Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning

被引：90

作者：

Dixit, Marm B. ^{[1
]}

Verma, Ankit ^{[2
]}

Zaman, Wahid ^{[1
]}

Zhong, Xinlin ^{[1
]}

Kenesei, Peter ^{[3
]}

Park, Jun Sang ^{[3
]}

Almer, Jonathan ^{[3
]}

Mukherjee, Partha P. ^{[2
]}

Hatzell, Kelsey B. ^{[4
,5
]}

机构：

[1] Vanderbilt Univ, Dept Mech Engn, Nashville, TN 37240 USA

[2] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47907 USA

[3] Argonne Natl Lab, Xray Sci Div, Adv Photon Source, Lemont, IL 60439 USA

[4] Vanderbilt Univ, Dept Mech Engn Chem & Biomol Engn, Nashville, TN 37240 USA

[5] Vanderbilt Univ, Interdisciplinary Mat Sci Program, Nashville, TN 37240 USA

来源：

ACS APPLIED ENERGY MATERIALS | 2020年 / 3卷 / 10期

基金：

美国国家科学基金会;

关键词：

solid electrolytes; LLZO; tomography; solid-state battery; lithium metal; machine learning; ELECTROLYTES; PROPAGATION; MICROSTRUCTURE; MORPHOLOGY; INTERFACE; KINETICS; GROWTH;

D O I：

10.1021/acsaem.0c02053

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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.

引用

页码：9534 / 9542

页数：9

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