Catalytic Hydrogenation of CO2 to Formate Using Ruthenium Nanoparticles Immobilized on Supported Ionic Liquid Phases
Source abstract
Abstract
Abstract Ruthenium nanoparticles (NPs) immobilized on imidazolium‐based supported ionic liquid phases (Ru@SILP) act as effective heterogeneous catalysts for the hydrogenation of carbon dioxide (CO2) to formate in a mixture of water and triethylamine (NEt3). The structure of the imidazolium‐based molecular modifiers is varied systematically regarding side chain functionality (neutral, basic, and acidic) and anion to assess the influence of the IL‐type environment on the NPs synthesis and catalytic properties. The resulting Ru@SILP materials contain well‐dispersed Ru NPs with diameters in the range 0.8–2.9 nm that are found 2 to 10 times more active for CO2 hydrogenation than a reference Ru@SiO2 catalyst under identical conditions. Introduction of sulfonic acid groups in the IL modifiers results in a greatly increased turnover number (TON) and turnover frequency (TOF) at reduced metal loadings. As a result, excellent productivity with TONs up to 16 100 at an initial TOF of 1430 h−1 can be achieved with the Ru@SILP(SO3H‐OAc) catalyst. H/D exchange and other control experiments suggest an accelerated desorption of the formate species from the Ru NPs promoted by the presence of ammonium sulfonate species on Ru@SILP(SO3H‐X) materials, resulting in enhanced catalyst activity and productivity.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Carbon dioxide utilization in catalysis · Catalysts for Methane Reforming · CO2 Reduction Techniques and Catalysts
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Max Planck Institute for Chemical Energy Conversion Stiftstraße 34–36 45470 Mülheim an der Ruhr Germany
Institut für Technische und Makromolekulare Chemie RWTH Aachen University Worringerweg 2 52074 Aachen Germany
Funding
Deutsche Forschungsgemeinschaft
References (57)
Show source-provided references
1. 10.1021/cr400275810.1021/cr4002758 ↗
2. 10.1126/science.aay306010.1126/science.aay3060 ↗
3. 10.1039/c3ee00056g10.1039/c3ee00056g ↗
4. 10.1021/acs.chemrev.6b0081610.1021/acs.chemrev.6b00816 ↗
5. 10.1039/D0CS01331E10.1039/D0CS01331E ↗
6. 10.1098/rsta.2015.031510.1098/rsta.2015.0315 ↗
7. 10.1002/anie.20150745810.1002/anie.201507458 ↗
8. 10.1002/ange.20150745810.1002/ange.201507458 ↗
9. 10.1002/anie.19952207110.1002/anie.199522071 ↗
10. 10.1039/c2ee21928j10.1039/c2ee21928j ↗
11. 10.1021/acs.chemrev.7b0043510.1021/acs.chemrev.7b00435 ↗
12. 10.1021/acs.chemrev.5b0019710.1021/acs.chemrev.5b00197 ↗
13. 10.1021/acs.chemrev.7b0018210.1021/acs.chemrev.7b00182 ↗
14. 10.1021/ja903574e10.1021/ja903574e ↗
15. 10.1002/cctc.20140211910.1002/cctc.201402119 ↗
16. 10.1002/cssc.20160181410.1002/cssc.201601814 ↗
17. 10.1038/s41467-020-20291-010.1038/s41467-020-20291-0 ↗
18. 10.1039/D0EE03575K10.1039/D0EE03575K ↗
19. 10.1039/C5QI00231A10.1039/C5QI00231A ↗
20. 10.1016/j.chempr.2018.12.01410.1016/j.chempr.2018.12.014 ↗
21. 10.1039/C9GC03685G10.1039/C9GC03685G ↗
22. 10.1021/jacs.8b0404710.1021/jacs.8b04047 ↗
23. 10.1021/acs.iecr.9b0065410.1021/acs.iecr.9b00654 ↗
24. 10.1021/jacs.8b0485210.1021/jacs.8b04852 ↗
25. 10.1021/acscatal.7b0380410.1021/acscatal.7b03804 ↗
26. 10.1002/cctc.20190116710.1002/cctc.201901167 ↗
27. 10.1002/anie.20200896210.1002/anie.202008962 ↗
28. 10.1002/ange.20200896210.1002/ange.202008962 ↗
29. 10.1002/cssc.20150137610.1002/cssc.201501376 ↗
30. 10.1021/acscatal.7b0409910.1021/acscatal.7b04099 ↗
31. 10.1016/j.cattod.2010.05.03410.1016/j.cattod.2010.05.034 ↗
32. 10.1016/j.mcat.2019.11046110.1016/j.mcat.2019.110461 ↗
33. 10.1038/s41467-017-01673-310.1038/s41467-017-01673-3 ↗
34. 10.1021/acs.accounts.1c0001310.1021/acs.accounts.1c00013 ↗
35. 10.1039/C4CS00172A10.1039/C4CS00172A ↗
36. 10.1021/acs.chemrev.9b0023010.1021/acs.chemrev.9b00230 ↗
37. 10.1021/acscatal.9b0512410.1021/acscatal.9b05124 ↗
38. 10.1021/acscatal.0c0355910.1021/acscatal.0c03559 ↗
39. 10.1002/anie.20180663810.1002/anie.201806638 ↗
40. 10.1002/ange.20180663810.1002/ange.201806638 ↗
41. 10.1002/anie.20191638510.1002/anie.201916385 ↗
42. 10.1002/ange.20191638510.1002/ange.201916385 ↗
43. 10.1038/s41557-021-00735-w10.1038/s41557-021-00735-w ↗
44. 10.1002/smll.20200668310.1002/smll.202006683 ↗
45. 10.1002/9783527693283.ch1210.1002/9783527693283.ch12 ↗
46. Haumann M. · 2014
47. 10.1021/acscatal.7b0393110.1021/acscatal.7b03931 ↗
48. 10.1016/j.jcat.2019.10.01310.1016/j.jcat.2019.10.013 ↗
49. 10.1021/jacs.8b0850510.1021/jacs.8b08505 ↗
50. 10.1039/C4SC02033B10.1039/C4SC02033B ↗
51. 10.1002/ange.20070448710.1002/ange.200704487 ↗
52. 10.1016/j.ejmech.2014.07.08610.1016/j.ejmech.2014.07.086 ↗
53. 10.1039/b812607k10.1039/b812607k ↗
54. 10.1039/c0gc00472c10.1039/c0gc00472c ↗
55. 10.1063/1.508460310.1063/1.5084603 ↗
56. 10.1088/1742-6596/430/1/01200710.1088/1742-6596/430/1/012007 ↗
57. c)M.Newville T.Stensitzki D. B.Allen A.Ingargiola LMFIT: NonLinear Least‐Square Minimization and Curve‐Fitting for Python (0.8.0). Zenodo 2014.