journal article·2026-01-13

Crystal Chemical Analysis Used to Predict the Mechanical Properties of Nanoporous Materials

Maxim Yu Arsentev, Sergey Balabanov, Evgenii I. Sysoev, Artem Surin, Alexander O. Bakulin, Maxim M. Sychov, Stepan A. Vorobiev, Ekaterina V. Skorb, Michael Nosonovsky

Chemistry of Materials· Volume 38· Issue 1
Publisher page

Abstract

The recognition of metal–organic frameworks (MOFs) with the 2025 Nobel Prize in Chemistry underscores the critical importance of their porous and tunable architectures for applications like atmospheric water harvesting, carbon dioxide capture, and toxic gas storage. Understanding the mechanical properties of zeolites, open-framework aluminophosphates (AlPOs), and MOFs porous materials is essential for predicting their practical applications and synthesizability. By applying Alan L. Mackay’s concept of “generalized crystallography,” we extend principles from these microporous crystals to the design of 3D-printed macroporous metamaterials and the derivation of universal, multiscale patterns. We validate the relevance of this method by detecting correlations between structural motifs and mechanical data for metamaterials consistent with results from density functional theory (DFT) calculations for crystals. The crystal chemical analysis involved simplifying crystal structures into nets by constructing Voronoi–Dirichlet polyhedra (VDP) using TOPOS or TopCryst in a fully automated mode. A strong correlation between mechanical properties and the deviation of average, maximum, and minimum bond angles from the regular angles of nets based polyhedral was found. Pearson correlation analysis also showed that greater distances between nodes in zeolites maximize their mechanical properties. This means that SiO 4 tetrahedra should join as “straightforwardly” as possible, without any bends that would reduce mechanical strength. For 3D-printed AlPOs, we observed a strong negative Pearson correlation between mechanical properties and the number of nodes and bonds, whereas for MOFs, the correlation is positive. This suggests the existence of an optimal value for these parameters to maximize mechanical properties at a given porosity. The discovered regularities provide the fast screening of both the most durable porous materials and 3D printed metamaterials.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Zeolite Catalysis and Synthesis · Metal-Organic Frameworks: Synthesis and Applications · Mesoporous Materials and Catalysis

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

Affiliations

Infochemistry Scientific Center, ITMO University, 9 Lomonosova Street, Saint-Petersburg 191002, Russia

Institute of Silicate Chemistry of Russian Academy of Sciences

Department of Micro- and Nanoelectronics, Saint Petersburg Electrotechnical University “LETI”, Professor Popov Str. 5, St. Petersburg 197376, Russia

CRISM “Prometey”, Shpalernaya Str. 49, Saint-Petersburg 191015, Russia

College of Engineering and Applied Science

University of Wisconsin-Milwaukee

Funding

Russian Science Foundation · Ministry of Science and Higher Education of the Russian Federation · ITMO University · Research project “Chemistry, physics and biology of nanostates”

References (41)

Show source-provided references
1. 10.1021/cm302610z10.1021/cm302610z ↗
2. 10.1039/C5CP01168J10.1039/C5CP01168J ↗
3. 10.1021/acs.chemmater.7b0253210.1021/acs.chemmater.7b02532 ↗
4. 10.1002/adma.20170482010.1002/adma.201704820 ↗
5. 10.1002/smll.20190474710.1002/smll.201904747 ↗
6. 10.1021/acsbiomaterials.0c0170810.1021/acsbiomaterials.0c01708 ↗
7. 10.1038/s41586-018-0850-310.1038/s41586-018-0850-3 ↗
8. 10.3390/biomimetics905028510.3390/biomimetics9050285 ↗
9. 10.1021/acsomega.3c0087410.1021/acsomega.3c00874 ↗
10. 10.1016/j.compstruct.2025.11909710.1016/j.compstruct.2025.119097 ↗
11. 10.1038/s42254-023-00639-310.1038/s42254-023-00639-3 ↗
12. 10.3390/cryst1504037410.3390/cryst15040374 ↗
13. 10.1021/acsami.5c0407210.1021/acsami.5c04072 ↗
14. 10.1016/0898-1221(86)90137-910.1016/0898-1221(86)90137-9 ↗
15. 10.1098/rsfs.2015.002710.1098/rsfs.2015.0027 ↗
16. 10.1021/acs.chemmater.4c0347710.1021/acs.chemmater.4c03477 ↗
17. Baerlocher, Ch.; Brouwer, D.; Marler, B.; McCusker, L. B. Database of Zeolite Structures. https://www.iza-structure.org/databases/(accessed September 06, 2025).
18. 10.1107/S002188981103897010.1107/S0021889811038970 ↗
19. 10.1107/S010876739401329210.1107/S0108767394013292 ↗
20. 10.1080/27660400.2022.208804110.1080/27660400.2022.2088041 ↗
21. 10.1080/10586458.1992.1050425310.1080/10586458.1992.10504253 ↗
22. 10.1021/cg500498k10.1021/cg500498k ↗
23. 10.1002/anie.19871059310.1002/anie.198710593 ↗
24. 10.1080/0889311041233132317010.1080/08893110412331323170 ↗
25. 10.3390/e2012095610.3390/e20120956 ↗
26. 10.3390/math1215242610.3390/math12152426 ↗
27. 10.1021/acs.jpclett.7b0265710.1021/acs.jpclett.7b02657 ↗
28. 10.1021/acs.langmuir.4c0348810.1021/acs.langmuir.4c03488 ↗
29. 10.1021/acsomega.3c0732810.1021/acsomega.3c07328 ↗
30. 10.1107/S010876730600362X10.1107/S010876730600362X ↗
31. 10.1038/nature0165010.1038/nature01650 ↗
32. 10.1021/ar800124u10.1021/ar800124u ↗
33. 10.1107/S010876730804059210.1107/S0108767308040592 ↗
34. 10.1007/s11224-025-02631-210.1007/s11224-025-02631-2 ↗
35. 10.1016/j.micromeso.2004.06.01310.1016/j.micromeso.2004.06.013 ↗
36. 10.1039/c3cp51817e10.1039/c3cp51817e ↗
37. 10.1038/s41597-020-0452-410.1038/s41597-020-0452-4 ↗
38. 10.1186/s13321-023-00780-210.1186/s13321-023-00780-2 ↗
39. 10.1016/j.matt.2025.10214010.1016/j.matt.2025.102140 ↗
40. 10.1557/mrs2003.7910.1557/mrs2003.79 ↗
41. 10.1007/s10237-022-01661-710.1007/s10237-022-01661-7 ↗