Browsing by Author "Cloetens, Peter"

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    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 (Springer Nature, 2020)
    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.