Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO2 reduction electrocatalysis

The Tafel slope is a key parameter often quoted to characterize the efficacy of an electrochemical catalyst. In this paper, we develop a Bayesian data analysis approach to estimate the Tafel slope from experimentally-measured current-voltage data. Our approach obviates the human intervention required by current literature practice for Tafel estimation, and provides robust, distributional uncertainty estimates. Using synthetic data, we illustrate how data insufficiency can unknowingly influence current fitting approaches, and how our approach allays these concerns. We apply our approach to conduct a comprehensive re-analysis of data from the CO2 reduction literature. This analysis reveals no systematic preference for Tafel slopes to cluster around certain cardinal values (e.g. 60 or 120 mV/decade). We hypothesize several plausible physical explanations for this observation, and discuss the implications of our finding for mechanistic analysis in electrochemical kinetic investigations. The Tafel slope in electrochemical catalysis is usually determined from experimental data and remains error-prone. Here, the authors develop a Bayesian approach for Tafel slope quantification, and apply it to study the prevalence of certain cardinal Tafel slopes in the electrochemical CO2 reduction literature.

Automated summary

The authors introduce a Bayesian data analysis approach to estimate the Tafel slope of electrochemical catalysts, providing robust uncertainty estimates and alleviating the need for human intervention. By re-analyzing data from the CO2 reduction literature, they find no systematic preference for certain cardinal Tafel slopes.