Volatile organic compounds (VOCs) are major precursors of ozone and fine particulate matter, posing significant risks to both environmental quality and human health. Catalytic oxidation is widely regarded as one of the most effective approaches for VOC abatement; however, the rational design of efficient catalysts remains a central challenge. In this review, we present a perspective on MOF-based catalysts by addressing three fundamental questions in catalyst design: where the active sites are located, how they function, and how their stability can be maintained. We show that metal-organic frameworks (MOFs), owing to their tunable coordination environments and porous architectures, provide unique opportunities to precisely define and regulate active sites. Early studies focus on dispersing active species on MOFs to control their location, while subsequent advances emphasize interfacial and electronic regulation to improve catalytic function. More recently, MOF-derived catalysts and photothermal systems have been developed to enhance structural stability and enable efficient energy utilization. By integrating representative studies from the literature with our own contributions, we review how MOF-based systems evolve from simple active-site carriers to structurally and functionally integrated catalysts. This perspective provides a unified framework for understanding structure-function-stability relationships in VOC oxidation and offers guidance for the design of next-generation catalytic systems.
Shuchen Liu, Qinye Fang, Jin-Ming Luo et al.· Chemical Communications· 0 citations
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.
Haotian Hu, Ying Wang, Zihao Zhai et al.· Journal of Colloid and Inter...· 0 citations
