铜
扩散
氧气
还原(数学)
氧还原
材料科学
氧化物
理论(学习稳定性)
氧还原反应
化学工程
化学
冶金
热力学
物理化学
计算机科学
电极
物理
数学
工程类
电化学
几何学
有机化学
机器学习
作者
Zan Lian,Federico Dattila,Núria López
标识
DOI:10.1038/s41929-024-01132-5
摘要
Abstract Oxide-derived Cu has an excellent ability to promote C–C coupling in the electrochemical carbon dioxide reduction reaction. However, these materials largely rearrange under reaction conditions; therefore, the nature of the active site remains controversial. Here we study the reduction process of oxide-derived Cu via large-scale molecular dynamics with a precise neural network potential trained on first-principles data and introducing experimental conditions. The oxygen concentration in the most stable oxide-derived Cu increases with an increase of the pH, potential or specific surface area. In long electrochemical experiments, the catalyst would be fully reduced to Cu, but removing all the trapped oxygen takes a considerable amount of time. Although the highly reconstructed Cu surface provides various sites to adsorb oxygen more strongly, the surface oxygen atoms are not stable under common experimental conditions. This work provides insight into the evolution of oxide-derived Cu catalysts and residual oxygen during reaction and also a deep understanding of the nature of active sites.
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