This model has two components explained in this order below:
- MODEL I: Transient-Diffusion Reaction (TDR) Model for a Planar Electrode
- MODEL II: The Transient-Diffusion Reaction-Kinetic (TDRK) Model for a Planar Electrode
- MODEL III: Transient-Diffusion Reaction Model for a Porous Electrode (pTDR)
The first model is based on efforts presented in the following works and adapted for the current format of interactivity:
- Gupta et al. J. Appl. Electrochem. 2006, 36, 161–172
- Resasco et al. ChemElectroChem 2018, 5, 1064–1072
- Kas et al. ChemElectroChem 2015, 2, 354–358
This has as of update 2.0 been extended to also include electrode kinetics (as MODEL II), based on work in:
- Blake et al. Electrochim. Acta 2021, 393, 138987
- Burdyny et al. ACS Sustain. Chem. Eng. 2017, 5 (5), 4031–4040
The third model is an extension of MODEL I and was further inspired by:
- Raciti et al. Nanotechnology 2018, 19, 044001
- Burdyny & Smith. Energy Environ. Sci. 2019, 12, 1442–1453
- Blake et al. Electrochim. Acta 2021, 393, 138987
It is worked on coupling MODEL III with electrode kinetics as well...
A full documentation with all sources and background of relevance and more proper descriptions is currently being written.
***MODEL I: Transient-Diffusion Reaction (TDR) Model for a Planar Electrode***
Fig. 1. Model domain with an electrochemical reaction occurring at a planar electrode.
Solving the coupled species' conservation equations across a diffusion boundary layer of length δ_DBL in contact with a planar cathode at which an electrochemical surface reaction takes place (Fig. 1). The pdepe solver is used to this end to model the behaviour of dissolved CO2, HCO3(-), CO3(2-), OH(-), and H(+). The equations that are solved for the five species' concentration profiles (both in time and space) are of the form
![](data:image/png;base64,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)
with c as concentration, D as diffusion coefficient, and R as source/sink term for the (bi)carbonate equilibrium reactions. The reduction reactions taking place at the cathode surface enter as Neumann boundary conditions at x = 0 and two such reactions are considered. For neutral to alkaline electrolyte the acid components of these chemical reactions may be largely ignored as can be derived from a scaling analysis of the reaction terms. Furthermore, the electrochemical reactions generate OH(-) and therefore serves to raise the pH, further motivating this simplification. This is henceforth referred to as the simplified model, though the option exists to retain the full model equations.
The model file tdr_model.m is supplied with an editor file tdr_model_editor.mlx that allows modification of pertinent parameters to study its influence on for example the concentration profiles, pH, and mass transfer limiting current density. The following parameters may be altered:
- temperature T
- pressure P
- molarity c (of CO2-saturated electrolyte)
- current density j
- Faradaic efficiency η for CO
- diffusion boundary layer thickness δ_DBL
The model file runs several subroutines (attached as separate MATLAB files) in order to determine the necessary input data for the model derived from the above input parameters. These files should be located in the same directory as the model and editor files:
- henry.m CO2 solubiltity as function of temperature and pressure
- sechenov.m CO2 solubiltity as function of ionic strength
- viscosity.m viscosity of electrolyte as function of molarity
- diffusivity.m diffusion coefficients as function of temperature
- carbonateeq.m equilibrium coefficients as function of temperature and ionic strength for (bi)carbonate equilibria
- carbonatekin.m rate coefficients as function of temperature and ionic strength for (bi)carbonate equilibria
- selfionization.m pKw value of water as function of temperature
- bjerrum.m shows the distribution of CO2, HCO3(-), CO3(2-) as function of pH given equilibrium coefficients
Examples are given of the plots returned by running the model through the editor file or data that may be derived from the results.
Fig. 2. Transient concentration and pH profiles upon imposing -5 mA/cm2 at the cathode (x = 0) for 0.1 M KHCO3.
