Ajou University repository

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
Citations

SCOPUS

14

Citation Export

Publication Year
2021-06-01
Publisher
Springer
Citation
Korean Journal of Chemical Engineering, Vol.38, pp.1117-1128
Keyword
Acid SitesComputational ChemistryMachine LearningStatistical AveragingZeolites
All Science Classification Codes (ASJC)
Chemistry (all)Chemical Engineering (all)
Abstract
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.
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/32063
DOI
https://doi.org/10.1007/s11814-021-0796-2
Fulltext

Type
Review
Funding
This work was supported by the C1 Gas Refinery Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (No. NRF-2018M3D3A1A01055765).
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Park, Myung-June Image
Park, Myung-June박명준
Department of Chemical Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.