journal article·2026-03-10

Metal–Organic Framework Flexibility Boosting Atmospheric Water Harvesting by a Zinc-Based Glycine–Glyoxime–Histidine Framework

Fatemeh Keshavarz, Stefanie Kröll, Jakob Gamper, Josef M. Gallmetzer, Thomas S. Hofer, Nima Rezaei, Bernardo Barbiellini

Chemistry of Materials· Volume 38· Issue 5
Publisher page

Abstract

Structural flexibility of metal–organic frameworks (MOFs) is both a challenge and an opportunity, depending on the MOF’s properties and the proposed applications. Here, we focus on ZnGGH, a peptide-based flexible MOF that switches from its “gate-closed” ZnGGH-9 conformer to “gate-open” ZnGGH-4 upon water adsorption. We use periodic density functional theory (DFT), semiempirical self-consistent charge density functional tight binding third order (DFTB3) and neural network-based MACE-MP simulations to understand the ZnGGH’s behavior and assess its potential for atmospheric water harvesting (AWH). Our results show that ZnGGH selectively adsorbs water over CO 2, O 2, and N 2 . As water loading increases, water-linker interactions trigger the gate opening and affect temperature-dependent water diffusion within the pores. Based on our analyses and comparison with benchmark AWH MOFs (MOF-303, MOF-333 and MOF-808), the gate opening facilitates AWH by weakening of the water interactions during water adsorption/desorption cycles while maintaining water selectivity. The MOF is thermally and hydrolytically stable under AWH conditions. To enable grid-scale applications, future work should focus on reducing synthesis costs to improve its economic viability.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Supramolecular Self-Assembly in Materials · Carbon Dioxide Capture Technologies

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

Affiliations

Department of Physics, School of Engineering Science, LUT University, Yliopistonkatu 34, Lappeenranta FI-53850, Finland

Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80-82, Innsbruck 6020, Austria

Department of Separation Science, School of Engineering Science, LUT University, Yliopistonkatu 34, Lappeenranta FI-53850, Finland

Department of Physics

Northeastern University

Funding

Austrian Science Fund · Research Council of Finland

References (87)

