diff --git a/CHANGELOG.md b/CHANGELOG.md
index cef03a025e..98d7527bd7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,8 +2,9 @@
 
 ## Features
 
-- Added sensitivity calculation support for `pybamm.Simulation` and `pybamm.Experiment` ([#4415](https://github.com/pybamm-team/PyBaMM/pull/4415))
 - Added OpenMP parallelization to IDAKLU solver for lists of input parameters ([#4449](https://github.com/pybamm-team/PyBaMM/pull/4449))
+- Porosity change now works for composite electrode ([#4417](https://github.com/pybamm-team/PyBaMM/pull/4417))
+- Added sensitivity calculation support for `pybamm.Simulation` and `pybamm.Experiment` ([#4415](https://github.com/pybamm-team/PyBaMM/pull/4415))
 - Added phase-dependent particle options to LAM #4369
 - Added a lithium ion equivalent circuit model with split open circuit voltages for each electrode (`SplitOCVR`). ([#4330](https://github.com/pybamm-team/PyBaMM/pull/4330))
 
@@ -18,6 +19,7 @@
 
 ## Breaking changes
 
+- The names of most SEI and plating parameters have been changed so that they have domains ([#4463](https://github.com/pybamm-team/PyBaMM/pull/4463))
 - The parameters "... electrode OCP entropic change [V.K-1]" and "... electrode volume change" are now expected to be functions of stoichiometry only instead of functions of both stoichiometry and maximum concentration ([#4427](https://github.com/pybamm-team/PyBaMM/pull/4427))
 - Renamed `set_events` function to `add_events_from` to better reflect its purpose. ([#4421](https://github.com/pybamm-team/PyBaMM/pull/4421))
 
diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb
index 63169e6a07..9281f744cf 100644
--- a/docs/source/examples/notebooks/batch_study.ipynb
+++ b/docs/source/examples/notebooks/batch_study.ipynb
@@ -501,15 +501,15 @@
     "    \"Mohtat2020_3\": pybamm.ParameterValues(\"Mohtat2020\"),\n",
     "}\n",
     "\n",
-    "# different values for the parameter \"Inner SEI open-circuit potential [V]\"\n",
+    "# different values for the parameter \"Negative inner SEI open-circuit potential [V]\"\n",
     "inner_sei_oc_v_values = [2.0e-4, 2.7e-4, 3.4e-4]\n",
     "\n",
-    "# updating the value of \"Inner SEI open-circuit potential [V]\" in all the dictionary items\n",
+    "# updating the value of \"Negative inner SEI open-circuit potential [V]\" in all the dictionary items\n",
     "for _, v, inner_sei_oc_v in zip(\n",
     "    parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values\n",
     "):\n",
     "    v.update(\n",
-    "        {\"Inner SEI open-circuit potential [V]\": inner_sei_oc_v},\n",
+    "        {\"Negative inner SEI open-circuit potential [V]\": inner_sei_oc_v},\n",
     "    )\n",
     "\n",
     "# creating a Single Particle Model with \"electron-mitigation limited\" SEI\n",
@@ -527,7 +527,7 @@
     "batch_study.solve(initial_soc=1)\n",
     "\n",
     "labels = [\n",
-    "    f\"Inner SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n",
+    "    f\"Negative inner SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n",
     "    for inner_sei_oc_v in inner_sei_oc_v_values\n",
     "]\n",
     "batch_study.plot(labels=labels)"
@@ -627,7 +627,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.12 ('conda_jl')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -641,7 +641,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
@@ -663,5 +663,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/docs/source/examples/notebooks/models/half-cell.ipynb b/docs/source/examples/notebooks/models/half-cell.ipynb
index 2085162694..90de72868b 100644
--- a/docs/source/examples/notebooks/models/half-cell.ipynb
+++ b/docs/source/examples/notebooks/models/half-cell.ipynb
@@ -65,14 +65,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -109,19 +107,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 285.669 and h = 7.17426e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 285.669 and h = 2.09913e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -156,14 +152,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -180,7 +174,6 @@
     "    }\n",
     ")\n",
     "param_GrSi = pybamm.ParameterValues(\"OKane2022_graphite_SiOx_halfcell\")\n",
-    "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 1.5e-07})\n",
     "var_pts = {\"x_n\": 1, \"x_s\": 5, \"x_p\": 7, \"r_n\": 1, \"r_p\": 30}\n",
     "exp_degradation = pybamm.Experiment(\n",
     "    [\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"]\n",
@@ -217,14 +210,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -249,7 +240,7 @@
    "id": "fbc7da60",
    "metadata": {},
    "source": [
-    "The SEI growth is slow compared to the reversible component of the lithium plating. What happens if the SEI growth rate is increased?"
+    "The SEI growth is slow compared to the reversible component of the lithium plating. What happens if the SEI growth rate on the lithium metal electrode is increased?"
    ]
   },
   {
@@ -260,19 +251,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 6e-07})\n",
+    "param_GrSi.update(\n",
+    "    {\"Negative SEI reaction exchange current density [A.m-2]\": 6e-07}\n",
+    ")  # 300% increase\n",
     "sim4 = pybamm.Simulation(\n",
     "    model_with_degradation,\n",
     "    parameter_values=param_GrSi,\n",
@@ -297,14 +288,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -329,12 +318,93 @@
    "id": "6e900be5",
    "metadata": {},
    "source": [
-    "The additional SEI increases the cell resistance, preventing the graphite-silicon composite from being fully lithiated, so there is less plating than before."
+    "The additional SEI increases the cell resistance, preventing the graphite-silicon composite from being fully lithiated, so there is less plating than before. What happens if the increase is applied to the porous electrode instead?"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "20d2680d-daae-46e0-a296-4c9dfd35178e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_GrSi.update(\n",
+    "    {\"Negative SEI reaction exchange current density [A.m-2]\": 1.5e-07}\n",
+    ")  # reset to original value\n",
+    "param_GrSi.update(\n",
+    "    {\"Positive SEI reaction exchange current density [A.m-2]\": 6e-07}\n",
+    ")  # 300% increase\n",
+    "sim5 = pybamm.Simulation(\n",
+    "    model_with_degradation,\n",
+    "    parameter_values=param_GrSi,\n",
+    "    experiment=exp_degradation,\n",
+    "    var_pts=var_pts,\n",
+    ")\n",
+    "sol5 = sim5.solve()\n",
+    "t = sol5[\"Time [s]\"].entries\n",
+    "V = sol5[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t, V)\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "86c0c41f-0b12-46f6-922c-4ee318d7fa3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q_SEI_n = sol5[\"Loss of capacity to negative SEI [A.h]\"].entries\n",
+    "Q_SEI_p = sol5[\"Loss of capacity to positive SEI [A.h]\"].entries\n",
+    "Q_SEI_cr = sol5[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n",
+    "Q_pl = sol5[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t, Q_SEI_n, label=\"Negative SEI\")\n",
+    "plt.plot(t, Q_SEI_p, label=\"Positive SEI\")\n",
+    "plt.plot(t, Q_SEI_cr, label=\"SEI on cracks\")\n",
+    "plt.plot(t, Q_pl, label=\"Lithium plating\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f326c29-b3da-4932-a9de-240346e908a6",
+   "metadata": {},
+   "source": [
+    "SEI on the porous electrode has a smaller effect on the cell resistance, because it is spread over the microstructure and is therefore much thinner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "faa82d38",
    "metadata": {},
    "outputs": [
@@ -344,16 +414,17 @@
      "text": [
       "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
       "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-      "[3] 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(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
-      "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
-      "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-      "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-      "[7] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n",
-      "[8] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. 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(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n",
-      "[9] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n",
-      "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "[11] Lars Ole Valøen and Jan N Reimers. Transport properties of lipf6-based li-ion battery electrolytes. Journal of The Electrochemical Society, 152(5):A882, 2005.\n",
-      "[12] Shanshan Xu, Kuan-Hung Chen, Neil P Dasgupta, Jason B Siegel, and Anna G Stefanopoulou. Evolution of dead lithium growth in lithium metal batteries: experimentally validated model of the apparent capacity loss. Journal of The Electrochemical Society, 166(14):A3456, 2019.\n",
+      "[3] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n",
+      "[4] 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(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
+      "[5] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
+      "[6] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[7] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[8] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n",
+      "[9] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. 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(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n",
+      "[10] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n",
+      "[11] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[12] Lars Ole Valøen and Jan N Reimers. Transport properties of lipf6-based li-ion battery electrolytes. Journal of The Electrochemical Society, 152(5):A882, 2005.\n",
+      "[13] Shanshan Xu, Kuan-Hung Chen, Neil P Dasgupta, Jason B Siegel, and Anna G Stefanopoulou. Evolution of dead lithium growth in lithium metal batteries: experimentally validated model of the apparent capacity loss. Journal of The Electrochemical Society, 166(14):A3456, 2019.\n",
       "\n"
      ]
     }
@@ -387,7 +458,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index e84fdbb1ac..184f12638d 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -46,9 +46,9 @@
     "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n",
     "parameter_values.update({\"Ambient 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})\n",
-    "parameter_values.update({\"Lithium plating transfer coefficient\": 0.5})\n",
-    "parameter_values.update({\"Dead lithium decay constant [s-1]\": 1e-4})"
+    "# parameter_values.update({\"Negative lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n",
+    "parameter_values.update({\"Negative lithium plating transfer coefficient\": 0.5})\n",
+    "parameter_values.update({\"Negative dead lithium decay constant [s-1]\": 1e-4})"
    ]
   },
   {
@@ -366,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
index 1ce1cca826..dcb1dac7c5 100644
--- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
+++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
@@ -632,7 +632,7 @@
     "# Changing secondary SEI solvent diffusivity to show different degradation between phases\n",
     "parameter_values.update(\n",
     "    {\n",
-    "        \"Secondary: Outer SEI solvent diffusivity [m2.s-1]\": 2.5000000000000002e-24,\n",
+    "        \"Secondary: Negative outer SEI solvent diffusivity [m2.s-1]\": 2.5000000000000002e-24,\n",
     "    }\n",
     ")\n",
     "\n",
@@ -722,7 +722,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
index fe06dadffe..5d7bd092b4 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
@@ -57,7 +57,7 @@
    "source": [
     "model = pybamm.lithium_ion.SPM({\"SEI\": \"ec reaction limited\"})\n",
     "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n",
-    "parameter_values.update({\"SEI kinetic rate constant [m.s-1]\": 1e-14})"
+    "parameter_values.update({\"Negative SEI kinetic rate constant [m.s-1]\": 1e-14})"
    ]
   },
   {
@@ -450,7 +450,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
index c7f1f0e634..a3e97facfd 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
@@ -64,7 +64,7 @@
    "outputs": [],
    "source": [
     "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n",
-    "parameter_values.update({\"SEI kinetic rate constant [m.s-1]\": 1e-14})\n",
+    "parameter_values.update({\"Negative SEI kinetic rate constant [m.s-1]\": 1e-14})\n",
     "spm = pybamm.lithium_ion.SPM({\"SEI\": \"ec reaction limited\"})"
    ]
   },
@@ -1950,7 +1950,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/src/pybamm/input/parameters/lithium_ion/Ai2020.py b/src/pybamm/input/parameters/lithium_ion/Ai2020.py
index f578d59fa5..d80f20cee2 100644
--- a/src/pybamm/input/parameters/lithium_ion/Ai2020.py
+++ b/src/pybamm/input/parameters/lithium_ion/Ai2020.py
@@ -533,28 +533,28 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "Initial inner SEI on cracks thickness [m]": 2.5e-13,  # avoid division by zero
-        "Initial outer SEI on cracks thickness [m]": 2.5e-13,  # avoid division by zero
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Initial negative inner SEI on cracks thickness [m]": 2.5e-13,
+        "Initial negative outer SEI on cracks thickness [m]": 2.5e-13,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020.py b/src/pybamm/input/parameters/lithium_ion/Chen2020.py
index b3655513a1..1bd0fb279f 100644
--- a/src/pybamm/input/parameters/lithium_ion/Chen2020.py
+++ b/src/pybamm/input/parameters/lithium_ion/Chen2020.py
@@ -226,26 +226,26 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
index 69b622a7c5..3b4a3cc2b0 100644
--- a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
+++ b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
@@ -327,46 +327,51 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Primary: Ratio of lithium moles to SEI moles": 2.0,
-        "Primary: Inner SEI reaction proportion": 0.5,
-        "Primary: Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Primary: Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Primary: SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "Primary: SEI resistivity [Ohm.m]": 200000.0,
-        "Primary: Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Primary: Bulk solvent concentration [mol.m-3]": 2636.0,
-        "Primary: Inner SEI open-circuit potential [V]": 0.1,
-        "Primary: Outer SEI open-circuit potential [V]": 0.8,
-        "Primary: Inner SEI electron conductivity [S.m-1]": 8.95e-14,
-        "Primary: Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Primary: Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Primary: Initial inner SEI thickness [m]": 2.5e-09,
-        "Primary: Initial outer SEI thickness [m]": 2.5e-09,
-        "Primary: EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "Primary: EC diffusivity [m2.s-1]": 2e-18,
-        "Primary: SEI kinetic rate constant [m.s-1]": 1e-12,
-        "Primary: SEI open-circuit potential [V]": 0.4,
-        "Primary: SEI growth activation energy [J.mol-1]": 0.0,
-        "Secondary: Ratio of lithium moles to SEI moles": 2.0,
-        "Secondary: Inner SEI reaction proportion": 0.5,
-        "Secondary: Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Secondary: Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Secondary: SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "Secondary: SEI resistivity [Ohm.m]": 200000.0,
-        "Secondary: Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Secondary: Bulk solvent concentration [mol.m-3]": 2636.0,
-        "Secondary: Inner SEI open-circuit potential [V]": 0.1,
-        "Secondary: Outer SEI open-circuit potential [V]": 0.8,
-        "Secondary: Inner SEI electron conductivity [S.m-1]": 8.95e-14,
-        "Secondary: Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Secondary: Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Secondary: Initial inner SEI thickness [m]": 2.5e-09,
-        "Secondary: Initial outer SEI thickness [m]": 2.5e-09,
-        "Secondary: EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "Secondary: EC diffusivity [m2.s-1]": 2e-18,
-        "Secondary: SEI kinetic rate constant [m.s-1]": 1e-12,
-        "Secondary: SEI open-circuit potential [V]": 0.4,
-        "Secondary: SEI growth activation energy [J.mol-1]": 0.0,
+        "Primary: Ratio of lithium moles to negative SEI moles": 2.0,
+        "Primary: Negative inner SEI reaction proportion": 0.5,
+        "Primary: Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Primary: Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Primary: Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Primary: Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Primary: Negative outer SEI solvent diffusivity [m2.s-1]"
+        "": 2.5000000000000002e-22,
+        "Primary: Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Primary: Negative inner SEI open-circuit potential [V]": 0.1,
+        "Primary: Negative outer SEI open-circuit potential [V]": 0.8,
+        "Primary: Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Primary: Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Primary: Negative lithium interstitial reference concentration [mol.m-3]"
+        "": 15.0,
