Inverse design of metal–organic frameworks for C2H4/C2H6 separation
Source abstract
Abstract
Abstract Efficient separation of C2H4/C2H6 mixtures is of paramount importance in the petrochemical industry. Nanoporous materials, especially metal-organic frameworks (MOFs), may serve the purpose owing to their tailorable structures and pore geometries. In this work, we propose a computational framework for high-throughput screening and inverse design of high-performance MOFs for adsorption and membrane processes. High-throughput screening of the computational-ready, experimental (CoRE 2019) MOF database leads to materials with exceptionally high ethane-selective adsorption selectivity (LUDLAZ: 7.68) and ethene-selective membrane selectivity (EBINUA02: 2167.3). Moreover, the inverse design enables the exploration of broader chemical space and identification of MOF structures with even higher membrane selectivity and permeability. In addition, a relative membrane performance score (rMPS) has been formulated to evaluate the overall membrane performance relative to the Robeson boundary. The computational framework offers guidelines for the design of MOFs and is generically applicable to materials discovery for gas storage and separation.
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Research context
OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Membrane Separation and Gas Transport · Covalent Organic Framework Applications
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References (48)
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1. Sholl, D. S. & Lively, R. P. Seven chemical separations to change the world. Nature 532, 435–437 (2016).10.1038/532435a ↗
2. Rungta, M., Zhang, C., Koros, W. J. & Xu, L. Membrane‐based ethylene/ethane separation: the upper bound and beyond. AIChE J. 59, 3475–3489 (2013).10.1002/aic.14105 ↗
3. Ding, Q. et al. Exploiting equilibrium-kinetic synergetic effect for separation of ethylene and ethane in a microporous metal-organic framework. Sci. Adv. 6, eaaz4322 (2020).10.1126/sciadv.aaz4322 ↗
4. Li, L. et al. Ethane/ethylene separation in a metal-organic framework with iron-peroxo sites. Science 362, 443–446 (2018).10.1126/science.aat0586 ↗
5. Lv, D. et al. Recent advances in adsorptive separation of ethane and ethylene by C2H6-selective MOFs and other adsorbents. Chem. Eng. J. https://doi.org/10.1016/j.cej.2021.133208 (2021).10.1016/j.cej.2021.133208 ↗
6. Rangnekar, N., Mittal, N., Elyassi, B., Caro, J. & Tsapatsis, M. Zeolite membranes—a review and comparison with MOFs. Chem. Soc. Rev. 44, 7128–7154 (2015).10.1039/C5CS00292C ↗
7. Li, J. R., Sculley, J. & Zhou, H. C. Metal-organic frameworks for separations. Chem. Rev. 112, 869–932 (2012).10.1021/cr200190s ↗
8. Tranchemontagne, D. J., Mendoza-Cortes, J. L., O’Keeffe, M. & Yaghi, O. M. Secondary building units, nets and bonding in the chemistry of metal-organic frameworks. Chem. Soc. Rev. 38, 1257–1283 (2009).10.1039/b817735j ↗
9. Zhou, M., Vassallo, A. & Wu, J. Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling. J. Membr. Sci. https://doi.org/10.1016/j.memsci.2019.117675 (2020).10.1016/j.memsci.2019.117675 ↗
10. Wang, J., Zhou, M., Lu, D., Fei, W. & Wu, J. Virtual screening of nanoporous materials for noble gas separation. ACS Appl. Nano Mater. 5, 3701–3711 (2022).10.1021/acsanm.1c03907 ↗
11. Bárcia, P. S. et al. Reverse shape selectivity in the adsorption of hexane and xylene isomers in MOF UiO-66. Microporous Mesoporous Mater. 139, 67–73 (2011).10.1016/j.micromeso.2010.10.019 ↗
