Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms
© 2020 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. We introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely...
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| Format: | Article |
| Language: | English |
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The Electrochemical Society
2021
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| Online Access: | https://hdl.handle.net/1721.1/133555 |
| _version_ | 1826193485523320832 |
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| author | Effendy, Surya Song, Juhyun Bazant, Martin Z |
| author_facet | Effendy, Surya Song, Juhyun Bazant, Martin Z |
| author_sort | Effendy, Surya |
| collection | MIT |
| description | © 2020 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. We introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely (1) the representation of unknown distributions, (2) the minimization of a metric of error to estimate parameters arising from the chosen representation, subject to constraints on (3) the complexity control parameters, and (4) a means for choosing optimal control parameter values. These features must be present to overcome the ill-posed nature of EIS inversion problems. We review three established EIS inversion algorithms to illustrate the pervasiveness of these features, and show the utility of the framework by resolving ambiguities concerning three more algorithms. Our framework is then used to design the generalized EIS inversion (gEISi) algorithm, which uses Gaussian basis function representation, modality control parameter, and cross-validation for choosing the optimal control parameter value. The gEISi algorithm is applicable to the generalized EIS inversion problem, which allows for a wider range of underlying models. We also considered the construction of credible intervals for distributions arising from the algorithm. The algorithm is able to accurately reproduce distributions which have been difficult to obtain using existing algorithms. It is provided gratis on the repository https://github.com/suryaeff/gEISi.git. |
| first_indexed | 2024-09-23T09:39:56Z |
| format | Article |
| id | mit-1721.1/133555 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:39:56Z |
| publishDate | 2021 |
| publisher | The Electrochemical Society |
| record_format | dspace |
| spelling | mit-1721.1/1335552021-10-28T04:42:15Z Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms Effendy, Surya Song, Juhyun Bazant, Martin Z © 2020 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. We introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely (1) the representation of unknown distributions, (2) the minimization of a metric of error to estimate parameters arising from the chosen representation, subject to constraints on (3) the complexity control parameters, and (4) a means for choosing optimal control parameter values. These features must be present to overcome the ill-posed nature of EIS inversion problems. We review three established EIS inversion algorithms to illustrate the pervasiveness of these features, and show the utility of the framework by resolving ambiguities concerning three more algorithms. Our framework is then used to design the generalized EIS inversion (gEISi) algorithm, which uses Gaussian basis function representation, modality control parameter, and cross-validation for choosing the optimal control parameter value. The gEISi algorithm is applicable to the generalized EIS inversion problem, which allows for a wider range of underlying models. We also considered the construction of credible intervals for distributions arising from the algorithm. The algorithm is able to accurately reproduce distributions which have been difficult to obtain using existing algorithms. It is provided gratis on the repository https://github.com/suryaeff/gEISi.git. 2021-10-27T19:53:32Z 2021-10-27T19:53:32Z 2020 2021-06-07T16:29:36Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133555 en 10.1149/1945-7111/AB9C82 Journal of the Electrochemical Society Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf The Electrochemical Society MIT web domain |
| spellingShingle | Effendy, Surya Song, Juhyun Bazant, Martin Z Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms |
| title | Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms |
| title_full | Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms |
| title_fullStr | Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms |
| title_full_unstemmed | Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms |
| title_short | Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms |
| title_sort | analysis design and generalization of electrochemical impedance spectroscopy eis inversion algorithms |
| url | https://hdl.handle.net/1721.1/133555 |
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