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

Zhisen Jiang, Jizhou Li, Yang Yang, Linqin Mu, Chenxi Wei, Xiqian Yu, Piero Pianetta, Kejie Zhao (), Peter Cloetens (), Feng Lin () and Yijin Liu ()
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Zhisen Jiang: SLAC National Accelerator Laboratory
Jizhou Li: Stanford University
Yang Yang: European Synchrotron Radiation Facility
Linqin Mu: Virginia Tech
Chenxi Wei: SLAC National Accelerator Laboratory
Xiqian Yu: Chinese Academy of Sciences
Piero Pianetta: SLAC National Accelerator Laboratory
Kejie Zhao: Purdue University
Peter Cloetens: European Synchrotron Radiation Facility
Feng Lin: Virginia Tech
Yijin Liu: SLAC National Accelerator Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-16233-5

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