Accessing the Nature of Active Sites and Particle Size Effect for Reduction of Carbon Dioxide over Copper-Based Catalysts
Shiyu Zhen, Xiaoyun Lin, Gong Zhang, Dongfang Cheng, Chenggong Jiang, Xiangcheng Shi, Shican Wu, Zhi‐Jian Zhao, Jinlong Gong
Abstract
Although electrocatalytic conversion of CO 2 (CO 2 ER) over bimetallic Cu-based catalysts has been regarded as a promising and compelling route for the sustainable synthesis of fuels and feedstock when combined by carbon-free electricity, questions still remain concerning the fundamental understanding of the reaction mechanism and the nature of active sites, hampering the rational design of catalyst with great activity and selectivity a priori. We report a global optimization in large scale to obtain serious realistic nanoparticles (NPs) models of different particle sizes and the identification of atom-level structures of active sites for CO products on CuZn and CuAu NPs catalysts during CO 2 ER, using machine learning and density functional theory calculations. After the analysis of 300 surface sites (600 computational data points) through neural network (NN) potential based high-throughput testing, we demonstrate that the bimetallic Cu-based NPs have superior CO 2 ER because there are many bimetallic synergistic effect sites that significantly stabilize the carboxyl intermediate during CO 2 reduction to CO, breaking the inherent linear relationship. This work shed light on the structure–performance relationship over more realistic large NPs, facilitating the rational design of Cu-based catalysts in CO 2 ER.