Electrochemical Impedance Spectroscopy (EIS) is a commonly employed characterization technique for studying electrochemical systems like batteries and fuel cells [1,2,3]. The distribution of relaxation times (DRT) has become a versatile alternative to equivalent circuits and physical models in EIS data analysis [1-8]. Despite its versatility, deconvolving the DRT from EIS spectra poses challenges, leading to the development of various approaches for this purpose [2-11]. In order to advance the field of DRT-based EIS analysis, we conducted a bilingual survey (English and Chinese) to gather perspectives from experts, shedding light on its current usage and outlining several opportunities for future directions to expand the DRT field. Informed by our survey findings, we identified specific challenges related to DRT-based EIS data analysis and interpretations, proposing solutions that significantly enhance the DRT domain and expand its application in impedance analysis.
In summary, our survey has helped lay a future roadmap for developing the DRT field by highlighting the need for automated DRT deconvolution and the automatic association of peaks with processes. This advanced software tool would surpass existing software tools, each with its strengths and limitations (as discussed in Section 4 of the main article).
Starting with EIS data, the DRT is deconvolved, which results in the identification of timescales, subsequent analysis allows the identification of the corresponding electrochemical processes.
For more details about the survey, you can check the supplementary information in docs
[1] Ciucci, F. (2018). Modeling electrochemical impedance spectroscopy. Current Opinion in Electrochemistry.132-139 https://doi.org/10.1016/j.coelec.2018.12.003
[2] Wan, T. H., Saccoccio, M., Chen, C., & Ciucci, F. (2015). Influence of the discretization methods on the distribution of relaxation times deconvolution: implementing radial basis functions with DRTtools. Electrochimica Acta, 184, 483-499. https://doi.org/10.1016/j.electacta.2015.09.097
[3] Saccoccio, M., Wan, T. H., Chen, C., & Ciucci, F. (2014). Optimal regularization in distribution of relaxation times applied to electrochemical impedance spectroscopy: ridge and lasso regression methods-a theoretical and experimental study. Electrochimica Acta, 147, 470-482. https://doi.org/10.1016/j.electacta.2014.09.058
[4] Liu, J., & Ciucci, F. (2020). The Gaussian process distribution of relaxation times: A machine learning tool for the analysis and prediction of electrochemical impedance spectroscopy data. Electrochimica Acta, 135316. https://doi.org/10.1016/j.electacta.2019.135316.
[5] Effat, M. B., & Ciucci, F. (2017). Bayesian and hierarchical Bayesian based regularization for deconvolving the distribution of relaxation times from electrochemical impedance spectroscopy data. Electrochimica Acta, 247, 1117-1129. https://doi.org/10.1016/j.electacta.2017.07.050.
[6] Maradesa, A., Py, B., Quattrocchi, E., & Ciucci, F. (2022). The probabilistic deconvolution of the distribution of relaxation times with finite Gaussian processes. Electrochimica Acta, 413, 140119. https://doi.org/10.1016/j.electacta.2022.140119.
[7] Py, B., Maradesa, A., & Ciucci, F. (2023). Gaussian processes for the analysis of electrochemical impedance spectroscopy data: Prediction, filtering, and active learning. Electrochimica Acta, 439, 141688. [doi.org/10.1016/j.electacta.2022.141688] (https://doi.org/10.1016/j.electacta.2022.141688)
[8] Ciucci, F., & Chen, C. (2015). Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A Bayesian and hierarchical Bayesian approach. Electrochimica Acta, 167, 439-454. https://doi.org/10.1016/j.electacta.2015.03.123
[9] Liu, J., Ciucci, F., The Deep-Prior Distribution of Relaxation Times Journal of The Electrochemical Society, 167.2 (2020): 026506 https://doi.org/10.1149/1945-7111/ab631a
[10] E. Quattrocchi, T.H. Wan, A. Belotti, D. Kim, S. Simona, S.V. Kalinin, M. Ahmadi, F. Ciucci, The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data. Electrocimica Acta, 392 (2021) 139010.doi.org/10.1016/j.electacta.2021.139010
[11] E. Quattrocchi, B. Py, A. Maradesa, Q. Meyer, C. Zhao, F. Ciucci, Deconvolution of electrochemical impedance spectroscopy data using the deep-neural-network-enhanced distribution of relaxation times. Electrocimica Acta, 439 (2023) 141499.https://doi.org/10.1016/j.electacta.2022.141499