| Download | - View final version: Bayesian assessment of commonly used equivalent circuit models for corrosion analysis in electrochemical impedance spectroscopy (PDF, 1.7 MiB)
- View supplementary information: Bayesian assessment of commonly used equivalent circuit models for corrosion analysis in electrochemical impedance spectroscopy (PDF, 2.1 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1038/s41529-024-00537-8 |
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| Author | Search for: Zhang, Runze; Search for: Sur, Debashish; Search for: Li, Kangming; Search for: Witt, Julia; Search for: Black, Robert1ORCID identifier: https://orcid.org/0009-0004-3383-0307; Search for: Whittingham, Alexander1ORCID identifier: https://orcid.org/0000-0001-9752-6356; Search for: Scully, John R.; Search for: Hattrick-Simpers, Jason |
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| Affiliation | - National Research Council Canada. Clean Energy Innovation
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| Funder | Search for: European Union; Search for: University of Toronto; Search for: National Research Council Canada; Search for: Office of Naval Research |
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| Format | Text, Article |
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| Subject | characterization and analytical techniques; corrosion |
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| Abstract | Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the system under study. In this work, we systematically examined the applicability of three commonly used ECMs across several typical material degradation scenarios. By applying Bayesian Inference to simulated corrosion EIS data, we assessed the suitability of these ECMs under different corrosion conditions and identified regions where the EIS data lacks sufficient information to statistically substantiate the ECM structure. Additionally, we posit that the traditional approach to EIS analysis, which often requires measurements to very low frequencies, might not be always necessary to correctly model the appropriate ECM. Our study assesses the impact of omitting data from low to medium-frequency ranges on inference results and reveals that a significant portion of low-frequency measurements can be excluded without substantially compromising the accuracy of extracting system parameters. Further, we propose simple checks to the posterior distributions of the ECM components and posterior predictions, which can be used to quantitatively evaluate the suitability of a particular ECM and the minimum frequency required to be measured. This framework points to a pathway for expediting EIS acquisition by intelligently reducing low-frequency data collection and permitting on-the-fly EIS measurements. |
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| Publication date | 2024-11-28 |
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| Publisher | Springer Nature |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 6aadc21c-40fb-48c8-9fe6-7302ee482d45 |
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| Record created | 2025-04-02 |
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| Record modified | 2025-11-03 |
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