):
             "working electrode": param.L_x,
             "separator": param.L_x,
             "working particle": R_w_typ,
-            "current collector y": param.L_y,
+            "current collector y": param.L_z,
             "current collector z": param.L_z,
         }
 
@@ -434,7 +434,6 @@ def default_spatial_methods(self):
     def new_copy(self, build=False):
         new_model = self.__class__(name=self.name, options=self.options)
         new_model.use_jacobian = self.use_jacobian
-        # new_model.use_simplify = self.use_simplify
         new_model.convert_to_format = self.convert_to_format
         new_model.timescale = self.timescale
         new_model.length_scales = self.length_scales

From b7242723bcf702e22af0dc3b86156ccb5c55edb4 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:22:03 +0000
Subject: [PATCH 34/46] Update
 pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../submodels/porosity/leading_reaction_driven_porosity.py       | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
index f3563c924d..2770ae2ba4 100644
--- a/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/leading_reaction_driven_porosity.py
@@ -19,7 +19,6 @@ class LeadingOrder(BaseModel):
 
     def __init__(self, param, options):
         super().__init__(param, options)
-        self.options = options
 
     def get_fundamental_variables(self):
 

From b8e296e8e3ee36f0be14fbaf4db17ba31a67c8b9 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:22:14 +0000
Subject: [PATCH 35/46] Update
 pybamm/models/submodels/porosity/full_reaction_driven_porosity.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../models/submodels/porosity/full_reaction_driven_porosity.py   | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
index e1df79ed1c..2e94f3e04a 100644
--- a/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/full_reaction_driven_porosity.py
@@ -19,7 +19,6 @@ class Full(BaseModel):
 
     def __init__(self, param, options):
         super().__init__(param, options)
-        self.options = options
 
     def get_fundamental_variables(self):
 

From 5a2c67c6f7f491afa280017a15c86e47426cc868 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:22:21 +0000
Subject: [PATCH 36/46] Update
 pybamm/models/submodels/porosity/base_porosity.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 pybamm/models/submodels/porosity/base_porosity.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/models/submodels/porosity/base_porosity.py b/pybamm/models/submodels/porosity/base_porosity.py
index 6c6841e09b..023270f623 100644
--- a/pybamm/models/submodels/porosity/base_porosity.py
+++ b/pybamm/models/submodels/porosity/base_porosity.py
@@ -17,7 +17,7 @@ class BaseModel(pybamm.BaseSubModel):
     """
 
     def __init__(self, param, options):
-        super().__init__(param)
+        super().__init__(param, options=options)
 
     def _get_standard_porosity_variables(
         self, eps_n, eps_s, eps_p, set_leading_order=False

From a13b03534adf499a6f4d03e5dfda400d8b8a08c4 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:22:34 +0000
Subject: [PATCH 37/46] Update
 pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../lithium_platings/yang2017_Li_plating/parameters.csv          | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
index 310e1d7ada..3fd808e948 100644
--- a/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv
@@ -3,6 +3,5 @@ Name [units],Value,Reference,Notes
 ,,,
 # Lithium plating parameters,,,
 Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
-Lithium plating kinetic rate constant [m.s-1],1.00E+00,,Not required for Yang2017
 Exchange-current density for plating [A.m-2],0.001,,
 Initial plated lithium concentration [mol.m-3],0.00E+00,,

From 651d21f9c46a9d238738975ef3010b0d1d94fd93 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:22:59 +0000
Subject: [PATCH 38/46] Update
 tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../test_full_battery_models/test_lithium_ion/test_spm.py      | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
index 01e3b82958..cd46328c5b 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
@@ -168,8 +168,7 @@ def test_surface_form_differential(self):
     def test_surface_form_algebraic(self):
         options = {"surface form": "algebraic"}
         model = pybamm.lithium_ion.SPM(options)
-        param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
-        modeltest = tests.StandardModelTest(model, parameter_values=param)
+        modeltest = tests.StandardModelTest(model)
         modeltest.test_all()
 
     def test_well_posed_irreversible_plating_with_porosity(self):

From 499805ca8aa536abb35b43c87acc5b0ac239efd2 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:23:14 +0000
Subject: [PATCH 39/46] Update
 pybamm/models/submodels/interface/lithium_plating/reversible_plating.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../submodels/interface/lithium_plating/reversible_plating.py    | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
index 782453f45c..ad0b005c58 100644
--- a/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/reversible_plating.py
@@ -1,3 +1,4 @@
+#
 # Class for reversible lithium plating
 #
 import pybamm

From f9b5370823f7b3f77ed034dc298fa1262f27f8a7 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:23:27 +0000
Subject: [PATCH 40/46] Update
 tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../test_full_battery_models/test_lithium_ion/test_yang2017.py  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py
index 4525d7f999..c8b3bfdfad 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_yang2017.py
@@ -3,7 +3,7 @@
 import tests
 
 
-class TestYANG2017(unittest.TestCase):
+class TestYang2017(unittest.TestCase):
     def test_basic_processing(self):
         model = pybamm.lithium_ion.Yang2017()
         modeltest = tests.StandardModelTest(model)

From 149c365c1c2cc2f83948d692c5650aa00024183b Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:23:43 +0000
Subject: [PATCH 41/46] Update
 tests/unit/test_models/test_full_battery_models/test_base_battery_model.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../test_full_battery_models/test_base_battery_model.py     | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 6ca7d9869d..4eb52c97bf 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -237,12 +237,6 @@ def test_options(self):
                 }
             )
 
