Computational Insights into Aluminum Proximity in CHA and MFI Zeolites: Implications for Catalyst Optimization

Zeolites, microporous aluminosilicate materials with well-defined channels and cavities, exhibit catalytic properties strongly influenced by the proximity and distribution of aluminum (Al) within their frameworks. Despite extensive research, the precise identity and quantity of Al proximity in different zeolite compositions and under various synthesis conditions remain elusive. In this dissertation, I employ supercell density functional theory (DFT) models to identify Ba2+ as a titrant for probing Al proximity in CHA and MFI zeolites. Additionally, Monte Carlo and a gragh networking model are utilized to investigate the thermodynamics governing heteroatom (e.g., Al) distributions in these zeolite structures, enabling predictions of Al arrangement. Through a combination of Ba and Co titrations and advanced computational models, this work bridges the microscopic details of Al proximity with the macroscopic composition of zeolites. The methodologies presented here can be extended to other zeolites and are also underscores the development of computational tools, in conjunction with experimental techniques like the Ba2+ exchange method, as a promising pathway for tailoring zeolite catalysts for specific applications.