Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO2 reduction electrocatalysis
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Author(s)
Limaye, Aditya M
Zeng, Joy
Willard, Adam P.
Manthiram, Karthish
Date Issued
January 2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Limaye, Aditya M. "Bayesian data analysis reveals no preference for cardinal Tafel slopes in CO2 reduction electrocatalysis." Nature Communications 12, 1 (January 2021): 703 © 2021 The Author(s)
Version
Final published version
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. 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41467-021-20924-y
