Machine-learning-guided prediction and mechanistic insights into support-acidity-regulated N₂ selectivity over...

Selective conversion of nitrogen-containing species into harmless molecular nitrogen (N₂) remains a key challenge in the catalytic oxidation of nitrogen-containing volatile organic compounds (NVOCs). Machine learning (ML) provides an effective approach for predicting catalytic performance and identifying key descriptors from complex literature-derived datasets. Herein, a literature-derived catalyst database was constructed to predict N₂ selectivity during NVOC oxidation and clarify the factors governing nitrogen transformation. Thirteen descriptors related to catalyst composition, structural properties, support acidity, and reaction conditions were used to train eight ML models. Among them, the ExtraTrees model exhibited the best predictive performance, with a coefficient of determination of 0.958 and a root mean square error of 7.638 on the test set. Shapley additive explanations and partial dependence plots revealed that oxygen concentration, reactant concentration, reaction temperature, gas hourly space velocity, and support acidity were the dominant factors affecting N₂ selectivity, with support acidity identified as the key catalyst-related descriptor. Guided by this descriptor-level insight, Cu/M and CuFe/M catalysts (M = SiO₂, ZSM-5, and Al₂O₃) were prepared and evaluated for acetonitrile oxidation. The catalytic and spectroscopic results confirmed the predicted role of support acidity, showing that different supports regulate CH₃CN adsorption, CN activation, and the evolution of hydrolysis and oxidation related nitrogen-containing intermediates, thereby affecting nitrogen-product distributions and N₂ selectivity. This work integrates interpretable machine-learning prediction with targeted external validation and mechanistic analysis, providing mechanistic insight into support-acidity-regulated nitrogen transformation and guidance for designing NVOC oxidation catalysts with high N₂ selectivity.