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Water plays a pivotal role in numerous chemical processes, especially in the production of fuels and fine chemicals de-rived from bio-based feedstocks. Zeolites are porous catalysts used extensively due to their shape-selective adsorption and confinement interactions; However the kinetics of zeolite-catalyzed reactions are significantly impacted by the presence of water, which may affect product selectivity and intrinsic rate constants depending on transition state polarity. In this study, we employed machine learning force fields (MLFFs) to accelerate ab initio molecular dynamics (AIMD) simulations and enhance the phase space exploration of water configurations in a model Brønsted acid zeolite, H-AFI. We interrogated the structure of adsorbed water based on the Si/Al ratio and acid site distribution to disentangle the impact of acid site density and distribution on water matrix organization as a function of water loading. We integrated adsorption thermodynamics, vibrational spectroscopy simulations, and local density maps to interrogate the spatial orientation of adsorbed water clusters and their degree of hydrogen bonding. Our analysis unveiled the intricate interplay between zeolite structure, Brønsted acid site location, and water where spatially disparate acid sites nucleate extended clusters that span siliceous regions of the zeolite. We found that the length scale of ordered water regions is directly related to the Si/Al ratio and spatial distribution of Al sites. These findings provide insights into the molecular-level structure of water in microporous aluminosilicate micropores and demonstrate how acid sites can be used to control water activity which has applications to heterogeneous catalysis and adsorptive separations.
Zheng et al. (Mon,) studied this question.