+        "Primary: Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Primary: Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Primary: Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "Primary: EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Primary: Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Primary: Negative SEI open-circuit potential [V]": 0.4,
+        "Primary: Negative SEI growth activation energy [J.mol-1]": 0.0,
+        "Secondary: Ratio of lithium moles to negative SEI moles": 2.0,
+        "Secondary: Negative inner SEI reaction proportion": 0.5,
+        "Secondary: Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Secondary: Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Secondary: Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Secondary: Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Secondary: Negative outer SEI solvent diffusivity [m2.s-1]"
+        "": 2.5000000000000002e-22,
+        "Secondary: Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Secondary: Negative inner SEI open-circuit potential [V]": 0.1,
+        "Secondary: Negative outer SEI open-circuit potential [V]": 0.8,
+        "Secondary: Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Secondary: Negative inner SEI lithium interstitial diffusivity [m2.s-1]"
+        "": 1e-20,
+        "Secondary: Negative lithium interstitial reference concentration [mol.m-3]"
+        "": 15.0,
+        "Secondary: Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Secondary: Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Secondary: Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "Secondary: EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Secondary: Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Secondary: Negative SEI open-circuit potential [V]": 0.4,
+        "Secondary: Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
         "Negative current collector thickness [m]": 1.2e-05,
diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py
new file mode 100644
index 0000000000..30234c7c35
--- /dev/null
+++ b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py
@@ -0,0 +1,441 @@
+import pybamm
+import os
+import numpy as np
+
+
+def li_metal_electrolyte_exchange_current_density_Xu2019(c_e, c_Li, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between li metal and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Xu, Shanshan, Chen, Kuan-Hung, Dasgupta, Neil P., Siegel, Jason B. and
+    Stefanopoulou, Anna G. "Evolution of Dead Lithium Growth in Lithium Metal Batteries:
+    Experimentally Validated Model of the Apparent Capacity Loss." Journal of The
+    Electrochemical Society 166.14 (2019): A3456-A3463.
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Pure metal lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+    m_ref = 3.5e-8 * pybamm.constants.F  # (A/m2)(mol/m3) - includes ref concentrations
+
+    return m_ref * c_Li**0.7 * c_e**0.3
+
+
+def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
+    c_e, c_s_surf, c_s_max, T
+):
+    """
+    Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in
+    EC:DMC.
+
+    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
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    c_s_max : :class:`pybamm.Symbol`
+        Maximum particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+    m_ref = 6.48e-7  # (A/m2)(m3/mol)**1.5 - includes ref concentrations
+    E_r = 35000
+    arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
+
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
+
+
+def silicon_ocp_lithiation_Mark2016(sto):
+    """
+    silicon Open-circuit Potential (OCP) as a a function of the
+    stoichiometry. The fit is taken from the Enertech cell [1], which is only accurate
+    for 0 < sto < 1.
+
+    References
+    ----------
+    .. [1] Verbrugge M, Baker D, Xiao X. Formulation for the treatment of multiple
+    electrochemical reactions and associated speciation for the Lithium-Silicon
+    electrode[J]. Journal of The Electrochemical Society, 2015, 163(2): A262.
+
+    Parameters
+    ----------
+    sto: double
+       stoichiometry of material (li-fraction)
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        OCP [V]
+    """
+    p1 = -96.63
+    p2 = 372.6
+    p3 = -587.6
+    p4 = 489.9
+    p5 = -232.8
+    p6 = 62.99
+    p7 = -9.286
+    p8 = 0.8633
+
+    U_lithiation = (
+        p1 * sto**7
+        + p2 * sto**6
+        + p3 * sto**5
+        + p4 * sto**4
+        + p5 * sto**3
+        + p6 * sto**2
+        + p7 * sto
+        + p8
+    )
+    return U_lithiation
+
+
+def silicon_ocp_delithiation_Mark2016(sto):
+    """
+    silicon Open-circuit Potential (OCP) as a a function of the
+    stoichiometry. The fit is taken from the Enertech cell [1], which is only accurate
+    for 0 < sto < 1.
+
+    References
+    ----------
+    .. [1] Verbrugge M, Baker D, Xiao X. Formulation for the treatment of multiple
+    electrochemical reactions and associated speciation for the Lithium-Silicon
+    electrode[J]. Journal of The Electrochemical Society, 2015, 163(2): A262.
+
+    Parameters
+    ----------
+    sto: double
+       stoichiometry of material (li-fraction)
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        OCP [V]
+    """
+    p1 = -51.02
+    p2 = 161.3
+    p3 = -205.7
+    p4 = 140.2
+    p5 = -58.76
+    p6 = 16.87
+    p7 = -3.792
+    p8 = 0.9937
+
+    U_delithiation = (
+        p1 * sto**7
+        + p2 * sto**6
+        + p3 * sto**5
+        + p4 * sto**4
+        + p5 * sto**3
+        + p6 * sto**2
+        + p7 * sto
+        + p8
+    )
+    return U_delithiation
+
+
+def silicon_LGM50_electrolyte_exchange_current_density_Chen2020(
+    c_e, c_s_surf, c_s_max, T
+):
+    """
+    Exchange-current density for Butler-Volmer reactions between silicon and LiPF6 in
+    EC:DMC.
+
+    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
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    c_s_max : :class:`pybamm.Symbol`
+        Maximum particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    m_ref = (
+        6.48e-7 * 28700 / 278000
+    )  # (A/m2)(m3/mol)**1.5 - includes ref concentrations
+    E_r = 35000
+    arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
+
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
+
+
+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
+
+
+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
+
+
+# Load data in the appropriate format
+path, _ = os.path.split(os.path.abspath(__file__))
+graphite_ocp_Enertech_Ai2020_data = pybamm.parameters.process_1D_data(
+    "graphite_ocp_Enertech_Ai2020.csv", path=path
+)
+
+
+def graphite_ocp_Enertech_Ai2020(sto):
+    name, (x, y) = graphite_ocp_Enertech_Ai2020_data
+    return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic")
+
+
+# Call dict via a function to avoid errors when editing in place
+def get_parameter_values():
+    """
+    Parameters for a composite graphite/silicon negative electrode, from the paper
+    :footcite:t:`Ai2022`, based on the paper :footcite:t:`Chen2020`, and references
+    therein.
+
+    SEI parameters are example parameters for composite SEI on silicon/graphite. Both
+    phases use the same values, from the paper :footcite:t:`Yang2017`
+    """
+
+    return {
+        "chemistry": "lithium_ion",
+        # sei
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
+        "Primary: Ratio of lithium moles to positive SEI moles": 2.0,
+        "Primary: Positive inner SEI reaction proportion": 0.5,
+        "Primary: Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Primary: Posituve outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Primary: Positive SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Primary: Positive SEI resistivity [Ohm.m]": 200000.0,
+        "Primary: Positive outer SEI solvent diffusivity [m2.s-1]"
+        "": 2.5000000000000002e-22,
+        "Primary: Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0,
+        "Primary: Positive inner SEI open-circuit potential [V]": 0.1,
+        "Primary: Positive outer SEI open-circuit potential [V]": 0.8,
+        "Primary: Positive inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Primary: Positive inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Primary: Positive lithium interstitial reference concentration [mol.m-3]"
+        "": 15.0,
+        "Primary: Initial positive inner SEI thickness [m]": 2.5e-09,
+        "Primary: Initial positive outer SEI thickness [m]": 2.5e-09,
+        "Primary: Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "Primary: EC diffusivity through positive SEI [m2.s-1]": 2e-18,
+        "Primary: Positive SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Primary: Positive SEI open-circuit potential [V]": 0.4,
+        "Primary: Positive SEI growth activation energy [J.mol-1]": 0.0,
+        "Secondary: Ratio of lithium moles to positive SEI moles": 2.0,
+        "Secondary: Positive inner SEI reaction proportion": 0.5,
+        "Secondary: Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Secondary: Positive outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Secondary: Positive SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Secondary: Positive SEI resistivity [Ohm.m]": 200000.0,
+        "Secondary: Positive outer SEI solvent diffusivity [m2.s-1]"
+        "": 2.5000000000000002e-22,
+        "Secondary: Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0,
+        "Secondary: Positive inner SEI open-circuit potential [V]": 0.1,
+        "Secondary: Positive outer SEI open-circuit potential [V]": 0.8,
+        "Secondary: Positive inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Secondary: Positive inner SEI lithium interstitial diffusivity [m2.s-1]"
+        "": 1e-20,
+        "Secondary: Positive lithium interstitial reference concentration [mol.m-3]"
+        "": 15.0,
+        "Secondary: Initial positive inner SEI thickness [m]": 2.5e-09,
+        "Secondary: Initial positive outer SEI thickness [m]": 2.5e-09,
+        "Secondary: Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "Secondary: EC diffusivity through positive SEI [m2.s-1]": 2e-18,
+        "Secondary: Positive SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Secondary: Positive SEI open-circuit potential [V]": 0.4,
+        "Secondary: Positive SEI growth activation energy [J.mol-1]": 0.0,
+        # cell
+        "Negative current collector thickness [m]": 1.2e-05,
+        "Negative electrode thickness [m]": 0.0007,
+        "Positive current collector thickness [m]": 1.2e-05,
+        "Positive electrode thickness [m]": 8.52e-05,
+        "Separator thickness [m]": 1.2e-05,
+        "Electrode height [m]": 0.065,
+        "Electrode width [m]": 1.58,
+        "Cell cooling surface area [m2]": 0.00531,
+        "Cell volume [m3]": 2.42e-05,
+        "Cell thermal expansion coefficient [m.K-1]": 1.1e-06,
+        "Positive current collector conductivity [S.m-1]": 58411000.0,
+        "Positive current collector density [kg.m-3]": 8960.0,
+        "Positive current collector specific heat capacity [J.kg-1.K-1]": 385.0,
+        "Positive current collector thermal conductivity [W.m-1.K-1]": 401.0,
+        "Nominal cell capacity [A.h]": 5.0,
+        "Current function [A]": 5.0,
+        "Contact resistance [Ohm]": 0,
+        # negative electrode
+        "Negative electrode OCP [V]": 0.0,
+        "Negative electrode conductivity [S.m-1]": 10776000.0,
+        "Negative electrode OCP entropic change [V.K-1]": 0.0,
+        "Exchange-current density for lithium metal electrode [A.m-2]"
+        "": li_metal_electrolyte_exchange_current_density_Xu2019,
+        "Negative electrode charge transfer coefficient": 0.5,
+        "Negative electrode double-layer capacity [F.m-2]": 0.2,
+        # positive electrode
+        "Positive electrode conductivity [S.m-1]": 215.0,
+        "Primary: Maximum concentration in positive electrode [mol.m-3]": 28700.0,
+        "Primary: Initial concentration in positive electrode [mol.m-3]": 27700.0,
+        "Primary: Positive particle diffusivity [m2.s-1]": 5.5e-14,
+        "Primary: Positive electrode OCP [V]": graphite_ocp_Enertech_Ai2020,
+        "Positive electrode porosity": 0.25,
+        "Primary: Positive electrode active material volume fraction": 0.735,
+        "Primary: Positive particle radius [m]": 5.86e-06,
+        "Positive electrode Bruggeman coefficient (electrolyte)": 1.5,
+        "Positive electrode Bruggeman coefficient (electrode)": 0,
+        "Positive electrode charge transfer coefficient": 0.5,
+        "Positive electrode double-layer capacity [F.m-2]": 0.2,
+        "Primary: Positive electrode exchange-current density [A.m-2]"
+        "": graphite_LGM50_electrolyte_exchange_current_density_Chen2020,
+        "Primary: Positive electrode density [kg.m-3]": 1657.0,
+        "Positive electrode specific heat capacity [J.kg-1.K-1]": 700.0,
+        "Positive electrode thermal conductivity [W.m-1.K-1]": 1.7,
+        "Primary: Positive electrode OCP entropic change [V.K-1]": 0.0,
+        "Secondary: Maximum concentration in positive electrode [mol.m-3]": 278000.0,
+        "Secondary: Initial concentration in positive electrode [mol.m-3]": 276610.0,
+        "Secondary: Positive particle diffusivity [m2.s-1]": 1.67e-14,
+        "Secondary: Positive electrode lithiation OCP [V]"
+        "": silicon_ocp_lithiation_Mark2016,
+        "Secondary: Positive electrode delithiation OCP [V]"
+        "": silicon_ocp_delithiation_Mark2016,
+        "Secondary: Positive electrode active material volume fraction": 0.015,
+        "Secondary: Positive particle radius [m]": 1.52e-06,
+        "Secondary: Positive electrode exchange-current density [A.m-2]"
+        "": silicon_LGM50_electrolyte_exchange_current_density_Chen2020,
+        "Secondary: Positive electrode density [kg.m-3]": 2650.0,
+        "Secondary: Positive electrode OCP entropic change [V.K-1]": 0.0,
+        # separator
+        "Separator porosity": 0.47,
+        "Separator Bruggeman coefficient (electrolyte)": 1.5,
+        "Separator density [kg.m-3]": 397.0,
+        "Separator specific heat capacity [J.kg-1.K-1]": 700.0,
+        "Separator thermal conductivity [W.m-1.K-1]": 0.16,
+        # electrolyte
+        "Initial concentration in electrolyte [mol.m-3]": 1000.0,
+        "Cation transference number": 0.2594,
+        "Thermodynamic factor": 1.0,
+        "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008,
+        "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008,
+        # experiment
+        "Reference temperature [K]": 298.15,
+        "Total heat transfer coefficient [W.m-2.K-1]": 10.0,
+        "Ambient temperature [K]": 298.15,
+        "Number of electrodes connected in parallel to make a cell": 1.0,
+        "Number of cells connected in series to make a battery": 1.0,
+        "Lower voltage cut-off [V]": 0.005,
+        "Upper voltage cut-off [V]": 1.5,
+        "Open-circuit voltage at 0% SOC [V]": 0.005,
+        "Open-circuit voltage at 100% SOC [V]": 1.5,
+        "Initial concentration in positive electrode [mol.m-3]": 29866.0,
+        "Initial temperature [K]": 298.15,
+        # citations
+        "citations": ["Chen2020", "Ai2022", "Xu2019"],
+    }
diff --git a/src/pybamm/input/parameters/lithium_ion/Ecker2015.py b/src/pybamm/input/parameters/lithium_ion/Ecker2015.py
index 05fbbb2fd7..a3b10a0367 100644
--- a/src/pybamm/input/parameters/lithium_ion/Ecker2015.py
+++ b/src/pybamm/input/parameters/lithium_ion/Ecker2015.py
@@ -316,9 +316,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_e
+    return pybamm.constants.F * k_pl * c_e
 
 
 def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -350,9 +350,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_Li
+    return pybamm.constants.F * k_pl * c_Li
 
 
 def SEI_limited_dead_lithium_OKane2022(L_sei):
@@ -374,9 +374,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei):
         Dead lithium decay rate [s-1]
     """
 
-    gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]")
-    L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]")
-    L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]")
+    gamma_0 = pybamm.Parameter("Negative dead lithium decay constant [s-1]")
+    L_inner_0 = pybamm.Parameter("Initial negative inner SEI thickness [m]")
+    L_outer_0 = pybamm.Parameter("Initial negative outer SEI thickness [m]")
     L_sei_0 = L_inner_0 + L_outer_0
 
     gamma = gamma_0 * L_sei_0 / L_sei
@@ -502,37 +502,37 @@ def get_parameter_values():
         "chemistry": "lithium_ion",
         # lithium plating
         "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
-        "Lithium plating kinetic rate constant [m.s-1]": 1e-10,
-        "Exchange-current density for plating [A.m-2]"
+        "Negative lithium plating kinetic rate constant [m.s-1]": 1e-10,
+        "Exchange-current density for negative lithium plating [A.m-2]"
         "": plating_exchange_current_density_OKane2020,
-        "Exchange-current density for stripping [A.m-2]"
+        "Exchange-current density for negative lithium stripping [A.m-2]"
         "": stripping_exchange_current_density_OKane2020,
-        "Initial plated lithium concentration [mol.m-3]": 0.0,
-        "Typical plated lithium concentration [mol.m-3]": 1000.0,
-        "Lithium plating transfer coefficient": 0.5,
-        "Dead lithium decay constant [s-1]": 1e-06,
-        "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
+        "Initial negative lithium plating concentration [mol.m-3]": 0.0,
+        "Negative lithium plating reference concentration [mol.m-3]": 1000.0,
+        "Negative lithium plating transfer coefficient": 0.5,
+        "Negative dead lithium decay constant [s-1]": 1e-06,
+        "Negative dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
index 267f55e774..0f24aaa309 100644
--- a/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
+++ b/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
@@ -203,9 +203,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_e
+    return pybamm.constants.F * k_pl * c_e
 
 
 def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -237,9 +237,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_Li
+    return pybamm.constants.F * k_pl * c_Li
 
 
 def SEI_limited_dead_lithium_OKane2022(L_sei):
@@ -261,9 +261,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei):
         Dead lithium decay rate [s-1]
     """
 
-    gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]")
-    L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]")
-    L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]")
+    gamma_0 = pybamm.Parameter("Positive dead lithium decay constant [s-1]")
+    L_inner_0 = pybamm.Parameter("Initial positive inner SEI thickness [m]")
+    L_outer_0 = pybamm.Parameter("Initial positive outer SEI thickness [m]")
     L_sei_0 = L_inner_0 + L_outer_0
 
