电化学阻抗谱（EIS）是电化学分析的有力工具；然而，其数据可能难以解释。在此，我们介绍一个名为 AutoEIS 的新型开源工具，它通过自动提出统计上合理的等效电路模型（ECMs）来辅助 EIS 分析。AutoEIS 无需对电化学系统有详尽的机理理解即可完成这一任务。我们通过使用 AutoEIS 分析来自三个不同电化学系统的 EIS 数据集，证明了其通用性，这些系统包括薄膜析氧反应（OER）电催化、自修复多主元合金的腐蚀以及二氧化碳还原电解槽装置。在每种情况下，AutoEIS 都识别出与专家推荐的 ECMs 相比具有竞争力或在某些情况下更优的模型，并提供了优选解的统计指标。结果表明，AutoEIS 能够在没有专家标签的情况下促进 EIS 分析，同时以高通量方式减少用户偏见。AutoEIS 提供了一种通用的自动化方法，可在对系统所需先验知识最少的情况下，促进涵盖广泛电化学应用的 EIS 分析。该工具在提高 EIS 分析的效率、准确性和便捷性方面具有巨大潜力，从而为广泛使用 EIS 加速新型电化学材料和器件的开发开辟了途径。
英文摘要：
Electrochemical Impedance Spectroscopy (EIS) is a powerful tool for electrochemical analysis; however, its data can be challenging to interpret. Here, we introduce a new open-source tool named AutoEIS that assists EIS analysis by automatically proposing statistically plausible equivalent circuit models (ECMs). AutoEIS does this without requiring an exhaustive mechanistic understanding of the electrochemical systems. We demonstrate the generalizability of AutoEIS by using it to analyze EIS datasets from three distinct electrochemical systems, including thin-film oxygen evolution reaction (OER) electrocatalysis, corrosion of self-healing multi-principal components alloys, and a carbon dioxide reduction electrolyzer device. In each case, AutoEIS identified competitive or in some cases superior ECMs to those recommended by experts and provided statistical indicators of the preferred solution. The results demonstrated AutoEIS's capability to facilitate EIS analysis without expert labels while diminishing user bias in a high-throughput manner. AutoEIS provides a generalized automated approach to facilitate EIS analysis spanning a broad suite of electrochemical applications with minimal prior knowledge of the system required. This tool holds great potential in improving the efficiency, accuracy, and ease of EIS analysis and thus creates an avenue to the widespread use of EIS in accelerating the development of new electrochemical materials and devices.
Performance Classification and Remaining Useful Life Prediction of Lithium Batteries Using Machine Learning and Early Cycle Electrochemical Impedance Spectroscopy Measurements
Molecular interpretability of the bulk electrochemical impedance of concentrated electrolytes
Yusuf Hamied Department of Chemistry, University of Cambridge（剑桥大学尤素夫·哈米德化学系）；Department of Chemistry, Durham University（杜伦大学化学系）；Sorbonne Université, CNRS, Physical Chemistry of Electrolytes and Interfacial Nanosystems (PHENIX)（索邦大学，法国国家科学研究中心，电解质和界面纳米系统物理化学（PHENIX））；Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459（电化学储能网络（RS2E），FR CNRS 3459）
The solid-state Li-ion conductor Li$_7$TaO$_6$: A combined computational and experimental study
采用第一性原理和经典分子动力学模拟方法研究了含氧六金属酸盐Li $_7$ TaO $_6$，得到了室温（300 K）下$\sim$的低扩散激活势垒0.29 eV和$5.7 \times 10^{-4}$ S cm $^{-1}$的高离子电导率.我们从计算和实验中找到了宽电化学稳定窗口的证据，表明其作为下一代固态锂离子电池中的固态电解质的可行用途。为了评估其在电化学储能系统中的适用性，我们对多晶颗粒进行了电化学阻抗谱测量，发现大量的离子电导率，如果低于模拟预测的值。我们进一步阐明合成条件和所观察到的离子电导率之间的关系，使用X射线衍射，电感耦合等离子体发射光谱，和X射线光电子能谱学，并研究Zr和Mo掺杂的影响。
Unraveling the storage mechanism of Na$_{3}$V$_{2-x}$Ni$_x$(PO$_4$)$_2$F$_3$/C cathodes for sodium-ion batteries through electrochemical, {\it operando} X-ray diffraction and microscopy studies
本研究结合 EIS、GITT、CV、operando XRD 与原位同步辐射 TXM，揭示 Ni 掺杂 Na₃V₂(PO₄)₂F₃/C 正极的储钠机制、扩散动力学及长循环稳定性。
Recycling of beta-Li3PS4-based all-solid-state Li-ion batteries: Interactions of electrode materials and electrolyte in a dissolution-based separation process
AutoREC: A reinforcement learning platform for equivalent circuit model generation
Clean Energy Innovation Research Center, National Research Council Canada（清洁能源创新研究中心，加拿大国家研究委员会）；Department of Material Science and Engineering, University of Toronto（材料科学与工程系，多伦多大学）；Cheriton School of Computer Science, University of Waterloo（计算机科学学院，滑铁卢大学）
