Unified electronic-geometric descriptor deciphers peroxymonosulfate activation using Fe-based dual-atom catalysts.

ABSTRACT: The rational design of high-efficiency Fenton-like catalysts remains hindered by insufficient understanding of electronic-geometric synergy in peroxymonosulfate (PMS) activation. We transcend classical d-band theory by proposing a machine learning-decoded binary descriptor (BD) unifying orbital electronic structure (I_OES) and orbital geometric structure (I_OGS) indices to predict PMS activation pathways across diverse coordination environments. This BD framework quantifies antibonding orbital occupancy (via d-p hybridization) and geometric constraints (interatomic distance/O-H elongation), enabling precise screening of Fe-based dual-atom catalysts (DACs). Among FeM DACs (M = Ti, V, Cr, Mn, Fe, Co, Ni, Cu), FeMn DACs with optimal BD values (I_OES = 0.86, I

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12647816 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature