Abstract
The potential of computational modeling in the design of organic structure directing agents (OSDAs) for target small-pore zeolites was demonstrated some three decades ago by the use of the ZEBEDDE program for an aluminophosphate zeotype with the CHA topology. In this structure type, d6r units are the only secondary building units (SBUs) and there is a single type of cage, making the approach relatively straightforward, but in other target structures of catalytic interest there may be more than one cage type. The use of a co-templating strategy, in which OSDAs are computationally-designed specifically for each cage type, has therefore been explored. This has been successful, both for AlPOs, where organic cations alone are required as templates, and also for zeolites, where inorganic cations required for aluminosilicate gel crystallization also perform templating functions, favoring specific SBUs. The successful use of co-templates in a synthesis requires that each has similar templating efficacy, to avoid either formation of a dominant single-template phase or a simple mixture of phases. Examples of computation-led new material syntheses that demonstrate these principles include SAPOs with the SAV, AFX, and SFW structure types and aluminosilicates of the RHO family of embedded isoreticular zeolites. Where structural considerations permit, for example in polytypes in the ABC-6 or other structural families, intergrown systems with either simple or statistically-complex patterns of intergrowths can result. These have characteristic pore size distributions and physical properties, and here a deep understanding and control over the ratio of intergrowth phases can only be achieved by detailed characterization and modeling as reported, for example for the JMZ-11 family.
Original language | English |
---|---|
Title of host publication | AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials |
Editors | German Sastre, Frits Daeyaert |
Publisher | Wiley Publications |
Pages | 113-143 |
Number of pages | 31 |
ISBN (Electronic) | 9781119819783 |
ISBN (Print) | 9781119819752 |
DOIs | |
Publication status | Published - 24 Jan 2023 |