(Keynote) On Interpretation of Impedance Spectroscopy Data

Abstract

Impedance spectroscopy represents a rich area of science that has been applied to many research disciplines, including those associated with corrosion and corrosion control, energy systems, and biological systems. In laboratory settings, impedance has been used to study the kinetics and mechanisms of electrochemical reactions. Impedance spectroscopy has also been applied in industrial applications to quality control and corrosion monitoring and constitutes the basis of a class of sensors [1].Impedance spectra are easily measured by used of modern instrumentation. The difficulty lies in interpretation of impedance spectra in terms of physically meaningful information. While graphical methods [2], distribution of relaxation times analyses [3], and measurement model analyses [4] can guide model development, interpretation is generally based on use of a deterministic model that describes the physics of the system under study [5]. Such models may be coupled with electrical circuits that account for the double layer formed at the electrified interface and for the electrolyte resistance. The deterministic model approach is often perceived as being much more complicated than use of electrical circuits alone because it requires mathematical expertise.In general, interpretation of the impedance response requires both a quantitative evaluation of the error structure of the impedance measurement and insight into the physics and chemistry of the system under study. Analysis of the error structure yields a standard deviation for the stochastic errors, which can be used to weight subsequent regressions. The error analysis also identifies the range of frequencies that are consistent with the Kramers–Kronig relations and are, therefore, suitable for regression analysis. The physical insight is used to guide the interpretation model development. It should be noted that interpretation models are not unique. The models may suggest other experiments which can be used to reject the proposed model. Greater confidence may be assigned to a model that explains a variety of experimental observations.The objective of this paper is to review our work on interpretation of impedance data, including the identification and quantification of the error structure associated with impedance spectroscopy measurements. The presentation makes use of a program that allows users to access the power of the measurement model concept and to fit custom process models [6].References S. Wang, J. Zhang, O. Gharbi, V. Vivier, M. Gao, and M. E. Orazem, “Electrochemical Impedance Spectroscopy,” Nat. Rev. Methods Primers, 1, 41 (2021), 1-21.M. E. Orazem, and N. Pébère, and B. Tribollet, “Enhanced Graphical Representation of Electrochemical Impedance Data,” J. Electrochem.Soc., 153 (2006), B129-B136.B. A. Boukamp, “Distribution (Function) of Relaxation Times, Successor to Complex Nonlinear Least Squares Analysis of Electrochemical Impedance Spectroscopy?” J. Phys. Energy, 4 (2020), 042001.H. Liao, W. Watson, A. Dizon, B. Tribollet, V. Vivier, and M. E. Orazem, “Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements,” Electrochim. Acta, 354 (2020), 136747.V. Vivier and M. E. Orazem, “Impedance Analysis of Electrochemical Systems,” Chem. Rev., 122 (2022), 11131-11168.W. Watson and M. E. Orazem, EIS: Measurement Model Program, ECSArXiv, 2020, https://ecsarxiv.org/kze9x/.