Water–Gas Shift Reaction to Capture Carbon Dioxide and Separate Hydrogen on Single-Walled Carbon Nanotubes
Source abstract
Abstract
In view of the increasingly severe global warming and ocean acidification caused by CO2 emissions, we report a new procedure, named “reactive separation”, to capture CO2. We used advanced Monte Carlo and molecular dynamics methods to simulate the water–gas shift reaction in single-walled carbon nanotubes. We found that (11,11) carbon nanotubes with a diameter of 0.75 nm have the best ability to capture CO2 generated in the water–gas shift reaction. When the feed water–gas ratio is 1:1, the pressure is 3 MPa, and the temperature is 473 K, the storage capacity of CO2 reaches 2.18 mmol/g, the molar fraction of CO2 and H2 inside the carbon nanotube is 0.87 and 0.09, respectively, the conversion of CO in the pore is as high as 97.6%, and the CO2/H2 separation factor is 10.3. Therefore, utilizing the reaction and separation coupling effect of carbon nanotubes to adsorb and store the product CO2 formed in the water–gas shift reaction, while separating the generated clean energy gas H2, is a promising strategy for developing novel CO2 capture technologies.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Carbon Dioxide Capture Technologies · Membrane Separation and Gas Transport · Zeolite Catalysis and Synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
Materials Simulation and Modelling, Department of Applied Physics, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Funding
National Natural Science Foundation of China
References (49)
Show source-provided references
1. 10.1002/anie.20100043110.1002/anie.201000431 ↗
2. 10.1002/aic.1407810.1002/aic.14078 ↗
3. 10.1039/c9ee02873k10.1039/c9ee02873k ↗
4. 10.1039/c0ee00558d10.1039/c0ee00558d ↗
5. 10.1016/s0926-860x(99)00349-x10.1016/s0926-860x(99)00349-x ↗
6. 10.1016/s0926-860x(02)00529-x10.1016/s0926-860x(02)00529-x ↗
7. 10.1016/s0926-860x(98)00280-410.1016/s0926-860x(98)00280-4 ↗
8. 10.1002/apj.36410.1002/apj.364 ↗
9. 10.1016/s0920-5861(02)00246-810.1016/s0920-5861(02)00246-8 ↗
10. 10.1016/j.apenergy.2019.11407810.1016/j.apenergy.2019.114078 ↗
11. 10.1021/ie00051a02210.1021/ie00051a022 ↗
12. 10.1016/j.desal.2007.10.04210.1016/j.desal.2007.10.042 ↗
13. 10.1088/0034-4885/62/12/20110.1088/0034-4885/62/12/201 ↗
14. 10.1038/s41467-017-00620-610.1038/s41467-017-00620-6 ↗
15. 10.1021/jp004368u10.1021/jp004368u ↗
16. 10.1080/0026897940010048110.1080/00268979400100481 ↗
17. 10.1063/1.46644310.1063/1.466443 ↗
18. 10.1038/354056a010.1038/354056a0 ↗
19. 10.1063/1.132875610.1063/1.1328756 ↗
20. 10.1063/1.143159010.1063/1.1431590 ↗
21. 10.1063/1.178203110.1063/1.1782031 ↗
22. 10.1016/j.jcis.2007.02.00910.1016/j.jcis.2007.02.009 ↗
23. 10.1021/jp211563e10.1021/jp211563e ↗
24. 10.1002/aic.1470710.1002/aic.14707 ↗
25. 10.1063/1.151457210.1063/1.1514572 ↗
26. 10.1063/1.44586910.1063/1.445869 ↗
27. 10.1021/acs.jpcc.8b0914310.1021/acs.jpcc.8b09143 ↗
28. 10.1021/acsanm.9b0163310.1021/acsanm.9b01633 ↗
29. 10.1002/aic.69047071910.1002/aic.690470719 ↗
30. 10.1063/1.46685410.1063/1.466854 ↗
31. 10.1080/0026897011008725410.1080/00268970110087254 ↗
32. 10.1063/1.188410810.1063/1.1884108 ↗
33. McQuarrie D. A. · 1976
34. 10.1021/es071306+10.1021/es071306+ ↗
35. 10.1021/es104367210.1021/es1043672 ↗
36. Páll, S.; Abraham, M. J.; Kutzner, C.; Hess, B.; Lindahl, E. Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS BT-Solving Software Challenges for Exascale; Markidis, S., Laure, E., Eds. Springer International Publishing: Cham, 2015; pp 3–27.10.1007/978-3-319-15976-8_1 ↗
37. 10.1007/s00894010004510.1007/s008940100045 ↗
38. 10.1016/0010-4655(95)00042-e10.1016/0010-4655(95)00042-e ↗
39. 10.1093/bioinformatics/btt05510.1093/bioinformatics/btt055 ↗
40. 10.1002/jcc.2029110.1002/jcc.20291 ↗
41. 10.1016/j.softx.2015.06.00110.1016/j.softx.2015.06.001 ↗
42. 10.1021/ct700301q10.1021/ct700301q ↗
43. 10.1103/physreva.31.169510.1103/physreva.31.1695 ↗
44. 10.1080/0026897840010120110.1080/00268978400101201 ↗
45. 10.1021/i160057a01110.1021/i160057a011 ↗
46. 10.1039/c2ee21935b10.1039/c2ee21935b ↗
47. 10.1021/la901156z10.1021/la901156z ↗
48. 10.1016/s1003-9953(06)60030-310.1016/s1003-9953(06)60030-3 ↗
49. 10.1038/s41467-018-07937-w10.1038/s41467-018-07937-w ↗