Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning
Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning
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DOI：
10.1021/acsaem.0c02053
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发表时间：
2020-10-26
影响因子：
6.4
通讯作者：
Hatzell, Kelsey B.
中科院分区：
文献类型：
--
作者：
Dixit, Marm B.;Verma, Ankit;Hatzell, Kelsey B.
高倍率、可逆的锂金属阳极是下一代储能系统所必需的。对Li/LLZO/Li电池进行了原位层析成像，以跟踪Li金属电极的形态变化。机器学习能够跟踪恒流循环过程中锂金属的形态。在循环过程中，锂电极在两个电极上的动力学不均匀。锂金属中的热点与LLZO中的微结构各向异性相关。中尺度模拟表明，有效性质(输运和机械性能)较低的区域是破坏的核心。先进的可视化与电化学相结合，是解决限制固态电池倍率能力的非平衡效应的重要途径。
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.