journal article·2025-12-23Open access

Lessons from Failed Attempts of Computationally Guided Synthesis of Aluminosilicate STF and IFR Zeolites in Hydroxide Media

Omer F. Altundal, Maria Galvez-Llompart, Angel Cantin, José Valero, Susana Valencia, Fernando Rey, Kingsley Christian Kemp, Suk Bong Hong, Frits Daeyaert, German Sastre

Chemistry of Materials· Volume 37· Issue 24

Abstract

Although zeolites are primarily aluminosilicates in terms of chemical composition, not all zeotypes have been successfully synthesized as aluminosilicates to date. This study aims to contribute answering the long-standing question of why some zeotypes cannot, or have not yet been, synthesized as aluminosilicates, using a computational approach that combines big data, artificial intelligence, accurate descriptors, and an algorithm for designing quaternary ammonium organic structure-directing agents (OSDAs) that help lower the total energy of the zeolite-OSDA system. The use of the synthesis energy descriptor enables evaluation of the reaction energy from gel monomers to the final zeolite product, including the occluded OSDA, while also accounting for the energetic contributions of framework aluminum, representing an improvement over previous models based on pure silica. The focus is on STF and IFR zeolites, whose synthesis in hydroxide media typically results in products with very high silicon to aluminum ratios. More than 10000 OSDAs, either designed or retrieved from existing databases, were evaluated under conditions likely to favor the formation of STF or IFR, and the corresponding synthesis energies were calculated for STF, IFR, and approximately 20 other competing zeolite phases. The computational results show that AEI, CHA, and a few other zeolites are consistently more energetically favorable, regardless of the OSDA used. This is explained by the increasing stability of phases as the aluminum content increases. Experimental synthesis using a shortlist of OSDAs predicted to favor STF or IFR, in addition to AEI or CHA, did not yield the expected aluminosilicate products, and instead, amorphous materials were obtained in most cases. While further refinement of computational tools is needed to better reflect experimental synthesis conditions, the results provide valuable insight and justification for the difficulty of synthesizing aluminosilicate STF and IFR in hydroxide media.
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Research context

OpenAlex topics: Zeolite Catalysis and Synthesis · Mesoporous Materials and Catalysis · Catalysis for Biomass Conversion

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Instituto de Tecnología Química UPV-CSIC

Universidad Politécnica de Valencia

Molecular Topology and Drug Design Unit. Faculty of Pharmacy and Food Sciences, Department of Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine

University of Valencia

Center for Ordered Nanoporous Materials Synthesis, Division of Environmental Science and Engineering

POSTECH

SynopsisDeNovoDesign

Funding

Consejo Superior de Investigaciones Cient?ficas · Ministerio de Ciencia, Innovaci?n y Universidades · European Regional Development Fund · Universitat Polit?cnica de Val?ncia · NextGenerationEU · Ag?ncia Valenciana de la Innovaci? · EcoPro HN

References (72)

