Jiang Zhisen, Li Jizhou, Yang Yang, Mu Linqin, Wei Chenxi, Yu Xiqian, Pianetta Piero, Zhao Kejie, Cloetens Peter, Lin Feng, Liu Yijin
Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94305, USA.
Nat Commun. 2020 May 8;11(1):2310. doi: 10.1038/s41467-020-16233-5.
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.
复合电极的微观结构决定了锂离子电池中单个电池颗粒的充电和放电方式。通过实验可视化并随后理解电池颗粒与导电基体不断演变的（脱）附着所产生的电化学后果，是一个前沿挑战。在此，我们采用多尺度实验方法、机器学习辅助统计分析和基于实验的数学建模的独特组合来解决这个问题。我们的结果表明，颗粒脱离程度与充电速率呈正相关，并且较小的颗粒从碳/粘结剂基体脱离时表现出更高的不确定性。我们进一步探讨了利用重建的电子密度作为充电状态替代指标的可行性和局限性。我们的研究结果强调了以统计置信度精确量化电池电极微观结构不断演变的性质的重要性，这是最大限度提高活性颗粒对更高电池容量效用的关键。
