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.

沸石是一类具备规整孔道与空腔的微孔铝硅酸盐材料，其催化性能受骨架内铝（Al）的空间邻近性与分布特征的显著调控。尽管已有广泛研究，但不同沸石组成及多种合成条件下，铝空间邻近性的精确类型与数量仍未明确。本研究采用超晶胞密度泛函理论（DFT）模型，以钡离子（Ba²+）作为滴定剂，探究CHA与MFI型沸石中的铝空间邻近性。此外，本研究还借助蒙特卡洛方法与图网络模型，解析调控此类沸石结构中杂原子（如铝）分布的热力学机制，从而实现铝原子排布的精准预测。通过结合钡离子与钴离子滴定实验及先进计算模型，本研究搭建起铝空间邻近性的微观细节与沸石宏观组成之间的桥梁。本文提出的研究方法可推广至其他沸石体系，同时也凸显了将计算工具与钡离子交换法等实验技术相结合的路径，为定制化开发适配特定应用场景的沸石催化剂提供了极具前景的发展方向。