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

Author

Listed:
  • Zhisen Jiang

    (SLAC National Accelerator Laboratory)

  • Jizhou Li

    (Stanford University)

  • Yang Yang

    (European Synchrotron Radiation Facility
    Brookhaven National Laboratory)

  • 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)

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.

Suggested Citation

  • Zhisen Jiang & Jizhou Li & Yang Yang & Linqin Mu & Chenxi Wei & Xiqian Yu & Piero Pianetta & Kejie Zhao & Peter Cloetens & Feng Lin & Yijin Liu, 2020. "Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16233-5
    DOI: 10.1038/s41467-020-16233-5
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    Cited by:

    1. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    2. Zhichen Xue & Nikhil Sharma & Feixiang Wu & Piero Pianetta & Feng Lin & Luxi Li & Kejie Zhao & Yijin Liu, 2023. "Asynchronous domain dynamics and equilibration in layered oxide battery cathode," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    3. Simon Müller & Christina Sauter & Ramesh Shunmugasundaram & Nils Wenzler & Vincent Andrade & Francesco Carlo & Ender Konukoglu & Vanessa Wood, 2021. "Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    4. Quansheng Ge & Mengmeng Hao & Fangyu Ding & Dong Jiang & Jürgen Scheffran & David Helman & Tobias Ide, 2022. "Modelling armed conflict risk under climate change with machine learning and time-series data," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    5. Samuel-Soma Ajibade & Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Anthonia Oluwatosin Adediran & Liton Chandra Voumik & Fazle Rabbi, 2023. "New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 303-314, September.

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