LAUSR

Metal dispersion is a key concept in the heterogeneous catalysis. The conventional approach for its estimation strongly relies on chemisorption with different probe molecules. Albeit it can generally provide an 'averaged' value in a cost-effective manner, the inhomogeneity of the metal species and the complicated metal-support interactions pose formidable challenges for the accurate determination. Full metal species quantification (FMSQ) is introduced as an advanced method to depict the whole distribution of the metal species, ranging from single atoms to clusters and nanoparticles, in a practical solid catalyst. In this approach, automated analysis of massive high-angle annular dark field scanning transmission electron microscopic images is realized through algorithms specialized in combining the electron microscopy-based atom recognition statistics and deep learning-driven nanoparticle segmentation. FMSQ can circumvent the drawbacks of chemisorption, allowing more reliable structure-performance relationships beyond the metal size.