离子束
离子
电池(电)
化学
材料科学
工程物理
工程类
物理
热力学
有机化学
功率(物理)
作者
S. Möller,Christian Schwab,Stefan Seidlmayer,M. Clausnitzer,M. Rosen,Jan Hormann,Markus Mann,Antonino Cannavò,G. Ceccio,J. Vacı́k,K.F. Mouzakka,Timo Danner,Arnulf Latz,Ralph Gilles,Martin Finsterbusch
标识
DOI:10.1016/j.jpowsour.2024.234681
摘要
Knowledge driven materials and component design is key for improving the performance of Li-ion batteries and solving the remaining hurdles for next-generation battery concepts like all-solid-state batteries. While the spatial and time dependent distribution of Li would help elucidating performance bottlenecks and degradation phenomena, only a few analysis techniques are available, due to the unique nature of Li, especially as Li+-ion. In fact, only two non-destructive techniques with good time resolution can combine spatial information with absolute quantification of Li, one being Neutron Depth Profiling (NDP), the other Ion-Beam-Analysis (IBA). While both exploit nuclear processes, the information gained is complementary. NDP provides high depth resolution, but only limited lateral resolution, whereas IBA has high lateral, but only limited depth resolution. In this study, we benchmark both techniques for the first time using a set of Li-battery test-samples and show the strengths and synergies of both techniques. The derived information regarding the depth dependent Li-concentration is then used to validate a microstructure resolved continuum model of charge, discharge, and relaxation behavior of the cells and electro-chemical analysis. This fundamental work demonstrates a new route to optimize Li batteries on material and component level by combining advanced characterization and digital twin modelling.
科研通智能强力驱动
Strongly Powered by AbleSci AI