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. 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.
Additional Information
© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 11 November 2020. Accepted 03 January 2021. Published 29 January 2021. We thank Nathan Corbin, Zachary Schiffer, John Cherian, and Bertrand Neyhouse for useful discussions. We also thank Dr. Ye Li for assistance with releasing the dataset associated with this study. A.M.L. acknowledges a graduate research fellowship from the National Science Foundation under Grant No. 1745302. Work by K.M. and J.S.Z. was supported by the National Science Foundation under grant no. 1955628. A.M.L. and A.P.W. additionally acknowledge support from the Air Force Office of Scientific Research (AFOSR) under award number FA9550-18-1-0420. J.S.Z. acknowledges MathWorks for a MathWorks fellowship. Contributions. Conceptualization: A.M.L., A.P.W., and K.M.; methodology: A.M.L., J.S.Z., A.P.W., and K.M.; software: A.M.L.; reproduction: J.S.Z. and A.M.L.; investigation: A.M.L., A.P.W., and K.M.; writing (original draft): A.M.L.; writing (review and editing): A.M.L., J.S.Z, A.P.W., and K.M.; supervision: A.P.W. and K.M. Data availability. Data that supports the findings of this study is available under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) in Zenodo (https://doi.org/10.5281/zenodo.3995021), with the exception of the excerpted figures from other articles as described in the Supporting information. The excerpted figures are reused under an agreement between MIT and the publishers of the articles (https://libraries.mit.edu/scholarly/publishing/using-published-figures/), where the copyright is owned by the publishers. Code availability. Code that supports the findings of this study is available under the MIT License (https://opensource.org/licenses/MIT) in Zenodo (https://doi.org/10.5281/zenodo.3995021). The authors declare no competing interests. Peer review information. Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.Attached Files
Published - s41467-021-20924-y.pdf
Supplemental Material - 41467_2021_20924_MOESM1_ESM.pdf
Supplemental Material - 41467_2021_20924_MOESM2_ESM.pdf
Files
Additional details
- PMCID
- PMC7846806
- Eprint ID
- 114610
- Resolver ID
- CaltechAUTHORS:20220505-565124000
- DGE-1745302
- NSF Graduate Research Fellowship
- CHE-1955628
- NSF
- FA9550-18-1-0420
- Air Force Office of Scientific Research (AFOSR)
- MathWorks
- Created
-
2022-05-12Created from EPrint's datestamp field
- Updated
-
2022-05-12Created from EPrint's last_modified field