12. Oh, H. et al. A cryogenically flexible covalent organic framework for efficient hydrogen isotope separation by quantum sieving. Angew. Chem. Int. Ed. 52, 13219–13222 (2013).10.1002/anie.201307443 ↗
13. Chen, G. et al. M-gallate MOF/6FDA-polyimide mixed-matrix membranes for C2H4/C2H6 separation. J. Membr. Sci. 620, 118852 (2021).10.1016/j.memsci.2020.118852 ↗
14. Bao, Z. et al. Molecular sieving of ethane from ethylene through the molecular cross‐section size differentiation in gallate‐based metal–organic frameworks. Angew. Chem. Int. Ed. 57, 16020–16025 (2018).10.1002/anie.201808716 ↗
15. Zhou, M., Tian, Y., Fei, W. & Wu, J. Fractionation of isotopic methanes with metal–organic frameworks. J. Phys. Chem. C 123, 7397–7407 (2019).10.1021/acs.jpcc.8b11393 ↗
16. Wang, J., Zhou, M., Lu, D., Fei, W. & Wu, J. Computational screening and design of nanoporous membranes for efficient carbon isotope separation. Green. Energy Environ. 5, 364–373 (2020).10.1016/j.gee.2020.07.025 ↗
17. Kang, M. et al. High-throughput discovery of Ni(IN)(2) for ethane/ethylene separation. Adv. Sci. 8, e2004940 (2021).10.1002/advs.202004940 ↗
18. Wijmans, J. G. & Baker, R. W. The solution-diffusion model: a review. J. Membr. Sci. 107, 1–21 (1995).10.1016/0376-7388(95)00102-I ↗
19. Altintas, C. & Keskin, S. Computational screening of MOFs for C2H6/C2H4 and C2H6/CH4 separations. Chem. Eng. Sci. 139, 49–60 (2016).10.1016/j.ces.2015.09.019 ↗
20. Tang, H. & Jiang, J. In silico screening and design strategies of ethane‐selective metal–organic frameworks for ethane/ethylene separation. AIChE J. 67, e17025 (2021).10.1002/aic.17025 ↗
21. Halder, P. & Singh, J. K. High-throughput screening of metal–organic frameworks for ethane–ethylene separation using the machine learning technique. Energy Fuels 34, 14591–14597 (2020).10.1021/acs.energyfuels.0c03063 ↗
22. Kim, J. et al. Large-scale computational screening of zeolites for ethane/ethene separation. Langmuir 28, 11914–11919 (2012).10.1021/la302230z ↗
23. Kärger, J., Ruthven, D. M. & Theodorou, D. N. Diffusion in Nanoporous Materials. (WILEY-VCH, 2012).10.1002/9783527651276 ↗
24. Verploegh, R. J., Nair, S. & Sholl, D. S. Temperature and loading-dependent diffusion of light hydrocarbons in ZIF-8 as predicted through fully flexible molecular simulations. J. Am. Chem. Soc. 137, 15760–15771 (2015).10.1021/jacs.5b08746 ↗
25. Zhou, M. & Wu, J. Massively parallel GPU-accelerated string method for fast and accurate prediction of molecular diffusivity in nanoporous materials. ACS Appl. Nano Mater. 4, 5394–5403 (2021).10.1021/acsanm.1c00727 ↗
26. Zhou, M. & Wu, J. A GPU implementation of classical density functional theory for rapid prediction of gas adsorption in nanoporous materials. J. Chem. Phys. 153, 074101 (2020).10.1063/5.0020797 ↗
27. Glaser, J. et al. Strong scaling of general-purpose molecular dynamics simulations on GPUs. Comput. Phys. Commun. 192, 97–107 (2015).10.1016/j.cpc.2015.02.028 ↗
28. Kutzner, C. et al. More bang for your buck: Improved use of GPU nodes for GROMACS 2018. J. Comput. Chem. 40, 2418–2431 (2019).10.1002/jcc.26011 ↗
29. Kim, B., Lee, S. & Kim, J. Inverse design of porous materials using artificial neural networks. Sci. Adv. 6, eaax9324 (2020).10.1126/sciadv.aax9324 ↗
30. Yao, Z. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3, 76–86 (2021).10.1038/s42256-020-00271-1 ↗
31. Gustafson, J. A. & Wilmer, C. E. Intelligent selection of metal-organic framework arrays for methane sensing via genetic algorithms. ACS Sens. 4, 1586–1593 (2019).10.1021/acssensors.9b00268 ↗