Fig. 3. Mass transfer limiting current density in 0.1 M KHCO3 as function of diffusion boundary layer (DBL) thickness. The model result is compared with the estimate based on the formula on the right (assuming a linear concentration profile).
MODEL II: Transient-Diffusion Reaction-Kinetic Model for a Planar Electrode (TDRK)
The same approach as in MODEL I is made and calculating concentrations across the DBL still relies on the pdepe.m solver. The surface concentrations at the electrode are then used to calculate the concentration polarization alongside kinetic and Ohmic polarization. One of the key results from this model is that it can predict a polarization curve. For example, using kinetic data of a gold electrocatalyst from the work of Chen et al. (J. Am. Chem. Soc. 2012, 134(49), 19969–19972) the polarization curve could be drawn. It indicates that above -0.8 V_RHE it suffers from mass transport limitation of CO2.
Fig. 4. Polarization curves in absence (dotted) and presence (solid) of transport polarization for a gold electrocatalyst as described in the text. Note the potential scale is IR-corrected.
In order to model the electrode kinetics alongside mass transport phenomena, the kinetic parameters ought to be known for every electrochemical reaction that is desired to be included. These are the exchange current density and charge transfer coefficient, which can be found from a Tafel plot. Moreover, an appropriate Butler-Volmer expression needs to be chosen to model the kinetics in a mixed kinetic-mass transfer regime. For the time being, only H2, CO, and C2H4 are included with assumed rate expressions:
Concentrations with subscript 0 denote the bulk concentrations. One may consult the book by Oldham et al. (Electrochemical Science and Technology: Fundamentals and Applications, 1st ed.; 2012) to see how such expressions may be derived on the basis of a reaction mechanism. In the case of CO for example, its expression is based on CO2 + e(-) ---> CO2(-) being the rate-determining step, making its partial current density first-order dependent on CO2 concentration.
The general procedure of the model is shown below. Just as before, the model input requires the user to define a current density and Faradaic efficiency (for each of the products involved). In addition, the kinetic parameters should be specified for each (or set their exchange current density or Faradaic efficiency to zero to ignore). Then it proceeds through an iteration loop for the following reason:
- current density determines reaction rate
- rate determines electrode surface concentrations
- concentrations determine electrode polarization (see Butler-Volmer expression)
- polarization determines overpotential
- overpotential determines current density
Fig. 5. General procedure in achieving self-consistency among concentration, current density, and potential.
This loop is repeated until self-consistency is achieved given by a convergence criterion C, or until the maximum number of iterations is reached. The constraint for the electrode potential E is that the sum of all partial current densities must fit the input current density j, so that
The convergence criterion is set such that the deviation in Faradaic efficiency for all products i in successive iterations (between iteration n and n - 1) is less than C, thus
To facilitate conversion at current densities close to the mass transfer limiting current density, a relaxation parameter lambda may have to be specified (between 0 and 1), defined as
The model file tdrk_model.m is supplied with an editor file tdrk_model_editor.mlx that allows modification of pertinent parameters to study its influence on for example the concentration profiles, pH, and overpotentials. The following parameters may be altered:
- temperature T
- pressure P
- molarity c (of CO2-saturated electrolyte)
- current density j
- Faradaic efficiency η for CO (initial guess)
- exchange current density j_0 and charge transfer coefficient alpha for each electrochemical reaction
- diffusion boundary layer thickness δ_DBL
- maximum number of iterations to allow
- convergence criterion C
- relaxation parameter lambda
The model file runs several subroutines (attached as separate MATLAB files) as before and a few other subroutines are introduced for incorporating electrokinetics in the model. These files should be located in the same directory as the model and editor files:
- nernst.m structure element with thermodynamics of electrochemical reactions
- butlerVolmer.m structure element with kinetic, transport, and Ohmic polarization
Both subroutines are updated during the simulation and contain the necessary kinetic data. The Butler-Volmer expressions listed above can be found in the latter and may be modified if different expressions need to be modelled. The Ohmic polarization can simply be added to a polarization curve as the product of current and high frequency resistance measured in the experiment (e.g. obtained by current interrupt method or impedance spectroscopy).
