Origin and predictive principle for selective products of electrocatalytic carbon dioxide reduction

Abstract

Direct conversion of carbon dioxide (CO₂) to high-value chemicals or fuels would provide new energy sources and environmental remediation. The conversion process however involves complex reaction pathways with multiple competing products, strongly depending on catalysts and applied potential; conventional computational models fail to accurately predict the process and products. Herein, we have created a computational approach, based on the density functional theory (DFT) and electrical double-layer interface models with explicit hydrogen bonding, to accurately predict potential-dependent reaction pathways, catalytic activity, and product selectivity of electrochemical CO₂ reduction. The onset potentials and the potential to achieve maximum faradaic efficiency of CO₂ reduction were calculated, which agree well with the experimental results for single-atom catalysts. This work provides a fundamental understanding of the complex potential-dependent catalytic behavior, and new approaches for screening of catalysts for not only CO₂ reduction but also more complex reaction systems, such as nitrogen reduction and urea oxidation.