2025 AIChE Annual Meeting
(712g) Rational Design of Metal Oxide Catalysts for Selective Catalytic Oxidation of Ammonia: Combining Computational Insights with Experimental Validation
By combining density functional theory (DFT) calculations coupled with machine learning potential-based screening, we systematically modeled over 20 metal oxides from the 3d, 4d, 5d, and lanthanide series. We identified the critical steps in NH₃ activation and established that the sum of nitrogen and hydrogen adsorption energies (N ads. + H ads. energy) serves as a primary descriptor for predicting ammonia conversion. Experimental validation confirmed that Co₃O₄, MnO₂, and CeO₂ exhibited high ammonia conversion rates, showing strong correlation with their low N ads. + H ads. energy values.
Through detailed analysis of the internal selective catalytic reduction (i-SCR) mechanism, we identified the dehydrogenation step (H* + HN₂O*) as the rate-determining step (RDS) affecting product selectivity, with H₂N-NO as the key intermediate determining N₂ formation. This revealed a strong correlation between H₂N₂O dehydrogenation reaction energy and N₂ selectivity. Experimental results confirmed that CuO, La₂O₃, and other predicted oxides achieved high N₂ selectivity, while Co₃O₄ showed low selectivity (~30%), aligning with our computational predictions.
This research presents validated descriptors for predicting both activity and selectivity of metal oxide catalysts, providing a rational approach for designing efficient NH₃-SCO catalysts that operate effectively at low temperatures. These findings contribute to the development of emission control technologies for eco-friendly vehicle engines and ammonia-based energy systems.