     gamma = gamma_0 * L_sei_0 / L_sei
@@ -424,37 +424,57 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # lithium plating
-        "Lithium plating kinetic rate constant [m.s-1]": 1e-10,
-        "Exchange-current density for plating [A.m-2]"
+        "Positive lithium plating kinetic rate constant [m.s-1]": 1e-10,
+        "Exchange-current density for positive lithium plating [A.m-2]"
         "": plating_exchange_current_density_OKane2020,
-        "Exchange-current density for stripping [A.m-2]"
+        "Exchange-current density for positive lithium stripping [A.m-2]"
         "": stripping_exchange_current_density_OKane2020,
-        "Initial plated lithium concentration [mol.m-3]": 0.0,
-        "Typical plated lithium concentration [mol.m-3]": 1000.0,
-        "Lithium plating transfer coefficient": 0.5,
-        "Dead lithium decay constant [s-1]": 1e-06,
-        "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
+        "Initial positive lithium plating concentration [mol.m-3]": 0.0,
+        "Positive lithium plating reference concentration [mol.m-3]": 1000.0,
+        "Positive lithium plating transfer coefficient": 0.5,
+        "Positive dead lithium decay constant [s-1]": 1e-06,
+        "Positive dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to positive SEI moles": 2.0,
+        "Positive inner SEI reaction proportion": 0.5,
+        "Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Positive outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Positive SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Positive SEI resistivity [Ohm.m]": 200000.0,
+        "Positive outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0,
+        "Positive inner SEI open-circuit potential [V]": 0.1,
+        "Positive outer SEI open-circuit potential [V]": 0.8,
+        "Positive inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Positive inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Positive lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial positive inner SEI thickness [m]": 2.5e-09,
+        "Initial positive outer SEI thickness [m]": 2.5e-09,
+        "Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through positive SEI [m2.s-1]": 2e-18,
+        "Positive SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Positive SEI open-circuit potential [V]": 0.4,
+        "Positive SEI growth activation energy [J.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
         "Negative current collector thickness [m]": 1.4e-05,
diff --git a/src/pybamm/input/parameters/lithium_ion/Marquis2019.py b/src/pybamm/input/parameters/lithium_ion/Marquis2019.py
index 16591eac2d..ff9c570877 100644
--- a/src/pybamm/input/parameters/lithium_ion/Marquis2019.py
+++ b/src/pybamm/input/parameters/lithium_ion/Marquis2019.py
@@ -351,27 +351,26 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI growth transfer coefficient": 0.5,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py b/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py
index 0176c3f6a0..37c6c034f6 100644
--- a/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py
+++ b/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py
@@ -338,31 +338,31 @@ def get_parameter_values():
         "chemistry": "lithium_ion",
         # lithium plating
         "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
-        "Exchange-current density for plating [A.m-2]": 0.001,
-        "Initial plated lithium concentration [mol.m-3]": 0.0,
-        "Typical plated lithium concentration [mol.m-3]": 1000.0,
-        "Lithium plating transfer coefficient": 0.7,
+        "Exchange-current density for negative lithium plating [A.m-2]": 0.001,
+        "Initial negative lithium plating concentration [mol.m-3]": 0.0,
+        "Negative lithium plating reference concentration [mol.m-3]": 1000.0,
+        "Negative lithium plating transfer coefficient": 0.7,
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py
index 1af610f58a..487b782505 100644
--- a/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py
+++ b/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py
@@ -310,26 +310,26 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/OKane2022.py b/src/pybamm/input/parameters/lithium_ion/OKane2022.py
index 4ccb72bf62..af4ac47f15 100644
--- a/src/pybamm/input/parameters/lithium_ion/OKane2022.py
+++ b/src/pybamm/input/parameters/lithium_ion/OKane2022.py
@@ -26,9 +26,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_e
+    return pybamm.constants.F * k_pl * c_e
 
 
 def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -60,9 +60,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_Li
+    return pybamm.constants.F * k_pl * c_Li
 
 
 def SEI_limited_dead_lithium_OKane2022(L_sei):
@@ -84,9 +84,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei):
         Dead lithium decay rate [s-1]
     """
 
-    gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]")
-    L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]")
-    L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]")
+    gamma_0 = pybamm.Parameter("Negative dead lithium decay constant [s-1]")
+    L_inner_0 = pybamm.Parameter("Initial negative inner SEI thickness [m]")
+    L_outer_0 = pybamm.Parameter("Initial negative outer SEI thickness [m]")
     L_sei_0 = L_inner_0 + L_outer_0
 
     gamma = gamma_0 * L_sei_0 / L_sei
@@ -511,39 +511,39 @@ def get_parameter_values():
         "chemistry": "lithium_ion",
         # lithium plating
         "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
-        "Lithium plating kinetic rate constant [m.s-1]": 1e-09,
-        "Exchange-current density for plating [A.m-2]"
+        "Negative lithium plating kinetic rate constant [m.s-1]": 1e-09,
+        "Exchange-current density for negative lithium plating [A.m-2]"
         "": plating_exchange_current_density_OKane2020,
-        "Exchange-current density for stripping [A.m-2]"
+        "Exchange-current density for negative lithium stripping [A.m-2]"
         "": stripping_exchange_current_density_OKane2020,
-        "Initial plated lithium concentration [mol.m-3]": 0.0,
-        "Typical plated lithium concentration [mol.m-3]": 1000.0,
-        "Lithium plating transfer coefficient": 0.65,
-        "Dead lithium decay constant [s-1]": 1e-06,
-        "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
+        "Initial negative lithium plating concentration [mol.m-3]": 0.0,
+        "Negative lithium plating reference concentration [mol.m-3]": 1000.0,
+        "Negative lithium plating transfer coefficient": 0.65,
+        "Negative dead lithium decay constant [s-1]": 1e-06,
+        "Negative dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
         # sei
-        "Ratio of lithium moles to SEI moles": 1.0,
-        "Inner SEI reaction proportion": 0.0,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 0.0,
-        "Initial outer SEI thickness [m]": 5e-09,
-        "Initial inner SEI on cracks thickness [m]": 0,
-        "Initial outer SEI on cracks thickness [m]": 5e-13,  # avoid division by zero
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 38000.0,
+        "Ratio of lithium moles to negative SEI moles": 1.0,
+        "Negative inner SEI reaction proportion": 0.0,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 0.0,
+        "Initial negative outer SEI thickness [m]": 5e-09,
+        "Initial negative inner SEI on cracks thickness [m]": 0,
+        "Initial negative outer SEI on cracks thickness [m]": 5e-13,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 38000.0,
         "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
diff --git a/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
index c343dd23f4..f17ec09a4c 100644
--- a/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
+++ b/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
@@ -56,9 +56,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_e
+    return pybamm.constants.F * k_pl * c_e
 
 
 def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
@@ -90,9 +90,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
         Exchange-current density [A.m-2]
     """
 
-    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+    k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]")
 
-    return pybamm.constants.F * k_plating * c_Li
+    return pybamm.constants.F * k_pl * c_Li
 
 
 def SEI_limited_dead_lithium_OKane2022(L_sei):
@@ -114,9 +114,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei):
         Dead lithium decay rate [s-1]
     """
 
-    gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]")
-    L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]")
-    L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]")
+    gamma_0 = pybamm.Parameter("Positive dead lithium decay constant [s-1]")
+    L_inner_0 = pybamm.Parameter("Initial positive inner SEI thickness [m]")
+    L_outer_0 = pybamm.Parameter("Initial positive outer SEI thickness [m]")
     L_sei_0 = L_inner_0 + L_outer_0
 
