期刊
NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-16233-5
关键词
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资金
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-76SF00515]
- National Science Foundation [DMR-1832613, CBET-1912885, CMMI-1726392, DMR-1832707]
- DOE Vehicle Technologies Program (VTP) within the Applied Battery Research (ABR) for Transportation Program
- National Key RAMP
- D Program of China [2016YFB0100100]
- National Natural Science Foundation of China [51822211, 11574281]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [51421002]
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. Developing understanding of degradation phenomena in nickel rich cathodes is under intense investigation. Here the authors use learning-assisted statistical analysis and experiment-informed mathematical modelling to resolve the microstructure of a Ni-rich NMC composite cathode.
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