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Single-layered fluorinated graphene nanopores for H2/CH4 and H2/CO2 separation with high efficiency and selectivity

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Abstract

The utilization of hydrogen gas (H2) as an energy resource is a critical alternative to relieve the current greenhouse effect exacerbated by the excessive use of fossil fuels. The production of pure H2 is usually achieved by its separation from H2/CH4 and H2/CO2 mixtures; however, such process still represents a great challenge due to the inevitable contamination that occurs after the membrane sieving. Here, we investigate the ability of a 2-dimensional material, a nanoporous fluorinated graphene (F-GRA), to perform the separation of H2/CH4 and H2/CO2 using molecular dynamics simulations. We generated three representative nanopores with different morphologies in F-GRA sheets to separately explore their sieving performances for the H2 separation in the H2/CH4 and H2/CO2 mixtures. Our results revealed that the three F-GRA pores have an excellent performance for the H2/CH4 separation, displaying a high permeance for H2 (over 104 GPU) and a complete rejection for CH4; these results suggest an ideal permeability and selectivity for these 2D systems. Additionally, two F-GRA pores, namely, pore2 and pore3, also displayed high separation performance in the case of the H2/CO2 mixture, while the remaining pore, namely, pore1, exhibit poor performance due to the tight obstruction of the CO2 gas inside the nanopore. Combined, our findings exploit the utilization of the nanoporous F-GRA 2D material for the separation of H2/CH4 and H2/CO2 gas mixtures, which might open new possibilities for the future of gas sieving membrane preparation.

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References

  1. Gallucci F, Fernandez E, Corengia P, Annaland MV (2013) Recent advances on membranes and membrane reactors for hydrogen production. Chem Eng Sci 92:40–66. https://doi.org/10.1016/j.ces.2013.01.008

    Article  CAS  Google Scholar 

  2. Liu H, Liu SQ (2021) Life cycle energy consumption and GHG emissions of hydrogen production from underground coal gasification in comparison with surface coal gasification. Int J Hydrogen Energ 46:9630–9643. https://doi.org/10.1016/j.ijhydene.2020.12.096

    Article  CAS  Google Scholar 

  3. Sinha V, Govindarajan N, de Bruin B, Meijer EJ (2018) How solvent affects C-H activation and hydrogen production pathways in homogeneous Ru-catalyzed methanol dehydrogenation reactions. ACS Catal 8:6908–6913. https://doi.org/10.1021/acscatal.8b01177

    Article  CAS  Google Scholar 

  4. Van Acht SCJ, Laycock CJ, Carr SJW, Maddy J, Guwy AJ, Lloyd G, Raymakers LFJM, Wright AD (2021) Optimization of VPSA-EHP/C process for high-pressure hydrogen recovery from coke oven gas using CO selective adsorbent. Int J Hydrogen Energ 46:709–725. https://doi.org/10.1016/j.ijhydene.2020.10.005

    Article  CAS  Google Scholar 

  5. Li X, Singh RP, Dudeck KW, Berchtold KA, Benicewicz BC (2014) Influence of polybenzimidazole main chain structure on H2/CO2 separation at elevated temperatures. J Membrane Sci 461:59–68

    Article  CAS  Google Scholar 

  6. Zhang G, Tang K, Zhang X, Xu L, Shen C, Meng Q (2021) Self-assembly of defect-free polymer-based zeolite imidazolate framework composite membranes with metal-phenolic networks for high efficient H2/CH4 separation. J Membrane Sci 617:118612

    Article  CAS  Google Scholar 

  7. Altintas C, Avci G, Daglar H, Gulcay E, Erucar I, Keskin S (2018) Computer simulations of 4240 MOF membranes for H-2/CH4 separations: insights into structure-performance relations. J Mater Chem A 6:5836–5847. https://doi.org/10.1039/c8ta01547c

    Article  CAS  Google Scholar 

  8. Fan HW, Mundstock A, Feldhoff A, Knebel A, Gu JH, Meng H, Caro J (2018) Covalent organic framework-covalent organic framework bilayer membranes for highly selective gas separation. J Am Chem Soc 140:10094–10098. https://doi.org/10.1021/jacs.8b05136

    Article  CAS  Google Scholar 

  9. Yang FF, Wu MA, Wang YC, Ashtiani S, Jiang HQ (2019) A GO-induced assembly strategy to repair MOF nanosheet-based membrane for efficient H-2/CO2 separation. Acs Appl Mater Inter 11:990–997. https://doi.org/10.1021/acsami.8b19480

