Probing Particle-Carbon/Binder Degradation Behavior in Fatigued Layered Cathode Materials through Machine Learning Aided Diffraction Tomography.

Understanding how reaction heterogeneity impacts cathode materials during Li-ion battery (LIB) electrochemical cycling is pivotal for unraveling their electrochemical performance. Yet, experimentally verifying these reactions has proven to be a challenge. To address this, we employed scanning μ-XRD computed tomography to scrutinize Ni-rich layered LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) and Li-rich layered Li[Li 0.2 Ni 0.2 Mn 0.6 ]O 2 (LLNMO). By harnessing machine learning (ML) techniques, we scrutinized an extensive dataset of μ-XRD patterns, about 100,000 patterns per slice, to unveil the spatial distribution of crystalline structure and microstrain. Our experimental findings unequivocally reveal the distinct behavior of these materials. NCM622 exhibits structural degradation and lattice strain intricately linked to the size of secondary particles. Smaller particles and the surface of larger particles in contact with the carbon/binder matrix experience intensified structural fatigue after long-term cycling. Conversely, both the surface and bulk of LLNMO particles endure severe strain-induced structural degradation during high-voltage cycling, resulting in significant voltage decay and capacity fade. This work holds the potential to fine-tune the microstructure of advanced layered materials and manipulate composite electrode construction in order to enhance the performance of LIBs and beyond.