Structure-activity relationship of Cu-based catalysts for the highly efficient CO2 electrochemical reduction reaction

Front Chem. 2023 Feb 9:11:1141453. doi: 10.3389/fchem.2023.1141453. eCollection 2023.

Abstract

Electrocatalytic carbon dioxide reduction (CO2RR) is a relatively feasible method to reduce the atmospheric concentration of CO2. Although a series of metal-based catalysts have gained interest for CO2RR, understanding the structure-activity relationship for Cu-based catalysts remains a great challenge. Herein, three Cu-based catalysts with different sizes and compositions (Cu@CNTs, Cu4@CNTs, and CuNi3@CNTs) were designed to explore this relationship by density functional theory (DFT). The calculation results show a higher degree of CO2 molecule activation on CuNi3@CNTs compared to that on Cu@CNTs and Cu4@CNTs. The methane (CH4) molecule is produced on both Cu@CNTs and CuNi3@CNTs, while carbon monoxide (CO) is synthesized on Cu4@CNTs. The Cu@CNTs showed higher activity for CH4 production with a low overpotential value of 0.36 V compared to CuNi3@CNTs (0.60 V), with *CHO formation considered the potential-determining step (PDS). The overpotential value was only 0.02 V for *CO formation on the Cu4@CNTs, and *COOH formation was the PDS. The limiting potential difference analysis with the hydrogen evolution reaction (HER) indicated that the Cu@CNTs exhibited the highest selectivity of CH4 among the three catalysts. Therefore, the sizes and compositions of Cu-based catalysts greatly influence CO2RR activity and selectivity. This study provides an innovative insight into the theoretical explanation of the origin of the size and composition effects to inform the design of highly efficient electrocatalysts.

Keywords: Cu-based catalysts; compositional effect; density functional theory (DFT); electrochemical CO2 reduction reaction (CO2RR); size effect.

Grants and funding

This work is financially supported by the National Natural Science Foundation of China (grant no. 22108093), the National Key Research and Development Program of China (grant no. 2018YFB1502900), the Natural Science Foundation of Zhejiang Province (grant nos. LY19B060006 and LQ20E030016), and the Innovation Jiaxxing Elite Leadership Plan and Technology Development Project of Jiaxing University (70518040).