Show source-provided references
1. Cronstedt A. F. · Sven. Vetenskapsakad. Handl. Stockholm · 1756 · 17 · 120
2. Coombs D. S. · Can. Mineral. · 1997 · 35 · 1571
3. 10.1016/0021-9517(73)90076-610.1016/0021-9517(73)90076-6 ↗
4. 10.1038/s41467-021-22936-010.1038/s41467-021-22936-0 ↗
5. 10.1021/jp903788n10.1021/jp903788n ↗
6. 10.1016/j.jcat.2011.11.00210.1016/j.jcat.2011.11.002 ↗
7. 10.1016/j.jcat.2014.01.00710.1016/j.jcat.2014.01.007 ↗
8. 10.1016/j.micromeso.2012.10.01910.1016/j.micromeso.2012.10.019 ↗
9. 10.1016/S0926-860X(98)00264-610.1016/S0926-860X(98)00264-6 ↗
10. 10.1021/ie500903710.1021/ie5009037 ↗
11. 10.1016/j.jcat.2015.01.01310.1016/j.jcat.2015.01.013 ↗
12. 10.1002/anie.20171330810.1002/anie.201713308 ↗
13. 10.1021/acs.jpcc.5b0328910.1021/acs.jpcc.5b03289 ↗
14. 10.1016/S0167-2991(00)81064-210.1016/S0167-2991(00)81064-2 ↗
15. 10.1002/ejic.20040074310.1002/ejic.200400743 ↗
16. 10.1021/acs.jpcc.6b0901210.1021/acs.jpcc.6b09012 ↗
17. 10.1016/j.cjsc.2024.10025210.1016/j.cjsc.2024.100252 ↗
18. 10.1093/nsr/nwac02310.1093/nsr/nwac023 ↗
19. 10.1021/acsnano.8b0262510.1021/acsnano.8b02625 ↗
20. 10.1016/j.micromeso.2021.11142210.1016/j.micromeso.2021.111422 ↗
21. 10.1039/a804457k10.1039/a804457k ↗
22. 10.1021/cg800115q10.1021/cg800115q ↗
23. Guth J. L. · 1989 · 398 · 17610.1021/bk-1989-0398.ch013 ↗
24. 10.1021/jp040243410.1021/jp0402434 ↗
25. 10.1039/a803263g10.1039/a803263g ↗
26. 10.1002/anie.20200780410.1002/anie.202007804 ↗
27. 10.1016/j.powtec.2013.03.04810.1016/j.powtec.2013.03.048 ↗
28. 10.1016/S0926-860X(03)00413-710.1016/S0926-860X(03)00413-7 ↗
29. 10.1021/cm401802410.1021/cm4018024 ↗
30. 10.1021/ja990722u10.1021/ja990722u ↗
31. 10.1021/acscatal.7b0367610.1021/acscatal.7b03676 ↗
32. 10.1039/D5SC00899A10.1039/D5SC00899A ↗
33. 10.1016/S0167-2991(08)63768-410.1016/S0167-2991(08)63768-4 ↗
34. 10.1039/FT996920206510.1039/FT9969202065 ↗
35. 10.1007/s11244-015-0381-110.1007/s11244-015-0381-1 ↗
36. 10.1021/acs.chemmater.5b0457810.1021/acs.chemmater.5b04578 ↗
37. 10.1002/9781119819783.ch910.1002/9781119819783.ch9 ↗
38. Schwalbe-Koda D. · 2023 · 8110.1002/9781119819783.ch4 ↗
39. 10.1039/D5CS00306G10.1039/D5CS00306G ↗
40. 10.1126/science.abh335010.1126/science.abh3350 ↗
41. Organic Structure Database ; 2025, https://zeodb.mit.edu/index. (accessed 4 Nov 2025).
42. 10.1039/C8TA11913A10.1039/C8TA11913A ↗
43. 10.1021/acs.chemmater.1c0132910.1021/acs.chemmater.1c01329 ↗
44. 10.1021/jacs.6b0134110.1021/jacs.6b01341 ↗
45. 10.1038/s41467-019-12394-010.1038/s41467-019-12394-0 ↗
46. 10.1016/j.jcat.2021.04.02710.1016/j.jcat.2021.04.027 ↗
47. 10.1039/C2DT32230G10.1039/C2DT32230G ↗
48. 10.1039/D3CC01779F10.1039/D3CC01779F ↗
49. 10.1021/acs.jpcc.3c0156710.1021/acs.jpcc.3c01567 ↗
50. 10.1021/acs.jpcc.3c0364010.1021/acs.jpcc.3c03640 ↗
51. 10.1021/cr800495t10.1021/cr800495t ↗
52. 10.1039/c0cp01045f10.1039/c0cp01045f ↗
53. 10.1021/acs.chemmater.2c0320410.1021/acs.chemmater.2c03204 ↗
54. 10.1039/D5MH01044F10.1039/D5MH01044F ↗
55. 10.1016/j.micromeso.2020.11083410.1016/j.micromeso.2020.110834 ↗
56. 10.1039/D2NJ02820D10.1039/D2NJ02820D ↗
57. Valyocsik, E. W. Synthesis of Porous Crystalline MCM-58 ; US 5,441,721 A, 1995.10.1016/0144-2449(96)88997-4 ↗
58. STF Literature ; 2025, https://zeodb.mit.edu/framework/STF. (accessed 4 Nov 2025).
59. 10.1021/acs.jpclett.0c0173410.1021/acs.jpclett.0c01734 ↗
60. StatSoft Inc, STATISTICA (data analysis software system)version 12 ; 2014.
61. 10.1038/382604a010.1038/382604a0 ↗
62. 10.1039/c3ta10626h10.1039/c3ta10626h ↗
63. 10.1039/D0SC03075A10.1039/D0SC03075A ↗
64. 10.26434/chemrxiv-2024-d74sw10.26434/chemrxiv-2024-d74sw ↗
65. 10.1016/j.ces.2023.11918810.1016/j.ces.2023.119188 ↗
66. 10.1002/9781119819783.ch210.1002/9781119819783.ch2 ↗
67. 10.1080/00268976.2018.149274710.1080/00268976.2018.1492747 ↗
68. Chemical Search ; 2025, https://chem-space.com/search. (accessed 4 Nov 2025).
69. 10.1515/zkri-2018-213210.1515/zkri-2018-2132 ↗
70. 10.1021/cm100610810.1021/cm1006108 ↗
71. IFR Literature ; 2025, https://zeodb.mit.edu/framework/IFR. (accessed 4 Nov 2025)
72. 10.1021/cm001180e10.1021/cm001180e ↗