Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data.

电化学阻抗谱 (EIS) 广泛应用于电化学、能源科学、生物学等领域。分析 EIS 数据至关重要，但由于可能的等效电路模型众多、需要精确的分析模型、非线性回归的困难以及在统一框架内管理大型数据集的必要性，分析 EIS 数据常常会带来挑战。为了克服这些挑战，非参数模型，例如弛豫时间分布（DRT，也称为弛豫时间分布函数，DFRT），已成为 EIS 分析的有前景的工具。例如，DRT 可用于生成等效电路模型、初始化回归参数、提供 EIS 谱的时域表示以及识别电化学过程。然而，掌握 DRT 方法会带来挑战，因为它需要数学和编程熟练程度，这可能超出实验人员通常的专业知识。 DRT 数据的反演后分析可能很困难，尤其是在准确识别电化学过程方面，导致结果可能并不总是符合预期。本文研究了非参数 EIS 分析方法，从理论、计算和最终用户的角度概述了它们的优点和局限性，并为其未来的发展提供了指导。此外，调查数据的见解强调需要开发大型阻抗数据库，类似于阻抗基因组。反过来，软件开发应以一键式、全自动 DRT 分析为目标，以实现多维 EIS 谱解释、软件验证和可靠性。 特别是，创建一个基于自由软件的协作生态系统可以促进创新并促进 DRT 方法在所有使用阻抗数据的领域的采用。