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AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy

Published

Author(s)

Runze Zhang, Robert Black, Debashish Sur, Parisa Karimi, Kangming Li, Brian DeCost, John Scully, Jason Hattrick-Simpers

Abstract

Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-source tool named AutoEIS that assists EIS analysis by automatically proposing statistically plausible equivalent circuit models (ECMs). AutoEIS does this without requiring an exhaustive mechanistic understanding of the electrochemical systems. We demonstrate the generalizability of AutoEIS by using it to analyze EIS datasets from three distinct electrochemical systems, including thin-film oxygen evolution reaction (OER) electrocatalysis, corrosion of self-healing multi-principal components alloys, and a carbon dioxide reduction electrolyzer device. In each case, AutoEIS identified alternative ECMs to those recommended by experts and provided statistical indicators of the preferred solution. The results demonstrated AutoEIS's capability to facilitate EIS analysis without expert labels while diminishing user bias in a high-throughput manner. AutoEIS provides a generalized automated approach to facilitate EIS analysis spanning a broad suite of electrochemical applications with minimal prior knowledge of the system required. It has great potential in improving the efficiency, accuracy, and ease of EIS analysis and thus creates an avenue to the generalized use of EIS in accelerating the development of new electrochemical materials and devices.
Citation
(potentially a different journal, still TBD)

Citation

Zhang, R. , Black, R. , Sur, D. , Karimi, P. , LI, K. , DeCost, B. , Scully, J. and Hattrick-Simpers, J. (2023), AutoEIS: automated Bayesian model selection and analysis for electrochemical impedance spectroscopy, (potentially a different journal, still TBD), [online], https://doi.org/10.1149/1945-7111/aceab2, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936800 (Accessed June 19, 2024)

Issues

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Created August 9, 2023, Updated November 10, 2023