Figure 6. Kinetic, transport, and Ohmic polarization (from ionic resistance in the electrolyte) superimposed.
MODEL III: Transient-Diffusion Reaction Model for a Porous Electrode (pTDR)
Fig. 7. Model domain with an electrochemical reaction occurring in a porous electrode.
Solving the coupled species' conservation equations across a porous catalyst layer of length δ_CL and diffusion boundary layer of length δ_DBL with an electrochemical reaction taking place in the former (Fig. 4). The pdepe solver is used to this end to model the behaviour of dissolved CO2, HCO3(-), CO3(2-), OH(-), and H(+). The equations that are solved for the five species' concentration profiles (both in time and space) are of similar form as in MODEL I.
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)
The reduction reactions take place homogeneously in the catalyst layer (0th order rate model) and enter as volumetric reaction term in the governing equations. This assumption may break down when significant gradients in CO2 concentration are found in the catalyst layer, and would necessitate a rate order showing dependence on CO2 concentration. If the associated reaction rate constant is known or specified, this may be implemented in the current model (see also the sourced work of Raciti et al.). Support is provided for the following reactions, which can be included or excluded by setting their appropriate Faradaic efficiencies.
![](data:image/png;base64,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)
A no-flux boundary condition is in place for all species at x = 0 with the exception of CO2 concentration (as it is supplied from the gas-diffusion layer side). This is specified as flux boundary condition with k_m as overall mass transfer coefficient. The exact value may be difficult to specify but it is likely <0.1 m/s (see also the sourced material above). At x = 0 all species are at their bulk values, dictated by the chosen electrolyte. In KOH this means that c_CO2 = 0, but in KHCO3 the bulk concentration of CO2 > 0 since HCO3(-) naturally exchanges to some degree (for as prepared 1 M KHCO3 electrolyte, about 3 mM of CO2 is naturally present according to thermodynamic data on its equilibrium coefficients).
Support is provided for neutral and alkaline electrolyte (KHCO3 or KOH) and the acid components of the chemical reactions are ignored as reasoned in MODEL I.
The model file ptdr_model.m is supplied with an editor file ptdr_model_editor.mlx that allows modification of pertinent parameters to study its influence on for example the concentration profiles, pH, and mass transfer limiting current density, or carbon efficiency. The following parameters may be altered:
- temperature T
- pressure P
- molarity c (of CO2-saturated electrolyte)
- current density j
- Faradaic efficiency η for product i
- catalyst layer porosity ε
- catalyst layer thickness δ_CL
- mass transfer coefficient k_m
- diffusion boundary layer thickness δ_DBL
These model and editor files rely on the same subroutines as MODEL I does, thus they are to be placed in the same directory as these scripts. Example below of the plots returned by running the model through the editor file.
Fig. 8. Transient concentration and pH profiles upon imposing -200 mA/cm2 to the cathode (δ_CL = 1 μm, δ_DBL = 1 μm) for 0.5 M KOH.
In addition, carbon efficiency is also calculated as either the fraction of CO2 consumed in the CL versus that entering the system at x = 0 (η_DBL) or the fraction of CO2 utilized in CO2RR compared to total consumption in the CL (η_CL). Their product is reported as total carbon efficiency. Note that this is distinct from single-pass conversion efficiency of an electrolyzer, of which this model has nothing to say about.
Fig. 9. Visual and formula representation of the two carbon efficiencies and overall carbon efficiency.
Fig. 10. Plot of carbon efficiency as function of DBL thickness for a GDE exposed to 1.0 M KHCO3 with η_CO = 0.9 showing how buffering action by the electrolyte decreases with diffusion length and lowers overall carbon efficiency.