A Combined Density Functional Theory and Microkinetics Simulation Study of Electrochemical CO2 Reduction on Ceria-Supported Bismuth

Abstract

Direct electrochemical CO₂ reduction (ECR) into carbon-based fuels and chemicals is a promising way to upgrade waste CO₂ with renewable energy, contributing to closing carbon cycles and mitigating climate change. Here, we investigate the ECR of Bi–CeO₂ catalysts. Using a combination of density functional theory (DFT), artificial neural networks (ANN), genetic algorithms (GA), and microkinetics simulations, we conducted a comprehensive exploration of active sites, the CO₂-to-formic acid (HCOOH) mechanism and the electrochemical behavior of Bi _x /CeO₂ catalysts. Three representative models were investigated: (i) a Bi atom adsorption on CeO₂ (Bi₁/CeO₂), (ii) a single Bi atom doped in the CeO₂ surface (Bi₁–CeO₂), and (iii) a small cluster of eight Bi atoms adsorbed on CeO₂ (Bi₈/CeO₂). ANN-GA was employed to identify the optimal structure of the Bi₈ clusters on the CeO₂ surface. Our investigation shows various reaction pathways for converting CO₂ to HCOOH and CO. For the structural models featuring Bi on the surface, the HCOO pathway toward HCOOH is the predominant one. Bi doping in CeO₂ predominantly favors the COOH pathway, resulting in CO as the main product. The former models leading to HCOOH exhibit higher current densities than the doped model, which mainly produces CO. Electronic structure analysis shows that stronger electron donation in the Bi₁/CeO₂ model enhances HCOOH current densities by weakening the O–H bond and stabilizing the transition state. We discuss the kinetic differences in current density and selectivity as a function of the electrochemical potential. These findings not only elucidate various CO₂ conversion pathways, which can explain the formation of desirable HCOOH and unwanted CO, but also offer theoretical guidance for the design of electrocatalysts.