     gamma = gamma_0 * L_sei_0 / L_sei
@@ -396,40 +396,59 @@ def get_parameter_values():
         "chemistry": "lithium_ion",
         # lithium plating
         "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
-        "Lithium plating kinetic rate constant [m.s-1]": 1e-09,
-        "Exchange-current density for plating [A.m-2]"
+        "Positive lithium plating kinetic rate constant [m.s-1]": 1e-09,
+        "Exchange-current density for positive lithium plating [A.m-2]"
         "": plating_exchange_current_density_OKane2020,
-        "Exchange-current density for stripping [A.m-2]"
+        "Exchange-current density for positive lithium stripping [A.m-2]"
         "": stripping_exchange_current_density_OKane2020,
-        "Initial plated lithium concentration [mol.m-3]": 0.0,
-        "Typical plated lithium concentration [mol.m-3]": 1000.0,
-        "Lithium plating transfer coefficient": 0.65,
-        "Dead lithium decay constant [s-1]": 1e-06,
-        "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
+        "Initial positive lithium plating concentration [mol.m-3]": 0.0,
+        "Positive lithium plating reference concentration [mol.m-3]": 1000.0,
+        "Positive lithium plating transfer coefficient": 0.65,
+        "Positive dead lithium decay constant [s-1]": 1e-06,
+        "Positive dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
         # sei
-        "Ratio of lithium moles to SEI moles": 1.0,
-        "Inner SEI reaction proportion": 0.0,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 0.0,
-        "Initial outer SEI thickness [m]": 5e-09,
-        "Initial inner SEI on cracks thickness [m]": 0,
-        "Initial outer SEI on cracks thickness [m]": 5e-13,  # avoid division by zero
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 38000.0,
-        "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 1.0,
+        "Negative inner SEI reaction proportion": 0.0,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 0.0,
+        "Initial negative outer SEI thickness [m]": 5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 38000.0,
+        "Ratio of lithium moles to positive SEI moles": 1.0,
+        "Positive inner SEI reaction proportion": 0.0,
+        "Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Positive outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Positive SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Positive SEI resistivity [Ohm.m]": 200000.0,
+        "Positive outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0,
+        "Positive inner SEI open-circuit potential [V]": 0.1,
+        "Positive outer SEI open-circuit potential [V]": 0.8,
+        "Positive inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Positive inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Positive lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial positive inner SEI thickness [m]": 0.0,
+        "Initial positive outer SEI thickness [m]": 5e-09,
+        "Initial positive inner SEI on cracks thickness [m]": 0,
+        "Initial positive outer SEI on cracks thickness [m]": 5e-13,
+        "Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through positive SEI [m2.s-1]": 2e-18,
+        "Positive SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Positive SEI open-circuit potential [V]": 0.4,
+        "Positive SEI growth activation energy [J.mol-1]": 38000.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
         "Negative current collector thickness [m]": 1.2e-05,
diff --git a/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py b/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py
index a1e24da7e3..a8ab71ebc4 100644
--- a/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py
+++ b/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py
@@ -365,26 +365,28 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-06,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.0,
-        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
+        "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
+        "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
         "Negative current collector thickness [m]": 1.7e-05,
         "Negative electrode thickness [m]": 8.8e-05,
diff --git a/src/pybamm/input/parameters/lithium_ion/Xu2019.py b/src/pybamm/input/parameters/lithium_ion/Xu2019.py
index caee487339..76962c3f62 100644
--- a/src/pybamm/input/parameters/lithium_ion/Xu2019.py
+++ b/src/pybamm/input/parameters/lithium_ion/Xu2019.py
@@ -214,27 +214,26 @@ def get_parameter_values():
     return {
         "chemistry": "lithium_ion",
         # sei
-        "Ratio of lithium moles to SEI moles": 2.0,
-        "Inner SEI reaction proportion": 0.5,
-        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
-        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
-        "SEI resistivity [Ohm.m]": 200000.0,
-        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
-        "Bulk solvent concentration [mol.m-3]": 2636.0,
-        "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,
-        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
-        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
-        "Initial inner SEI thickness [m]": 2.5e-09,
-        "Initial outer SEI thickness [m]": 2.5e-09,
-        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
-        "EC diffusivity [m2.s-1]": 2e-18,
-        "SEI kinetic rate constant [m.s-1]": 1e-12,
-        "SEI open-circuit potential [V]": 0.4,
-        "SEI growth activation energy [J.mol-1]": 0.0,
-        "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
+        "Ratio of lithium moles to negative SEI moles": 2.0,
+        "Negative inner SEI reaction proportion": 0.5,
+        "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "Negative SEI resistivity [Ohm.m]": 200000.0,
+        "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0,
+        "Negative inner SEI open-circuit potential [V]": 0.1,
+        "Negative outer SEI open-circuit potential [V]": 0.8,
+        "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Negative lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial negative inner SEI thickness [m]": 2.5e-09,
+        "Initial negative outer SEI thickness [m]": 2.5e-09,
+        "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity through negative SEI [m2.s-1]": 2e-18,
+        "Negative SEI kinetic rate constant [m.s-1]": 1e-12,
+        "Negative SEI open-circuit potential [V]": 0.4,
+        "Negative SEI growth activation energy [J.mol-1]": 0.0,
         "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
         "Negative electrode thickness [m]": 0.0007,
diff --git a/src/pybamm/models/submodels/interface/lithium_plating/plating.py b/src/pybamm/models/submodels/interface/lithium_plating/plating.py
index f019c3b9d8..9950ca1450 100644
--- a/src/pybamm/models/submodels/interface/lithium_plating/plating.py
+++ b/src/pybamm/models/submodels/interface/lithium_plating/plating.py
@@ -27,7 +27,7 @@ def __init__(self, param, domain, x_average, options, phase="primary"):
 
     def get_fundamental_variables(self):
         domain, Domain = self.domain_Domain
-        scale = self.phase_param.c_Li_typ
+        scale = self.phase_param.c_Li_ref
         if self.x_average is True:
             c_plated_Li_av = pybamm.Variable(
                 f"X-averaged {domain} {self.phase_name}lithium plating concentration "
diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py
index 989c90cd9c..0ead0d1134 100644
--- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py
+++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py
@@ -25,36 +25,63 @@ def __init__(self, param, options, x_average):
     def get_coupled_variables(self, variables):
         eps_dict = {}
         for domain in self.options.whole_cell_domains:
+            delta_eps_k = 0
             if domain == "separator":
-                delta_eps_k = 0  # separator porosity does not change
+                pass  # separator porosity does not change
             else:
-                Domain = domain.split()[0].capitalize()
-                L_sei_k = variables[f"{Domain} total SEI thickness [m]"]
-                if Domain == "Negative":
-                    L_sei_0 = self.param.n.prim.L_inner_0 + self.param.n.prim.L_outer_0
-                elif Domain == "Positive":
-                    L_sei_0 = self.param.p.prim.L_inner_0 + self.param.p.prim.L_outer_0
-                L_pl_k = variables[f"{Domain} lithium plating thickness [m]"]
-                L_dead_k = variables[f"{Domain} dead lithium thickness [m]"]
-                L_sei_cr_k = variables[f"{Domain} total SEI on cracks thickness [m]"]
+                dom = domain.split()[0]
+                Domain = dom.capitalize()
                 roughness_k = variables[f"{Domain} electrode roughness ratio"]
+                SEI_option = getattr(self.options, dom)["SEI"]
+                phases_option = getattr(self.options, dom)["particle phases"]
+                phases = self.options.phases[dom]
+                for phase in phases:
+                    if phases_option == "1" and phase == "primary":
+                        # `domain` has one phase
+                        phase_name = ""
+                        pref = ""
+                    else:
+                        # `domain` has more than one phase
+                        phase_name = phase + " "
+                        pref = phase.capitalize() + ": "
+                    L_sei_k = variables[f"{Domain} total {phase_name}SEI thickness [m]"]
+                    if SEI_option == "none":
+                        L_sei_0 = pybamm.Scalar(0)
+                    else:
+                        L_inner_0 = pybamm.Parameter(
+                            f"{pref}Initial {dom} inner SEI thickness [m]"
+                        )
+                        L_outer_0 = pybamm.Parameter(
+                            f"{pref}Initial {dom} outer SEI thickness [m]"
+                        )
+                        L_sei_0 = L_inner_0 + L_outer_0
+                    L_pl_k = variables[
+                        f"{Domain} {phase_name}lithium plating thickness [m]"
+                    ]
+                    L_dead_k = variables[
+                        f"{Domain} {phase_name}dead lithium thickness [m]"
+                    ]
+                    L_sei_cr_k = variables[
+                        f"{Domain} total {phase_name}SEI on cracks thickness [m]"
+                    ]
 
-                L_tot = (
-                    (L_sei_k - L_sei_0)
-                    + L_pl_k
-                    + L_dead_k
-                    + L_sei_cr_k * (roughness_k - 1)
-                )
+                    L_tot = (
+                        (L_sei_k - L_sei_0)
+                        + L_pl_k
+                        + L_dead_k
+                        + L_sei_cr_k * (roughness_k - 1)
+                    )
 
-                a_k = variables[
-                    f"{Domain} electrode surface area to volume ratio [m-1]"
-                ]
+                    a_k = variables[
+                        f"{Domain} electrode {phase_name}"
+                        "surface area to volume ratio [m-1]"
+                    ]
 
-                # This assumes a thin film so curvature effects are neglected.
-                # They could be included (e.g. for a sphere it is
-                # a_n * (L_tot + L_tot ** 2 / R_n + L_tot ** # 3 / (3 * R_n ** 2)))
-                # but it is not clear if it is relevant or not.
-                delta_eps_k = -a_k * L_tot
+                    # This assumes a thin film so curvature effects are neglected.
+                    # They could be included (e.g. for a sphere it is
+                    # a_n * (L_tot + L_tot ** 2 / R_n + L_tot ** # 3 / (3 * R_n ** 2)))
+                    # but it is not clear if it is relevant or not.
+                    delta_eps_k += -a_k * L_tot
 
             domain_param = self.param.domain_params[domain.split()[0]]
             eps_k = domain_param.epsilon_init + delta_eps_k
diff --git a/src/pybamm/parameters/lithium_ion_parameters.py b/src/pybamm/parameters/lithium_ion_parameters.py
index 3902242d78..342eca8059 100644
--- a/src/pybamm/parameters/lithium_ion_parameters.py
+++ b/src/pybamm/parameters/lithium_ion_parameters.py
@@ -359,64 +359,83 @@ def _set_parameters(self):
 
