Artificial generation of representative single Li-ion electrode particle architectures from microscopy data

Abstract Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information drawn from multimodal microscopy techniques to artificially generate LiNi 0.5 Mn 0.3 Co 0.2 O 2 with full sub-particle grain detail. Statistical representations particle architectures are derived X-ray nano-computed tomography data supporting an ‘outer shell’ model, focused-ion beam electron backscatter diffraction a ‘grain’ model. A random field model used characterize outer shells, tessellation architectures, combined form multi-scale generation virtual full-grain This work demonstrates possibility generating representative modeling characterization that can guide synthesis approaches enhanced performance.