-        with self.assertRaisesRegex(
-            pybamm.OptionError,
-            "lithium plating porosity change must now be given in string format",
-        ):
-            pybamm.BaseBatteryModel({"lithium plating porosity change": True})
-
     def test_build_twice(self):
         model = pybamm.lithium_ion.SPM()  # need to pick a model to set vars and build
         with self.assertRaisesRegex(pybamm.ModelError, "Model already built"):

From 731048e7e0145e42c4dcb5e23946b011f5296cd9 Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:24:08 +0000
Subject: [PATCH 42/46] Update
 pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .../submodels/interface/lithium_plating/irreversible_plating.py  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
index 947085df0d..16805f0f89 100644
--- a/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/irreversible_plating.py
@@ -1,3 +1,4 @@
+#
 # Class for irreversible lithium plating
 #
 import pybamm

From acd47acfbba23e94d181ace0a3eddc1c20ee339e Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:24:51 +0000
Subject: [PATCH 43/46] Update pybamm/CITATIONS.txt

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 pybamm/CITATIONS.txt | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pybamm/CITATIONS.txt b/pybamm/CITATIONS.txt
index a870a8aff6..61ccc186d4 100644
--- a/pybamm/CITATIONS.txt
+++ b/pybamm/CITATIONS.txt
@@ -371,7 +371,7 @@ doi={10.1149/2.0661810jes}
   year={2004},
   publisher={IOP Publishing},
   doi={10.1149/1.1634273},
-  }
+}
 
 
 @article{Newman1962,
@@ -394,4 +394,3 @@ doi={10.1149/2.0661810jes}
   year={2020},
   publisher={Elsevier}
 }
-

From 276b1c656143132d0cb88be66f4a6c26b7eaceba Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 11:30:31 +0000
Subject: [PATCH 44/46] Delete python

---
 python | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 python

diff --git a/python b/python
deleted file mode 100644
index e69de29bb2..0000000000

From c7154e5e0d5d6ee61e0195cdf8a780edbcaee82b Mon Sep 17 00:00:00 2001
From: Nwanoro <nwanoro@lancaster.ac.uk>
Date: Fri, 19 Mar 2021 16:44:52 +0000
Subject: [PATCH 45/46] Changes for changelog.md

---
 CHANGELOG.md                                | 2 +-
 docs/source/models/lithium_ion/index.rst    | 1 +
 docs/source/models/lithium_ion/yang2017.rst | 7 +++++++
 3 files changed, 9 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/models/lithium_ion/yang2017.rst

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b6c67c1174..4fd34b99d9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,11 +1,11 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM)
 
 ## Features
-
 -   Added `NewmanTobias` li-ion battery model ([#1423](https://github.com/pybamm-team/PyBaMM/pull/1423))
 -   Added `plot_voltage_components` to easily plot the component overpotentials that make up the voltage ([#1419](https://github.com/pybamm-team/PyBaMM/pull/1419))
 -   Made `QuickPlot` more customizable and added an example ([#1419](https://github.com/pybamm-team/PyBaMM/pull/1419))
 -   `Solution` objects can now be created by stepping *different* models ([#1408](https://github.com/pybamm-team/PyBaMM/pull/1408))
+-   Added Yang et al 2017 model that couples irreversible lithium plating, SEI growth and change in porosity which produces a transition from linear to nonlinear degradation pattern of lithium-ion battery over extended cycles([#1398](https://github.com/pybamm-team/PyBaMM/pull/1398))
 -   Added support for Python 3.9 and dropped support for Python 3.6. Python 3.6 may still work but is now untested ([#1370](https://github.com/pybamm-team/PyBaMM/pull/1370))
 -   Added the electrolyte overpotential and Ohmic losses for full conductivity, including surface form ([#1350](https://github.com/pybamm-team/PyBaMM/pull/1350))
 -   Added functionality to `Citations` to print formatted citations ([#1340](https://github.com/pybamm-team/PyBaMM/pull/1340))
diff --git a/docs/source/models/lithium_ion/index.rst b/docs/source/models/lithium_ion/index.rst
index c3cb147cd1..07cbed2c7e 100644
--- a/docs/source/models/lithium_ion/index.rst
+++ b/docs/source/models/lithium_ion/index.rst
@@ -8,3 +8,4 @@ Lithium-ion Models
   spme
   dfn
   newman_tobias
+  yang2017
diff --git a/docs/source/models/lithium_ion/yang2017.rst b/docs/source/models/lithium_ion/yang2017.rst
new file mode 100644
index 0000000000..58f621b2e3
--- /dev/null
+++ b/docs/source/models/lithium_ion/yang2017.rst
@@ -0,0 +1,7 @@
+Yang et al 2017
+=========================
+
+.. autoclass:: pybamm.lithium_ion.Yang2017
+    :members:
+
+

From c092252f53220e18eb9cfe07d1f4df7f88fb36ac Mon Sep 17 00:00:00 2001
From: KennethNwanoro <78538806+KennethNwanoro@users.noreply.github.com>
Date: Fri, 19 Mar 2021 16:55:16 +0000
Subject: [PATCH 46/46] Update docs/source/models/lithium_ion/yang2017.rst

Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 docs/source/models/lithium_ion/yang2017.rst | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/source/models/lithium_ion/yang2017.rst b/docs/source/models/lithium_ion/yang2017.rst
index 58f621b2e3..06134be58d 100644
--- a/docs/source/models/lithium_ion/yang2017.rst
+++ b/docs/source/models/lithium_ion/yang2017.rst
@@ -1,7 +1,6 @@
 Yang et al 2017
-=========================
+===============
 
 .. autoclass:: pybamm.lithium_ion.Yang2017
     :members:
 
-