Fast Screening for Copper-Based Bimetallic Electrocatalysts: Efficient Electrocatalytic Reduction of CO2 to C2+ Products on Magnesium-Modified Copper.

Electroreduction of CO2 (CO2 RR) into high value-added chemicals is an attractive route to achieve carbon neutrality. However, the development of an efficient catalyst for CO2 RR is still largely by trial-and-error and is very time-consuming. Herein, we built an electrocatalyst testing platform featuring a home-built automatic flow cell to accelerate the discovery of efficient catalysts. A fast screening of 109 Cu-based bimetallic catalysts in only 55 h identifies Mg combined with Cu as the best electrocatalyst for CO2 to C2+ products. The thus designed Mg-Cu catalyst achieves a Faradaic efficiency (FE) of C2+ products up to 80 % with a current density of 1.0 A cm-2 at -0.77 V versus reversible hydrogen electrode (RHE). Systematic experiments with in situ spectroelectrochemistry analyses show that Mg2+ species stabilize Cu+ sites during CO2 RR and promote the CO2 activation, thus enhancing the *CO coverage to promote C-C coupling.

Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction.

Discovery and optimization of new catalysts can be potentially accelerated by efficient data analysis using machine-learning (ML). In this paper, we record the process of searching for additives in the electrochemical deposition of Cu catalysts for CO2 reduction (CO2RR) using ML, which includes three iterative cycles: "experimental test; ML analysis; prediction and redesign". Cu catalysts are known for CO2RR to obtain a range of products including C1 (CO, HCOOH, CH4, CH3OH) and C2+ (C2H4, C2H6, C2H5OH, C3H7OH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CO2RR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C2+ products. This catalyst discovery process highlights the potential of ML to accelerate material development by efficiently extracting information from a limited number of experimental data.