Probing the Role of Acid Site Distribution on the Water Structure in Aluminosilicate Zeolites: Insights from Molecular Dynamics

Water plays a pivotal role in numerous chemical processes, especially in the production of fuels and fine chemicals derived from biobased 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 mode 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 the 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 aluminosilicate micropores and demonstrate how acid sites can be used to control water activity, which has applications to heterogeneous catalysis and adsorptive separations.

水在诸多化学过程中发挥关键作用，尤其在由生物质基原料制取燃料与精细化学品的生产环节中。沸石（zeolites）是一类多孔催化剂，凭借其择形吸附与限域相互作用而被广泛应用；然而，沸石催化反应的动力学过程会显著受水的存在影响，而水可依据过渡态极性（transition-state polarity）改变产物选择性与本征速率常数。在本研究中，我们采用机器学习力场（machine learning force fields，MLFF）以加速从头算分子动力学（ab initio molecular dynamics，AIMD）模拟，并提升了H-AFI型布伦斯特酸（Brønsted acid）沸石中水构型的相空间探索能力。我们基于硅铝比与酸位点分布，解析了吸附态水的结构，以此阐明酸位点密度与分布随水负载量变化对水团簇排布的影响。我们整合了吸附热力学、振动光谱模拟与局部密度图，对吸附态水团簇的空间取向及其氢键作用程度进行了研究。我们的分析揭示了沸石结构、布伦斯特酸位点位置与水之间复杂的相互作用：空间上分散的酸位点会形成跨越沸石硅质区域的扩展型水团簇。我们发现有序水区的尺寸与硅铝比以及铝位点的空间分布直接相关。这些发现为铝硅酸盐微孔中水的分子级结构提供了深入见解，并阐明了可通过酸位点调控水活度的方法，这一成果可应用于多相催化与吸附分离领域。