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Bayesian Data Analysis Reveals No Preference for Cardinal Tafel Slopes in CO2 Reduction Electrocatalysis

preprint
submitted on 21.08.2020 and posted on 24.08.2020 by Aditya Limaye, Joy S. Zeng, Adam Willard, Karthish Manthiram
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.

History

Email Address of Submitting Author

amlimaye@mit.edu

Institution

MIT

Country

USA

ORCID For Submitting Author

0000-0003-0639-4154

Declaration of Conflict of Interest

No conflict of interest.

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