32. Chung, Y. G. et al. In silico discovery of metal-organic frameworks for precombustion CO(2) capture using a genetic algorithm. Sci. Adv. 2, e1600909 (2016).10.1126/sciadv.1600909 ↗
33. Dou, H. et al. Boron nitride membranes with a distinct nanoconfinement effect for efficient ethylene/ethane separation. Angew. Chem. Int. Ed. 58, 13969–13975 (2019).10.1002/anie.201907773 ↗
34. Cotterman, R. L., Schwarz, B. J. & Prausnitz, J. M. Molecular thermodynamics for fluids at low and high densities. Part I: pure fluids containing small or large molecules. AIChE J. 32, 1787–1798 (1986).10.1002/aic.690321104 ↗
35. McKellar, S. C. et al. The effect of pressure on the post-synthetic modification of a nanoporous metal-organic framework. Nanoscale 6, 4163–4173 (2014).10.1039/C3NR04161A ↗
36. Tian, L., Chen, Z., Yu, A., Song, H.-B. & Cheng, P. Novel flexible bis-triazole bridged copper (II) coordination polymers varying from one-to three-dimensionality. CrystEngComm 14, 2032–2039 (2012).10.1039/c2ce06204f ↗
37. Lee, S. et al. Computational screening of trillions of metal-organic frameworks for high-performance methane storage. ACS Appl. Mater. Interfaces 13, 23647–23654 (2021).10.1021/acsami.1c02471 ↗
38. Wu, Y., Chen, H., Liu, D., Qian, Y. & Xi, H. Adsorption and separation of ethane/ethylene on ZIFs with various topologies: Combining GCMC simulation with the ideal adsorbed solution theory (IAST). Chem. Eng. Sci. 124, 144–153 (2015).10.1016/j.ces.2014.07.019 ↗
39. Bux, H., Chmelik, C., Krishna, R. & Caro, J. Ethene/ethane separation by the MOF membrane ZIF-8: molecular correlation of permeation, adsorption, diffusion. J. Membr. Sci. 369, 284–289 (2011).10.1016/j.memsci.2010.12.001 ↗
40. Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A. & Skiff, W. M. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114, 10024–10035 (2002).10.1021/ja00051a040 ↗
41. Heinen, J. & Dubbeldam, D. On flexible force fields for metal-organic frameworks: Recent developments and future prospects. Wiley Interdiscip. Rev. Comput. Mol. Sci. 8, e1363 (2018).10.1002/wcms.1363 ↗
42. Yang, Y. & Sholl, D. S. A systematic examination of the impacts of MOF flexibility on intracrystalline molecular diffusivities. J. Mater. Chem. A 10, 4242–4253 (2022).10.1039/D1TA09267G ↗
43. Willems, T. F., Rycroft, C. H., Kazi, M., Meza, J. C. & Haranczyk, M. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous Mesoporous Mater. 149, 134–141 (2012).10.1016/j.micromeso.2011.08.020 ↗
44. Haldoupis, E., Nair, S. & Sholl, D. S. Efficient calculation of diffusion limitations in metal organic framework materials: a tool for identifying materials for kinetic separations. J. Am. Chem. Soc. 132, 7528–7539 (2010).10.1021/ja1023699 ↗
45. Bennett, C. Molecular Dynamics and Transition State Theory: The Simulation of Infrequent Events in Algorithms for Chemical Computations ACS Symposium Series Vol. 46 Ch. 4, 63–97 (American Chemical Society, 1977).10.1021/bk-1977-0046.ch004 ↗
46. Chandler, D. Statistical mechanics of isomerization dynamics in liquids and the transition state approximation. J. Chem. Phys. 68, 2959–2970 (1978).10.1063/1.436049 ↗
47. Xu, H., Cabriolu, R. & Smit, B. Effects of degrees of freedom on calculating diffusion properties in nanoporous materials. J. Chem. Theory Comput. 18, 2826–2835 (2022).10.1021/acs.jctc.2c00094 ↗
48. Gad, A. F. Pygad: an intuitive genetic algorithm python library. Preprint at arXiv https://arxiv.org/abs/2106.06158 (2021).