Nickel-rich layered materials LiNi 1-x-y Mn x Co y O 2 are promising candidates for high energy density lithium-ion battery cathodes. Unfortunately, they suffer from capacity fading upon cycling, especially with high voltage charging. It is critical to have mechanistic understanding of such fade. Herein, synchrotron-based techniques (including scattering, spectroscopy, and microcopy) and finite element analysis were utilized to understand the LiNi 0.6 Mn 0.2 Co 0.2 O 2 material from structural, chemical, morphological, and mechanical points of view. The lattice structural changes are shown to be relatively reversible during cycling, even when 4.9V charging was applied. However, local disorder and strain were induced by high voltage charging. Nano-resolution 3D transmission X-ray microscopy data analyzed by machine learning methodology reveals that high-voltage charging induced significant oxidation state inhomogeneities in the cycled particles. Regions at the surface have rock-salt type structure with lower oxidation state and build up the impedance while regions with higher oxidization state are scattered in the bulk and are likely deactivated during cycling. In addition, the development of micro-cracks is highly dependent on the pristine state morphology and cycling conditions. Hollow particles seem to be more robust against stress-induced cracks than the solid ones, suggesting that morphology engineering can be effective in mitigating the crack problem in these materials. The engineering support from D. Van Campen, V. Borzenets and D. Day for the TXM experiment at beamline 6-2C of SSRL is gratefully acknowledged. The work done at Brookhaven National Laboratory was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Vehicle Technology Office of the U.S. Department of Energy through the Advanced Battery Materials Research (BMR) Program, including Battery500 Consortium under contract DE-SC0012704. This research used beamlines 7-BM and 28-ID-2 of the National Synchrotron Light Source II, a U.S.

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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.