         # SEI parameters
         self.V_bar_inner = pybamm.Parameter(
-            f"{pref}Inner SEI partial molar volume [m3.mol-1]"
+            f"{pref}{Domain} inner SEI partial molar volume [m3.mol-1]"
         )
         self.V_bar_outer = pybamm.Parameter(
-            f"{pref}Outer SEI partial molar volume [m3.mol-1]"
+            f"{pref}{Domain} outer SEI partial molar volume [m3.mol-1]"
         )
-
         self.j0_sei = pybamm.Parameter(
-            f"{pref}SEI reaction exchange current density [A.m-2]"
+            f"{pref}{Domain} SEI reaction exchange current density [A.m-2]"
+        )
+        self.R_sei = pybamm.Parameter(f"{pref}{Domain} SEI resistivity [Ohm.m]")
+        self.D_sol = pybamm.Parameter(
+            f"{pref}{Domain} outer SEI solvent diffusivity [m2.s-1]"
+        )
+        self.c_sol = pybamm.Parameter(
+            f"{pref}Bulk solvent concentration for {domain} SEI [mol.m-3]"
+        )
+        self.U_inner = pybamm.Parameter(
+            f"{pref}{Domain} inner SEI open-circuit potential [V]"
+        )
+        self.U_outer = pybamm.Parameter(
+            f"{pref}{Domain} outer SEI open-circuit potential [V]"
         )
-
-        self.R_sei = pybamm.Parameter(f"{pref}SEI resistivity [Ohm.m]")
-        self.D_sol = pybamm.Parameter(f"{pref}Outer SEI solvent diffusivity [m2.s-1]")
-        self.c_sol = pybamm.Parameter(f"{pref}Bulk solvent concentration [mol.m-3]")
-        self.U_inner = pybamm.Parameter(f"{pref}Inner SEI open-circuit potential [V]")
-        self.U_outer = pybamm.Parameter(f"{pref}Outer SEI open-circuit potential [V]")
         self.kappa_inner = pybamm.Parameter(
-            f"{pref}Inner SEI electron conductivity [S.m-1]"
+            f"{pref}{Domain} inner SEI electron conductivity [S.m-1]"
         )
         self.D_li = pybamm.Parameter(
-            f"{pref}Inner SEI lithium interstitial diffusivity [m2.s-1]"
+            f"{pref}{Domain} inner SEI lithium interstitial diffusivity [m2.s-1]"
         )
         self.c_li_0 = pybamm.Parameter(
-            f"{pref}Lithium interstitial reference concentration [mol.m-3]"
+            f"{pref}{Domain} lithium interstitial reference concentration [mol.m-3]"
+        )
+        self.L_inner_0 = pybamm.Parameter(
+            f"{pref}Initial {domain} inner SEI thickness [m]"
+        )
+        self.L_outer_0 = pybamm.Parameter(
+            f"{pref}Initial {domain} outer SEI thickness [m]"
         )
-        self.L_inner_0 = pybamm.Parameter(f"{pref}Initial inner SEI thickness [m]")
-        self.L_outer_0 = pybamm.Parameter(f"{pref}Initial outer SEI thickness [m]")
+        self.L_sei_0 = self.L_inner_0 + self.L_outer_0
         self.L_inner_crack_0 = pybamm.Parameter(
-            f"{pref}Initial inner SEI on cracks thickness [m]"
+            f"{pref}Initial {domain} inner SEI on cracks thickness [m]"
         )
         self.L_outer_crack_0 = pybamm.Parameter(
-            f"{pref}Initial outer SEI on cracks thickness [m]"
+            f"{pref}Initial {domain} outer SEI on cracks thickness [m]"
+        )
+        self.E_sei = pybamm.Parameter(
+            f"{pref}{Domain} SEI growth activation energy [J.mol-1]"
+        )
+        self.alpha_SEI = pybamm.Parameter(
+            f"{pref}{Domain} SEI growth transfer coefficient"
         )
-
-        self.L_sei_0 = self.L_inner_0 + self.L_outer_0
-        self.E_sei = pybamm.Parameter(f"{pref}SEI growth activation energy [J.mol-1]")
-        self.alpha_SEI = pybamm.Parameter(f"{pref}SEI growth transfer coefficient")
         self.inner_sei_proportion = pybamm.Parameter(
-            f"{pref}Inner SEI reaction proportion"
+            f"{pref}{Domain} inner SEI reaction proportion"
+        )
+        self.z_sei = pybamm.Parameter(
+            f"{pref}Ratio of lithium moles to {domain} SEI moles"
         )
-        self.z_sei = pybamm.Parameter(f"{pref}Ratio of lithium moles to SEI moles")
 
         # EC reaction
         self.c_ec_0 = pybamm.Parameter(
-            f"{pref}EC initial concentration in electrolyte [mol.m-3]"
+            f"{pref}{Domain} EC initial concentration in electrolyte [mol.m-3]"
+        )
+        self.D_ec = pybamm.Parameter(
+            f"{pref}EC diffusivity through {domain} SEI [m2.s-1]"
         )
-        self.D_ec = pybamm.Parameter(f"{pref}EC diffusivity [m2.s-1]")
-        self.k_sei = pybamm.Parameter(f"{pref}SEI kinetic rate constant [m.s-1]")
-        self.U_sei = pybamm.Parameter(f"{pref}SEI open-circuit potential [V]")
+        self.k_sei = pybamm.Parameter(
+            f"{pref}{Domain} SEI kinetic rate constant [m.s-1]"
+        )
+        self.U_sei = pybamm.Parameter(f"{pref}{Domain} SEI open-circuit potential [V]")
 
         # Lithium plating parameters
-        self.c_Li_typ = pybamm.Parameter(
-            f"{pref}Typical plated lithium concentration [mol.m-3]"
+        self.c_Li_ref = pybamm.Parameter(
+            f"{pref}{Domain} lithium plating reference concentration [mol.m-3]"
         )
         self.c_plated_Li_0 = pybamm.Parameter(
-            f"{pref}Initial plated lithium concentration [mol.m-3]"
+            f"{pref}Initial {domain} lithium plating concentration [mol.m-3]"
         )
         self.alpha_plating = pybamm.Parameter(
-            f"{pref}Lithium plating transfer coefficient"
+            f"{pref}{Domain} lithium plating transfer coefficient"
         )
         self.alpha_stripping = 1 - self.alpha_plating
 
@@ -577,27 +596,29 @@ def j0(self, c_e, c_s_surf, T, lithiation=None):
 
     def j0_stripping(self, c_e, c_Li, T):
         """Dimensional exchange-current density for stripping [A.m-2]"""
-        Domain = self.domain.capitalize()
+        domain, Domain = self.domain_Domain
         inputs = {
             f"{Domain} electrolyte concentration [mol.m-3]": c_e,
             f"{Domain} plated lithium concentration [mol.m-3]": c_Li,
             f"{Domain} temperature [K]": T,
         }
         return pybamm.FunctionParameter(
-            f"{self.phase_prefactor}Exchange-current density for stripping [A.m-2]",
+            f"{self.phase_prefactor}"
+            f"Exchange-current density for {domain} lithium stripping [A.m-2]",
             inputs,
         )
 
     def j0_plating(self, c_e, c_Li, T):
         """Dimensional exchange-current density for plating [A.m-2]"""
-        Domain = self.domain.capitalize()
+        domain, Domain = self.domain_Domain
         inputs = {
             f"{Domain} electrolyte concentration [mol.m-3]": c_e,
             f"{Domain} plated lithium concentration [mol.m-3]": c_Li,
             f"{Domain} temperature [K]": T,
         }
         return pybamm.FunctionParameter(
-            f"{self.phase_prefactor}Exchange-current density for plating [A.m-2]",
+            f"{self.phase_prefactor}"
+            f"Exchange-current density for {domain} lithium plating [A.m-2]",
             inputs,
         )
 
@@ -606,7 +627,7 @@ def dead_lithium_decay_rate(self, L_sei):
         Domain = self.domain.capitalize()
         inputs = {f"{Domain} total {self.phase_name}SEI thickness [m]": L_sei}
         return pybamm.FunctionParameter(
-            f"{self.phase_prefactor}Dead lithium decay rate [s-1]", inputs
+            f"{self.phase_prefactor}{Domain} dead lithium decay rate [s-1]", inputs
         )
 
     def U(self, sto, T, lithiation=None):
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py
index 5dc5b2dc94..9bdee1e565 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py
@@ -60,38 +60,84 @@ def test_sei_constant(self):
 
     def test_sei_reaction_limited(self):
         options = {"SEI": "reaction limited"}
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
+        parameter_values.update(
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
+            check_already_exists=False,
+        )
         self.run_basic_processing_test(options)
 
     def test_sei_asymmetric_reaction_limited(self):
         options = {"SEI": "reaction limited (asymmetric)"}
         parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
         parameter_values.update(
-            {"SEI growth transfer coefficient": 0.2, "Current function [A]": -0.07826},
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
             check_already_exists=False,
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
 
     def test_sei_solvent_diffusion_limited(self):
         options = {"SEI": "solvent-diffusion limited"}
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
+        parameter_values.update(
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
+            check_already_exists=False,
+        )
         self.run_basic_processing_test(options)
 
     def test_sei_electron_migration_limited(self):
         options = {"SEI": "electron-migration limited"}
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
+        parameter_values.update(
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
+            check_already_exists=False,
+        )
         self.run_basic_processing_test(options)
 
     def test_sei_interstitial_diffusion_limited(self):
         options = {"SEI": "interstitial-diffusion limited"}
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
+        parameter_values.update(
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
+            check_already_exists=False,
+        )
         self.run_basic_processing_test(options)
 
     def test_sei_ec_reaction_limited(self):
         options = {"SEI": "ec reaction limited"}
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
+        parameter_values.update(
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
+            check_already_exists=False,
+        )
         self.run_basic_processing_test(options)
 
     def test_sei_asymmetric_ec_reaction_limited(self):
         options = {"SEI": "ec reaction limited (asymmetric)"}
         parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
         parameter_values.update(
-            {"SEI growth transfer coefficient": 0.2, "Current function [A]": -0.07826},
+            {
+                "Positive SEI growth transfer coefficient": 0.2,
+                "Current function [A]": -0.07826,
+            },
             check_already_exists=False,
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 7c176249fd..2b5f875bae 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -180,7 +180,7 @@ def test_sei_asymmetric_reaction_limited(self):
         options = {"SEI": "reaction limited (asymmetric)"}
         parameter_values = pybamm.ParameterValues("Marquis2019")
         parameter_values.update(
-            {"SEI growth transfer coefficient": 0.2},
+            {"Negative SEI growth transfer coefficient": 0.2},
             check_already_exists=False,
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
@@ -211,7 +211,7 @@ def test_sei_asymmetric_ec_reaction_limited(self):
         }
         parameter_values = pybamm.ParameterValues("Marquis2019")
         parameter_values.update(
-            {"SEI growth transfer coefficient": 0.2},
+            {"Negative SEI growth transfer coefficient": 0.2},
             check_already_exists=False,
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
@@ -333,6 +333,7 @@ def test_composite_graphite_silicon_sei(self):
             "particle phases": ("2", "1"),
             "open-circuit potential": (("single", "current sigmoid"), "single"),
             "SEI": "ec reaction limited",
+            "SEI porosity change": "true",
         }
         parameter_values = pybamm.ParameterValues("Chen2020_composite")
         name = "Negative electrode active material volume fraction"
diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py
index 4f981ba04c..b46d59419e 100644
--- a/tests/unit/test_experiments/test_simulation_with_experiment.py
+++ b/tests/unit/test_experiments/test_simulation_with_experiment.py
@@ -382,7 +382,7 @@ def test_run_experiment_termination_capacity(self):
         )
         model = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"})
         param = pybamm.ParameterValues("Chen2020")
-        param["SEI kinetic rate constant [m.s-1]"] = 1e-14
+        param["Negative SEI kinetic rate constant [m.s-1]"] = 1e-14
         sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param)
         sol = sim.solve(solver=pybamm.CasadiSolver())
         C = sol.summary_variables["Capacity [A.h]"]
@@ -404,7 +404,7 @@ def test_run_experiment_termination_capacity(self):
         )
         model = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"})
         param = pybamm.ParameterValues("Chen2020")
-        param["SEI kinetic rate constant [m.s-1]"] = 1e-14
+        param["Negative SEI kinetic rate constant [m.s-1]"] = 1e-14
         sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param)
         sol = sim.solve(solver=pybamm.CasadiSolver())
         # all but the last value should be above the termination condition
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 7b690257dc..ba5714e7af 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -569,6 +569,7 @@ def test_well_posed_composite_different_degradation(self):
         options = {
             "particle phases": ("2", "1"),
             "SEI": ("ec reaction limited", "none"),
+            "SEI porosity change": "true",
             "lithium plating": ("reversible", "none"),
             "open-circuit potential": (("current sigmoid", "single"), "single"),
         }
@@ -577,6 +578,7 @@ def test_well_posed_composite_different_degradation(self):
         options = {
             "particle phases": ("2", "1"),
             "SEI": (("ec reaction limited", "solvent-diffusion limited"), "none"),
+            "SEI porosity change": "true",
             "lithium plating": (("reversible", "irreversible"), "none"),
             "open-circuit potential": (("current sigmoid", "single"), "single"),
         }
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
index 4be67175d7..d5ad55c1f2 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
@@ -14,12 +14,15 @@ def test_functions(self):
 
         fun_test = {
             # Lithium plating
-            "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-3),
-            "Exchange-current density for stripping [A.m-2]": (
+            "Exchange-current density for negative lithium plating [A.m-2]": (
+                [1e3, 1e4, T],
+                9.6485e-3,
+            ),
+            "Exchange-current density for negative lithium stripping [A.m-2]": (
                 [1e3, 1e4, T],
                 9.6485e-2,
             ),
-            "Dead lithium decay rate [s-1]": ([1e-8], 5e-7),
+            "Negative dead lithium decay rate [s-1]": ([1e-8], 5e-7),
             # Negative electrode
             "Negative particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14),
             "Negative electrode exchange-current density [A.m-2]": (
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
index 6000b997b7..d8db8839d9 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
@@ -13,12 +13,15 @@ def test_functions(self):
 
         fun_test = {
             # Lithium plating
-            "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-3),
-            "Exchange-current density for stripping [A.m-2]": (
+            "Exchange-current density for positive lithium plating [A.m-2]": (
+                [1e3, 1e4, T],
+                9.6485e-3,
+            ),
+            "Exchange-current density for positive lithium stripping [A.m-2]": (
                 [1e3, 1e4, T],
                 9.6485e-2,
             ),
-            "Dead lithium decay rate [s-1]": ([1e-8], 5e-7),
+            "Positive dead lithium decay rate [s-1]": ([1e-8], 5e-7),
             # Positive electrode
             "Positive particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14),
             "Positive electrode exchange-current density [A.m-2]": (
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py
index 014b467715..49ff25de76 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py
@@ -14,12 +14,15 @@ def test_functions(self):
 
         fun_test = {
             # Lithium plating
-            "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-2),
-            "Exchange-current density for stripping [A.m-2]": (
+            "Exchange-current density for negative lithium plating [A.m-2]": (
+                [1e3, 1e4, T],
+                9.6485e-2,
+            ),
+            "Exchange-current density for negative lithium stripping [A.m-2]": (
                 [1e3, 1e4, T],
                 9.6485e-1,
             ),
-            "Dead lithium decay rate [s-1]": ([1e-8], 5e-7),
+            "Negative dead lithium decay rate [s-1]": ([1e-8], 5e-7),
             # Negative electrode
             "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14),
             "Negative electrode exchange-current density [A.m-2]": (
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py
index beebeb35e3..327f074e0b 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py
@@ -13,12 +13,15 @@ def test_functions(self):
 
         fun_test = {
             # Lithium plating
-            "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-2),
-            "Exchange-current density for stripping [A.m-2]": (
+            "Exchange-current density for positive lithium plating [A.m-2]": (
+                [1e3, 1e4, T],
+                9.6485e-2,
+            ),
+            "Exchange-current density for positive lithium stripping [A.m-2]": (
                 [1e3, 1e4, T],
                 9.6485e-1,
             ),
-            "Dead lithium decay rate [s-1]": ([1e-8], 5e-7),
+            "Positive dead lithium decay rate [s-1]": ([1e-8], 5e-7),
             # Positive electrode
             "Positive particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14),
             "Positive electrode exchange-current density [A.m-2]": (