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Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes

The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)att...

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Autores principales: Jiang, Zhisen, Li, Jizhou, Yang, Yang, Mu, Linqin, Wei, Chenxi, Yu, Xiqian, Pianetta, Piero, Zhao, Kejie, Cloetens, Peter, Lin, Feng, Liu, Yijin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7210251/
https://www.ncbi.nlm.nih.gov/pubmed/32385347
http://dx.doi.org/10.1038/s41467-020-16233-5
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author Jiang, Zhisen
Li, Jizhou
Yang, Yang
Mu, Linqin
Wei, Chenxi
Yu, Xiqian
Pianetta, Piero
Zhao, Kejie
Cloetens, Peter
Lin, Feng
Liu, Yijin
author_facet Jiang, Zhisen
Li, Jizhou
Yang, Yang
Mu, Linqin
Wei, Chenxi
Yu, Xiqian
Pianetta, Piero
Zhao, Kejie
Cloetens, Peter
Lin, Feng
Liu, Yijin
author_sort Jiang, Zhisen
collection PubMed
description The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity.
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spelling pubmed-72102512020-05-13 Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes Jiang, Zhisen Li, Jizhou Yang, Yang Mu, Linqin Wei, Chenxi Yu, Xiqian Pianetta, Piero Zhao, Kejie Cloetens, Peter Lin, Feng Liu, Yijin Nat Commun Article The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity. Nature Publishing Group UK 2020-05-08 /pmc/articles/PMC7210251/ /pubmed/32385347 http://dx.doi.org/10.1038/s41467-020-16233-5 Text en © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Jiang, Zhisen
Li, Jizhou
Yang, Yang
Mu, Linqin
Wei, Chenxi
Yu, Xiqian
Pianetta, Piero
Zhao, Kejie
Cloetens, Peter
Lin, Feng
Liu, Yijin
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
title Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
title_full Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
title_fullStr Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
title_full_unstemmed Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
title_short Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
title_sort machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7210251/
https://www.ncbi.nlm.nih.gov/pubmed/32385347
http://dx.doi.org/10.1038/s41467-020-16233-5
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