Recent progress on Al distribution over zeolite frameworks: Linking theories and experiments - Kwak, Seung Jae; Kim, Hyo Seok; Park, Namjun; Park, Myung June; Lee, Won Bo (Korean Journal of Chemical Engineering, 2021)
The location and distribution of aluminum in zeolites is considered important in determining various properties, such as acidity and reactivity. Controlling the placement of aluminum substitution has therefore been of significant interest, and a number of studies have been conducted, including synthesis methods using either different organic structure-directing agents (OSDAs) or cationic species, and the application of dealumination as post-processing. In addition to experimental developments, computational methods have emerged as a useful tool for analyzing the effects of different types of aluminum siting on catalytic properties, especially by incorporating statistical methods. A review of recent developments and findings related to aluminum siting and its effects is presented in this work. Analysis of the thermodynamic distribution of aluminum, as well as synthetically altered distribution in different zeolite frameworks, has been discussed. Computational studies have revealed that catalytic properties are sensitive to adsorbate-dependent properties such as the size of rings and voids for the residence of aluminum, the relative distribution of acid sites, and the adsorption properties of molecules in different framework motifs. Along with the atomic scale evaluation of synthetic treatments in positioning the aluminum, cases of instrumental analysis methods and their verification with simulations is discussed, demonstrating how theories have complemented and, sometimes modified, experimental perspectives. Lastly, recent progress in incorporating machine learning techiques, its application to zeolites, and directions for future work are introduced.
Keywords: Acid Sites; Computational Chemistry; Machine Learning; Statistical Averaging; Zeolites