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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-05-08

AUTHORS

Zhisen Jiang, Jizhou Li, Yang Yang, Linqin Mu, Chenxi Wei, Xiqian Yu, Piero Pianetta, Kejie Zhao, Peter Cloetens, Feng Lin, Yijin Liu

ABSTRACT

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. More... »

PAGES

2310

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-020-16233-5

DOI

http://dx.doi.org/10.1038/s41467-020-16233-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1127455499

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/32385347


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