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

Nature communications, 2020-05, Vol.11 (1), p.2310-2310, Article 2310 [Peer Reviewed Journal]

This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Distributed under a Creative Commons Attribution 4.0 International License ;This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-16233-5 ;PMID: 32385347

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Title:
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
Author: Jiang, Zhisen ; Li, Jizhou ; Yang, Yang ; Mu, Linqin ; Wei, Chenxi ; Yu, Xiqian ; Pianetta, Piero ; Zhao, Kejie ; Cloetens, Peter ; Lin, Feng ; Liu, Yijin
Subjects: Battery ; Binders (materials) ; Cathodes ; Charged particles ; Charging ; Confidence intervals ; Electrochemistry ; Electrodes ; Electron density ; ENERGY STORAGE ; Evolution ; Learning algorithms ; Lithium ; Lithium-ion batteries ; Machine learning ; MATERIALS SCIENCE ; Mathematical analysis ; Mathematical models ; Matrix methods ; Microstructure ; Multiscale analysis ; Nickel ; Particulate composites ; Physics ; Rechargeable batteries ; Statistical analysis ; Statistics
Is Part Of: Nature communications, 2020-05, Vol.11 (1), p.2310-2310, Article 2310
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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 2041-1723
EISSN: 2041-1723
DOI: 10.1038/s41467-020-16233-5
PMID: 32385347
Source: Hyper Article en Ligne (HAL) (Open Access)
PubMed Central
ProQuest Central
DOAJ Directory of Open Access Journals

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

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