    Article  CAS  Google Scholar 

  10. Li Z, Yang PP, Yan SC, Fang QR, Xue M, Qu SL (2019) A robust zeolitic imidazolate framework membrane with high H-2/CO2 separation performance under hydrothermal conditions. Acs Appl Mater Inter 11:15748–15755. https://doi.org/10.1021/acsami.9b01051

    Article  CAS  Google Scholar 

  11. Hassani N, Ghorbani-Asl M, Radha B, Drndic M, Krasheninnikov AV, Neek-Amal M (2021) Gas permeability and selectivity of a porous WS2 monolayer. J Phys Chem C 125:25055–25066. https://doi.org/10.1021/acs.jpcc.1c06894

    Article  CAS  Google Scholar 

  12. Ashirov T, Yazaydin AO, Coskun A (2022) Tuning the transport properties of gases in porous graphene membranes with controlled pore size and thickness. Adv Mater 34: 2106785. Artn 2106785. https://doi.org/10.1002/Adma.202106785

  13. Villalobos LF, Vahdat MT, Dakhchoune M, Nadizadeh Z, Mensi M, Oveisi E, Campi D, Marzari N, Agrawal KV (2020) Large-scale synthesis of crystalline g-C3N4 nanosheets and high-temperature H2 sieving from assembled films. Sci Adv 6:eaay9851

    Article  CAS  Google Scholar 

  14. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25

    Article  Google Scholar 

  15. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph Model 14:33–38. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  CAS  Google Scholar 

  16. Hummer G, Rasaiah JC, Noworyta JP (2001) Water conduction through the hydrophobic channel of a carbon nanotube. Nature 414:188–190. https://doi.org/10.1038/35102535

    Article  CAS  Google Scholar 

  17. Wang XX, Li BY, Bell DR, Li WF, Zhou RH (2017) Hydrogen and methane storage and release by MoS2 nanotubes for energy storage. J Mater Chem A 5:23020–23027. https://doi.org/10.1039/c7ta05995g

    Article  CAS  Google Scholar 

  18. Taqieddin A, Heiranian M, Aluru NR (2020) Interfacial properties of water on hydrogenated and fluorinated graphene surfaces: parametrization of nonbonded interactions. J Phys Chem C 124:21467–21475. https://doi.org/10.1021/acs.jpcc.0c05951

    Article  CAS  Google Scholar 

  19. Sun CZ, Zhu SH, Liu MC, Shen SH, Bai BF (2019) Selective molecular sieving through a large graphene nanopore with surface charges. J Phys Chem Lett 10:7188–7194. https://doi.org/10.1021/acs.jpclett.9b02715

    Article  CAS  Google Scholar 

  20. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:014101. https://doi.org/10.1063/1.2408420

    Article  CAS  Google Scholar 

  21. Darden T, York D, Pedersen L (1993) Particle mesh Ewald - An N.Log(N) method for ewald sums in large systems. J Chem Phys 98:10089–10092. https://doi.org/10.1063/1.464397

    Article  CAS  Google Scholar 

  22. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593. https://doi.org/10.1063/1.470117

    Article  CAS  Google Scholar 

  23. Hess B, Bekker H, Berendsen HJC, Fraaije J (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472. https://doi.org/10.1002/(sici)1096-987x(199709)18:12%3c1463::aid-jcc4%3e3.0.co;2-h

    Article  CAS  Google Scholar 

  24. Hassani N, Ghorbani-Asl M, Radha B, Drndić M, Krasheninnikov AV, Neek-Amal M (2021) Gas permeability and selectivity of a porous WS2 monolayer. J Phys Chem C 125:25055–25066

    Article  CAS  Google Scholar 

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Acknowledgements

We thank Shuming Zeng for helping with the manuscript.

Funding

Zonglin Gu acknowledges the support of the National Natural Science Foundation of China (No. 12104394), the Natural Science Foundation for Colleges and Universities in Jiangsu Province (No. 21KJB140024), and the Youth Hundred Talents Program of Yangzhou University. Lu Liu acknowledges the support of the Science and Technology Attack Projects of Henan Province (No. 222102310387).

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Zonglin Gu conceived the concept and designed the study. Zonglin Gu and Tian Wang carried out the theoretical calculations and analysis. Zonglin Gu, Jose Manuel Perez-Aguilar, and Lu Liu co-wrote the paper. All authors discussed the results and commented on the manuscript.

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Correspondence to Jose Manuel Perez-Aguilar or Zonglin Gu.

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Wang, T., Liu, L., Perez-Aguilar, J.M. et al. Single-layered fluorinated graphene nanopores for H2/CH4 and H2/CO2 separation with high efficiency and selectivity. J Mol Model 28, 403 (2022). https://doi.org/10.1007/s00894-022-05400-8

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