Show source-provided references
1. 10.1016/j.mtcomm.2024.10978010.1016/j.mtcomm.2024.109780 ↗
2. 10.1021/acs.chemmater.3c0302610.1021/acs.chemmater.3c03026 ↗
3. 10.1002/adma.20220907310.1002/adma.202209073 ↗
4. 10.1039/D1TA01043C10.1039/D1TA01043C ↗
5. 10.1002/ange.20221807610.1002/ange.202218076 ↗
6. 10.1016/j.ccr.2023.21561110.1016/j.ccr.2023.215611 ↗
7. 10.1039/D1DT03842G10.1039/D1DT03842G ↗
8. 10.1016/j.ccr.2024.21566010.1016/j.ccr.2024.215660 ↗
9. 10.1002/anie.20201180210.1002/anie.202011802 ↗
10. 10.1039/C7TA05598F10.1039/C7TA05598F ↗
11. 10.1002/adma.20241472410.1002/adma.202414724 ↗
12. 10.1039/d2cs00442a10.1039/d2cs00442a ↗
13. 10.1038/s41586-018-0820-910.1038/s41586-018-0820-9 ↗
14. 10.1016/j.mattod.2022.08.01110.1016/j.mattod.2022.08.011 ↗
15. 10.1007/s40242-020-9093-610.1007/s40242-020-9093-6 ↗
16. 10.1038/s41598-020-58405-910.1038/s41598-020-58405-9 ↗
17. 10.1002/adma.20170430410.1002/adma.201704304 ↗
18. 10.1016/0927-0256(96)00008-010.1016/0927-0256(96)00008-0 ↗
19. 10.1103/PhysRevB.54.1116910.1103/PhysRevB.54.11169 ↗
20. 10.1016/j.chempr.2019.10.01210.1016/j.chempr.2019.10.012 ↗
21. 10.1039/c1cc11416f10.1039/c1cc11416f ↗
22. 10.1103/PhysRevLett.115.03640210.1103/PhysRevLett.115.036402 ↗
23. 10.1103/PhysRevB.59.175810.1103/PhysRevB.59.1758 ↗
24. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
25. 10.1063/1.511378710.1063/1.5113787 ↗
26. 10.3390/met1304072810.3390/met13040728 ↗
27. 10.1073/pnas.192125811710.1073/pnas.1921258117 ↗
28. 10.1038/s41467-021-26618-910.1038/s41467-021-26618-9 ↗
29. 10.1063/1.338234410.1063/1.3382344 ↗
30. 10.1002/jcc.2175910.1002/jcc.21759 ↗
31. 10.1063/5.002882110.1063/5.0028821 ↗
32. 10.1063/5.022751410.1063/5.0227514 ↗
33. Press W. H. · 1988
34. 10.48550/arXiv.2510.0255010.48550/arXiv.2510.02550 ↗
35. 10.1021/cm502760q10.1021/cm502760q ↗
36. 10.1103/PhysRevB.58.726010.1103/PhysRevB.58.7260 ↗
37. 10.1590/S0103-5053200900070000210.1590/S0103-50532009000700002 ↗
38. 10.1080/23746149.2019.171025210.1080/23746149.2019.1710252 ↗
39. 10.1021/acs.jpcb.3c0415510.1021/acs.jpcb.3c04155 ↗
40. 10.1021/acs.jpcc.4c0787210.1021/acs.jpcc.4c07872 ↗
41. 10.1021/acs.jpclett.3c0094110.1021/acs.jpclett.3c00941 ↗
42. 10.1098/rsta.2012.048310.1098/rsta.2012.0483 ↗
43. 10.1063/5.008704210.1063/5.0087042 ↗
44. 10.1038/s42256-024-00956-x10.1038/s42256-024-00956-x ↗
45. 10.1063/1.514319010.1063/1.5143190 ↗
46. 10.1088/1361-648X/aa680e10.1088/1361-648X/aa680e ↗
47. Gamper, J.; Gallmetzer, J. M.; Weiss, A. K. H.; Hofer, T. S. PQ: An Open-Source Platform for Advanced Molecular Dynamics Simulations ; Zenodo, 2024.
48. 10.1021/ct300849w10.1021/ct300849w ↗
49. 10.1021/jp506557r10.1021/jp506557r ↗
50. 10.1021/ct100684s10.1021/ct100684s ↗
51. 10.1021/acs.jpcc.3c0412310.1021/acs.jpcc.3c04123 ↗
52. 10.1021/acs.jctc.3c0140510.1021/acs.jctc.3c01405 ↗
53. 10.1080/08927022.2016.127702410.1080/08927022.2016.1277024 ↗
54. 10.1103/PhysRevB.13.518810.1103/PhysRevB.13.5188 ↗
55. 10.1021/acs.jpcc.2c0510310.1021/acs.jpcc.2c05103 ↗
56. 10.1063/5.029700610.1063/5.0297006 ↗
57. 10.1038/s41467-019-12751-z10.1038/s41467-019-12751-z ↗
58. 10.1021/acsami.3c1071010.1021/acsami.3c10710 ↗
59. 10.1038/s41467-022-30687-910.1038/s41467-022-30687-9 ↗
60. 10.1021/jacs.4c1528710.1021/jacs.4c15287 ↗
61. 10.1007/BF0158911610.1007/BF01589116 ↗
62. 10.1016/j.jcp.2006.07.03310.1016/j.jcp.2006.07.033 ↗
63. 10.1103/PhysRev.159.9810.1103/PhysRev.159.98 ↗
64. 10.1016/0021-9991(83)90014-110.1016/0021-9991(83)90014-1 ↗
65. 10.1063/1.240842010.1063/1.2408420 ↗
66. 10.1063/1.46394010.1063/1.463940 ↗
67. 10.1063/1.44811810.1063/1.448118 ↗
68. Gamper, J.; Gallmetzer, J. M.; Weiss, A. K. H.; Hofer, T. S. PQAnalysis ; Zenodo, 2024.
69. 10.4028/www.scientific.net/SSP.130.1510.4028/www.scientific.net/SSP.130.15 ↗
70. 10.1107/S002188981103897010.1107/S0021889811038970 ↗
71. 10.1002/andp.1905322080610.1002/andp.19053220806 ↗
72. 10.1016/0263-7855(96)00018-510.1016/0263-7855(96)00018-5 ↗
73. 10.1002/chem.20110307810.1002/chem.201103078 ↗
74. 10.1016/j.seppur.2020.11732610.1016/j.seppur.2020.117326 ↗
75. Keshavarz, F.; Livas, C.; Tylianakis, E.; Barbiellini, B.; Froudakis, G. Accelerating Materials Discovery for Water Crisis: Multi-Objective Machine Learning for Atmospheric Water Harvesting by MOFs ; Springer Science and Business Media LLC, 2025.10.21203/rs.3.rs-7765200/v1 ↗
76. 10.1016/j.micromeso.2011.08.02010.1016/j.micromeso.2011.08.020 ↗
77. 10.1016/j.susmat.2024.e0112010.1016/j.susmat.2024.e01120 ↗
78. 10.1021/acs.jpcc.4c0173310.1021/acs.jpcc.4c01733 ↗
79. 10.1021/acs.jpcc.3c0035410.1021/acs.jpcc.3c00354 ↗
80. 10.1002/adma.20221023510.1002/adma.202210235 ↗
81. 10.1039/D3QM00484H10.1039/D3QM00484H ↗
82. 10.1021/ja500330a10.1021/ja500330a ↗
83. 10.1021/jacs.2c0975610.1021/jacs.2c09756 ↗
84. 10.1021/acs.jpcc.4c0023810.1021/acs.jpcc.4c00238 ↗
85. 10.3390/nano1201015910.3390/nano12010159 ↗
86. 10.1016/j.rcradv.2023.20016610.1016/j.rcradv.2023.200166 ↗
87. 10.1038/s41597-022-01181-010.1038/s41597-022-01181-0 ↗