Probing lattice defects in crystalline battery cathode using hard X-ray nanoprobe with data-driven modeling

Lattice defects, e.g., dislocations and grain boundaries, critically impact the properties of crystalline battery cathode materials. A longstanding challenge is to probe the meso -scale heterogeneity and evolution of lattice defects with sensitivity to atomic-scale details. Herein, we tackle this issue with a unique combination of X-ray nanoprobe diffractive imaging and advanced machine learning techniques. The domains with different lattice defect configuration within a single-crystalline LiCoO(2 )cathode particle are faithfully revealed using our approach. We further visualize the rearrangement of grain boundaries and local crystallinity upon mild thermal annealing. These results pave a direct way to the understanding of crystalline battery materials' response under external stimuli with high fidelity, which provides valuable empirical guidance to defect-engineering strategies for improving the cathode materials against aggressive battery operation.

Automated summary

In this study, a unique combination of X-ray nanoprobe diffractive imaging and advanced machine learning techniques is used to reveal the lattice defects and grain boundaries in a single-crystalline lithium-ion battery cathode material. The results show the rearrangement of the grain boundaries and local crystallinity upon mild thermal annealing, providing valuable empirical guidance for defect-engineering strategies to improve the cathode materials.