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A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

preprint
revised on 05.05.2020 and posted on 06.05.2020 by Jiapeng Liu, Ting Hei Wan, Francesco Ciucci

Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.

Funding

Ba segregation in La-substituted BaFeO3: in-situ and ex-situ characterization and computational modeling.

University Grants Committee

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Chemically and electrochemically driven nanoparticle egress from A-site-deficient perovskites: A computational and experimental study of nanoparticle growth

University Grants Committee

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Non-Flammable Solid-State Rechargeable Li-ion Batteries for Rescue and Public Safety Applications

Innovation and Technology Commission

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History

Email Address of Submitting Author

jliubt@connect.ust.hk

Institution

Hong Kong University of Science and Technology

Country

Hong Kong

ORCID For Submitting Author

0000-0001-8667-1929

Declaration of Conflict of Interest

We declare no conflict of interest

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