What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective. Issue 9 (17th February 2023)

• Record Type: Journal Article
• Authors: Rossi, Kevin
• Is Part Of: Physical chemistry chemical physics. Volume 25:Issue 9(2023)
• Page Start: 6867
• Page End: 6876
• Publication Date: 2023-02-17
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d2cp04576a
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces and leverage data-driven methods to quantify the qualitative and quantitative agreement between previous milestone reports. Abstract : We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO2 reduction reaction products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2 reduction reactions trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2 reduction reaction measurements, which result in quantitative disagreement between experiments.