Multiscale Materials Imaging and Spectroscopy for Battery Materials

IF 10.7 Q1 CHEMISTRY, PHYSICAL
EcoMat Pub Date : 2025-05-06 DOI:10.1002/eom2.70016
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
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引用次数: 0

Abstract

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.

Abstract Image

电池材料的多尺度材料成像与光谱学
多尺度成像和光谱学在理解电池材料的结构、化学和动态行为方面发挥着关键作用,为推动性能、寿命和安全性的进步提供了关键见解。这篇综述提供了各种成像技术的综合分析,从宏观工具,如x射线断层扫描到纳米尺度的方法,如原子力显微镜和透射电子显微镜。通过基于空间分辨率对这些技术进行分类,综述了它们在解决电池运行过程中电极退化、枝晶形成和相变等关键问题方面的应用。此外,机器学习的集成加速了数据处理,实现了多尺度关联和预测建模。该综述强调了多尺度方法优化电池性能、安全性和寿命的必要性,展示了新兴方法如何为下一代储能技术做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
17.30
自引率
0.00%
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0
审稿时长
4 weeks
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