Electrochemical impedance spectroscopy (EIS), as a non-invasive and non-destructive diagnostic technique, has shown unique advantages and significant potential in lithium-ion battery state monitoring. However, its traditional steady-state methods face substantial limitations under the non-stationary operating conditions commonly encountered in practical applications. To overcome these challenges, dynamic electrochemical impedance spectroscopy (DEIS) has emerged as a critical tool due to its real-time monitoring capabilities. This review provides a comprehensive overview of recent advances in DEIS for lithium-ion battery state monitoring, starting with an in-depth explanation of its working principles and a comparison with conventional EIS to highlight their respective advantages. Analytical methodologies for EIS are then introduced to establish a theoretical foundation for the discussion of subsequent findings. The review emphasizes recent breakthroughs achieved using DEIS, particularly in elucidating charge transfer dynamics during charge–discharge cycles, detecting lithium plating at the anode, and monitoring internal temperature variations within batteries. It further explores the potential of DEIS in battery health prediction, demonstrating its role in enhancing the accuracy and reliability of battery management systems. Finally, the review concludes with a forward-looking perspective on the future development of DEIS, underscoring its transformative potential in advancing battery diagnostics and management technologies.
{"title":"Dynamic Electrochemical Impedance Spectroscopy: A Forward Application Approach for Lithium-Ion Battery Status Assessment","authors":"Xinyi Zhang, Yunpei Lu, Jingfu Shi, Yuezheng Liu, Hao Cheng, Yingying Lu","doi":"10.1002/eom2.70018","DOIUrl":"https://doi.org/10.1002/eom2.70018","url":null,"abstract":"<p>Electrochemical impedance spectroscopy (EIS), as a non-invasive and non-destructive diagnostic technique, has shown unique advantages and significant potential in lithium-ion battery state monitoring. However, its traditional steady-state methods face substantial limitations under the non-stationary operating conditions commonly encountered in practical applications. To overcome these challenges, dynamic electrochemical impedance spectroscopy (DEIS) has emerged as a critical tool due to its real-time monitoring capabilities. This review provides a comprehensive overview of recent advances in DEIS for lithium-ion battery state monitoring, starting with an in-depth explanation of its working principles and a comparison with conventional EIS to highlight their respective advantages. Analytical methodologies for EIS are then introduced to establish a theoretical foundation for the discussion of subsequent findings. The review emphasizes recent breakthroughs achieved using DEIS, particularly in elucidating charge transfer dynamics during charge–discharge cycles, detecting lithium plating at the anode, and monitoring internal temperature variations within batteries. It further explores the potential of DEIS in battery health prediction, demonstrating its role in enhancing the accuracy and reliability of battery management systems. Finally, the review concludes with a forward-looking perspective on the future development of DEIS, underscoring its transformative potential in advancing battery diagnostics and management technologies.</p>","PeriodicalId":93174,"journal":{"name":"EcoMat","volume":"7 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eom2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weibin Zhang, Man Kwan Law, Muhammad Bilal Asif, Jinglei Yang
Transparent solar heat control (TSHC) coatings for windows have garnered significant attention as a key technology for passive cooling in green buildings to reduce energy consumption. However, many studies have focused only on TSHC coatings composed of single functional nanoparticles, and the development of these coatings traditionally relied on trial-and-error methods. Herein, we propose a real experimental data-driven tandem neural networks (NNs) model, comprising spectrum NNs and inverse design NNs, for the combinatorial innovation, development, and optimization of TSHC coatings. Attributed to the high quality of the data, the resulting well-trained tandem NNs with an R2 value above 0.95 facilitate the rapid development and precise inverse design of TSHC coatings with multiple functional nanoparticles. The developed coating, composed of cesium tungsten oxide (CWO), antimony tin oxide (ATO), and indium tin oxide (ITO) nanoparticles, achieves a luminous transmittance of 69%, UV transmittance of 0.1%, and NIR transmittance of 4%. The calculated solar heat gain coefficient (SHGC) and light-to-solar gain (LSG) ratio are 0.49 and 1.41, respectively. Temperature reduction tests using a house simulant revealed that the developed TSHC coating can reduce indoor temperatures by up to 8°C. Furthermore, innovative application methods, including spray coating and solution-processed film techniques, have been explored to apply the TSHC coating to large glass surfaces. Our work provides a novel strategy to efficiently develop and optimize the optical properties of coatings with multiple functional compositions.
{"title":"Combinatorial Data-Driven Innovation of Ecofriendly Transparent Solar Heat Control Coating for Green Buildings","authors":"Weibin Zhang, Man Kwan Law, Muhammad Bilal Asif, Jinglei Yang","doi":"10.1002/eom2.70017","DOIUrl":"https://doi.org/10.1002/eom2.70017","url":null,"abstract":"<p>Transparent solar heat control (TSHC) coatings for windows have garnered significant attention as a key technology for passive cooling in green buildings to reduce energy consumption. However, many studies have focused only on TSHC coatings composed of single functional nanoparticles, and the development of these coatings traditionally relied on trial-and-error methods. Herein, we propose a real experimental data-driven tandem neural networks (NNs) model, comprising spectrum NNs and inverse design NNs, for the combinatorial innovation, development, and optimization of TSHC coatings. Attributed to the high quality of the data, the resulting well-trained tandem NNs with an R<sup>2</sup> value above 0.95 facilitate the rapid development and precise inverse design of TSHC coatings with multiple functional nanoparticles. The developed coating, composed of cesium tungsten oxide (CWO), antimony tin oxide (ATO), and indium tin oxide (ITO) nanoparticles, achieves a luminous transmittance of 69%, UV transmittance of 0.1%, and NIR transmittance of 4%. The calculated solar heat gain coefficient (SHGC) and light-to-solar gain (LSG) ratio are 0.49 and 1.41, respectively. Temperature reduction tests using a house simulant revealed that the developed TSHC coating can reduce indoor temperatures by up to 8°C. Furthermore, innovative application methods, including spray coating and solution-processed film techniques, have been explored to apply the TSHC coating to large glass surfaces. Our work provides a novel strategy to efficiently develop and optimize the optical properties of coatings with multiple functional compositions.</p>","PeriodicalId":93174,"journal":{"name":"EcoMat","volume":"7 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eom2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Youngwoo Choi, Gumin Kang, Seonghyun Kim, Yoonhan Cho, Jaewhan Oh, Dongho Kim, Jacob Choe, Jong Min Yuk, Pyuck-Pa Choi, Yongsoo Yang, Sung-Yoon Chung, Chi Won Ahn, Jongwoo Lim, Seungbum Hong
Multiscale imaging and spectroscopy play a pivotal role in understanding the structural, chemical, and dynamic behavior of battery materials, providing critical insights that drive advancements in performance, longevity, and safety. This review provides a comprehensive analysis of various imaging techniques, from macroscopic tools like x-ray tomography to nanoscale methods such as atomic force microscopy and transmission electron microscopy. By categorizing these techniques based on spatial resolution, the review highlights their applications in resolving key issues like electrode degradation, dendrite formation, and phase transitions during battery operation. Moreover, the integration of machine learning accelerates data processing, enabling multiscale correlations and predictive modeling. The review underscores the necessity of multiscale approaches to optimize battery performance, safety, and lifespan, showcasing how emerging methodologies contribute to next-generation energy storage technologies.