부산대학교 도서관

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.
Oxide-derived copper is well-known as a CO2 reduction electrocatalyst, yet the mechanism of its formation and the structure of the active phase remain unclear. Here the reduction of oxide-derived copper is modelled using large-scale molecular dynamics with a neural network potential, providing important insights into the removal of trapped oxygen under operating conditions.