期刊
ACS APPLIED ENERGY MATERIALS
卷 3, 期 10, 页码 9534-9542出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsaem.0c02053
关键词
solid electrolytes; LLZO; tomography; solid-state battery; lithium metal; machine learning
资金
- National Science Foundation [1847029, 1821573]
- Scialog program - Research Corporation for Science Advancement
- Scialog program - Alfred P. Sloan Foundation
- DOE Office of Science [DE-AC02-06CH11357]
- Alfred P. Sloan Foundation
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [1821573] Funding Source: National Science Foundation
High-rate capable, reversible lithium metal anodes are necessary for next generation energy storage systems. In situ tomography of Li/LLZO/Li cells is carried out to track morphological transformations in Li metal electrodes. Machine learning enables tracking the lithium metal morphology during galvanostatic cycling. Nonuniform lithium electrode kinetics are observed at both electrodes during cycling. Hot spots in lithium metal are correlated with microstructural anisotropy in LLZO. Mesoscale modeling reveals that regions with lower effective properties (transport and mechanical) are nuclei for failure. Advanced visualization combined with electrochemistry represents an important pathway toward resolving non-equilibrium effects that limit rate capabilities of solid-state batteries.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据