Synchrotron imaging of pore formation in Li metal solid-state batteries aided by machine learning

Marm B. Dixit; Ankit Verma; Wahid Zaman; Xinlin Zhong; Peter Kenesei; Jun Sang Park; Jonathan Almer; Partha P. Mukherjee; Kelsey B. Hatzell

doi:10.1021/acsaem.0c02053

Synchrotron imaging of pore formation in Li metal solid-state batteries aided by machine learning

Marm B. Dixit, Ankit Verma, Wahid Zaman, Xinlin Zhong, Peter Kenesei, Jun Sang Park, Jonathan Almer, Partha P. Mukherjee, Kelsey B. Hatzell

Research output: Contribution to journal › Article › peer-review

88 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	9534-9542
Number of pages	9
Journal	ACS Applied Energy Materials
Volume	3
Issue number	10
DOIs	https://doi.org/10.1021/acsaem.0c02053
State	Published - Oct 26 2020
Externally published	Yes

Funding

The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan . Acknowledgments K.B.H. and M.B.D. acknowledge support from National Science Foundation Grant 1847029. K.B.H. and W.Z. acknowledge support from from National Science Foundation Grant 1821573. P.P.M. and A.V. acknowledge financial support in part from a Scialog program sponsored jointly by Research Corporation for Science Advancement and the Alfred P. Sloan Foundation which includes a grant to Purdue University by the Alfred P. Sloan Foundation. The authors acknowledge the Vanderbilt Institute of Nanoscience and Engineering (VINSE) for access to their shared characterization facilities. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract DE-AC02-06CH11357.

Funders	Funder number
Vanderbilt Institute of Nanoscience and Engineering
National Science Foundation	1821573, 1847029
U.S. Department of Energy
Alfred P. Sloan Foundation
Research Corporation for Science Advancement
Office of Science
Argonne National Laboratory	DE-AC02-06CH11357
Purdue University

Keywords

LLZO
Lithium metal
Machine learning
Solid electrolytes
Solid-state battery
Tomography

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1021/acsaem.0c02053

Cite this

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abstract = "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.",

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AB - 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.

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KW - Tomography

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Synchrotron imaging of pore formation in Li metal solid-state batteries aided by machine learning

Abstract

Funding

Keywords

UN SDGs

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