Data-driven analysis of electrochemical impedance spectroscopy using the Loewner framework.

Area of Science:

• Electrochemistry
• Materials Science
• Data Science

Background:

• Optimizing electrochemical devices necessitates understanding mass transport and electrokinetics at electrode-electrolyte interfaces.
• Electrochemical impedance spectroscopy (EIS) is crucial for probing these processes, often analyzed with equivalent circuit models (ECMs).
• Selecting the correct ECM is difficult due to spectral similarities between different models, hindering accurate physical representation.

Purpose of the Study:

• To present a data-driven approach for extracting the distribution of relaxation times (DRTs) using the Loewner framework (LF).
• To facilitate the identification of the most suitable ECM for a given EIS dataset.
• To validate the method on common Randles ECMs and assess its performance with noisy data.

Main Methods:

• Utilized the Loewner framework (LF) for data-driven extraction of the distribution of relaxation times (DRTs).
• Applied the method to electrochemical impedance spectroscopy (EIS) datasets.
• Validated the approach using variants of Randles equivalent circuit models (ECMs).

Main Results:

• The Loewner framework (LF) successfully extracts the distribution of relaxation times (DRTs) from EIS data.
• The method aids in identifying the most appropriate equivalent circuit model (ECM) for electrochemical interfaces.
• The approach demonstrates robustness with noisy datasets and offers advantages over traditional inversion algorithms.

Conclusions:

• The data-driven DRT extraction via LF provides a reliable method for selecting appropriate ECMs in electrochemical analysis.
• This technique enhances the accuracy of physical interpretations from EIS data.
• The LF-based method offers a robust and advantageous alternative to existing ECM identification techniques.