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

ABSTRACT: 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.

SUBMITTER: Limaye AM 

PROVIDER: S-EPMC7846806 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature