Multiscale Modeling of CO2 Electrochemical Reduction on Copper Electrocatalysts: A Review of Advancements, Challenges, and Future Directions

Abstract

AbstractAlthough CO2 contributes significantly to global warming, it also offers potential as a raw material for the production of hydrocarbons such as CH4, C2H4 and CH3OH. Electrochemical CO2 reduction reaction (eCO2RR) is an emerging technology that utilizes renewable energy to convert CO2 into valuable fuels, solving environmental and energy problems simultaneously. Insights gained at any individual scale can only provide a limited view of that specific scale. Multiscale modeling, which involves coupling atomistic‐level insights (density functional theory, DFT) and (Molecular Dynamics, MD), with mesoscale (kinetic Monte Carlo, KMC, and microkinetics, MK) and macroscale (computational fluid dynamics, CFD) simulations, has received significant attention recently. While multiscale modeling of eCO2RR on electrocatalysts across all scales is limited due to its complexity, this review offers an overview of recent works on single scales and the coupling of two and three scales, such as “DFT+MD”, “DFT+KMC”, “DFT+MK”, “KMC/MK+CFD” and “DFT+MK/KMC+CFD”, focusing particularly on Cu‐based electrocatalysts as copper is known to be an excellent electrocatalyst for eCO2RR. This sets it apart from other reviews that solely focus exclusively on a single scale or only on a combination of DFT and MK/KMC scales. Furthermore, this review offers a concise overview of machine learning (ML) applications for eCO2RR, an emerging approach that has not yet been reviewed. Finally, this review highlights the key challenges, research gaps and perspectives of multiscale modeling for eCO2RR.