Publication | Open Access
Mass Transport in Catalytic Pores of GDE-Based CO2 Electroreduction Systems
14
Citations
42
References
2020
Year
Unknown Venue
Materials ScienceMass TransportChemical EngineeringGas Diffusion ElectrodeEngineeringOxygen Reduction ReactionCatalytic ApplicationSurface ElectrochemistryCatalyst Layer ThicknessCo 2CatalysisChemistryElectrochemical ProcessElectrochemical CellCatalytic ProcessTheoretical ElectrochemistryElectrochemistry
Gas diffusion electrode (GDE)-based setups have shown promising performance for CO 2 electrocatalysis and further development of these systems will be important on the path to industrial feasibility. In this article, we model an effective catalyst pore within a GDE-based flow-cell to study the influence of the catalyst structure and operating conditions on the reaction environment for CO 2 electrocatalysis at practically relevant current densities. Using a generalized modified Poisson-Nernst-Planck (GMPNP) 3D model of the nanoporous catalyst layer, we show that the length of the catalyst pore as well as the boundary conditions at the gas-electrolyte and electrolyte-electrolyte interfaces across this length are highly influential parameters for determining the conditions within the catalyst pore. Pores with the same catalytic surface area can have very different reaction environments depending primarily on the pore length and not the pore radius. Properties such as electrolyte pH and buffer breakdown, ionic strength and CO 2 concentration are also highly-sensitive to the catalyst layer thickness, gas pressure, electrolyte flow rate and the flow-channel geometry. The applied potential impacts the concentration of ionic species in the pore, which in turn determines the solubility of CO 2 available for the reaction. Our results underline the need to understand and manage transport within GDE-based electrocatalysis systems as an essential means to control catalyst performance. Benchmarking of GDE-based electrocatalytic systems against their structural and operational parameters will be important for achieving improvements in performance that can be ultimately translated to large-scale operation.
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