Quantitative image-analysis framework for precise discrimination of cation mixing in high-nickel NCM cathodes

Abstract

Quantitative assessment of Li/Ni mixing phenomena in high-nickel layered oxide cathode materials for lithiuim-ion batteries (LIBs) remain constrained by subjective visual interpretation limiting reproducibility and statistical rigor in atomic-scale characterization. Systematic image processing methodology incorporating Gaussian convolution filtering, adaptive threshold segmentation, morphological boundary refinement, and circular Hough transform detection enables automated extraction of crystallographic descriptors from atomic-scale images while eliminating observer-dependent interpretation variabilities. Comprehensive structural analysis reveals disparities between distinct Li/Ni mixing regimes, with inadequate cation interdiffusion exhibiting substantially elevated angular deviation frequencies and extensive misaligned region compared to enhanced mixing conditions. Crystallographic parameter investigation demonstrates interlayer spacing variations that reflect preservation of layered structure with compositional heterogeneities versus thermodynamically favorable arrangements. The underlying thermodynamics elucidates counterintuitive relationships wherein enhanced Li/Ni mixing promotes structural coherence through cooperative cation rearrangement approaching minimum energy configurations. These protocols achieve exceptional reproducibility, enabling systematic structure–property correlations essential for data-driven optimization in advanced material development.