A key challenge for metal-exchanged zeolites is the determination of metal cation speciation and nuclearity under synthesis and reaction conditions. Copper-exchanged zeolites, which are widely used in automotive emissions control and potential catalysts for partial methane oxidation, have in particular evidenced a wide variety of Cu structures that are observed to change with exposure conditions, zeolite composition, and topology. Here, we develop predictive models for Cu cation speciation and nuclearity in CHA, MOR, BEA, AFX, and FER zeolite topologies using interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to interrogate this vast configurational and compositional space. Model predictions are used to rationalize experimentally observed differences between Cu-zeolites in a wide-body of literature, including nuclearity populations, structural variations, and methanol per Cu yields. Our results show that both topological features and commonly observed Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites observed in these materials relative to other zeolites such as CHA and BEA. Finally, we used a machine learning classification model to determine the preference to form mononuclear or binuclear Cu sites at different Al configurations in 200 zeolites in the international zeolite database. Model results reveal several zeolite topologies at extreme ends of the mononuclear vs binuclear spectrum, highlighting synthetic options for realization of zeolites with strong Cu nuclearity preferences.
© 2024 The Authors. Published by American Chemical Society.