Unified electronic-geometric descriptor deciphers peroxymonosulfate activation using Fe-based dual-atom catalysts.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41290678.
- Also identified by DOI 10.1038/s41467-025-65500-w and PMC identifier 12647816.
- Licence recorded as CC BY-NC-ND.
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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<sub>OES</sub>) and orbital geometric structure (I<sub>OGS</sub>) 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<sub>OES</sub> = 0.86, I<sub>OGS</sub> = 0.40) achieved 94.2% <sup>1</sup>O<sub>2</sub> yield and fast kinetics (k<sub>obs</sub> = 1.2 min⁻<sup>1</sup>) toward sulfadiazine degradation. Crucially, a flow-through reactor demonstrated >90% pollutant removal for 30 days at industrial flux (122.3 L m⁻<sup>2</sup> h⁻<sup>1</sup>). This work establishes universal orbital-level design principles for sustainable water remediation, bridging atomic-scale insights to engineering-scale implementation.