journal article·2025-12-17

Revealing Selectivity in Nanozyme Reactions via OH Adsorption: A Descriptor-Guided Mechanism for H 2 O 2 Activation on Doped Carbon Nanotubes

Zhicheng Xiong, Ziyu Shen, Leyun Li, Yuxin Wan, Xingfa Gao, Junnan Li, Shengliang Zhong, Xuejiao J. Gao

ACS Applied Materials & Interfaces· Volume 17· Issue 50
Publisher page

Abstract

High Resolution Image Download MS PowerPoint Slide Controlling the catalytic selectivity of nanozymes remains a major challenge in redox nanomedicine. Although various carbon-based materials have been developed as enzyme-mimicking catalysts for H 2 O 2 decomposition, doped carbon nanotubes (CNTs) remain underexplored, especially regarding their ability to direct peroxidase (POD) or catalase (CAT) pathways. Here, we conduct a systematic first-principles study of H 2 O 2 activation on 70 single-walled CNTs with varying chiralities, diameters, and dopants (B, N, P, S). By calculating adsorption energies of key intermediates (H*, O*, OH*, OOH*), we establish strong linear correlations between E ads,OH and the reaction energies of different H 2 O 2 decomposition pathways. We propose E ads,OH as a unified descriptor that predicts both activity and selectivity. N-doped CNTs favor OH* formation, while B- and N,P- co -doped CNTs promote O* intermediates─demonstrating two distinct POD-like mechanisms. This difference originates from dopant-dependent shifts in the p -band center, which tune adsorption strengths and reaction thermodynamics. Several doped CNTs, including B-doped CNT(5,5) and N,P- co -doped CNT(6,0), show strong POD selectivity, whereas pristine armchair CNTs prefer CAT-like behavior. Our work reveals how dopant and geometric modulation of CNTs govern catalytic selectivity at the molecular level. These insights not only clarify H 2 O 2 reaction pathways on carbon nanozymes but also provide a descriptor-guided framework for designing selective and high-performance nanocatalysts.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Nanomaterials in Catalysis · Enzyme-mediated dye degradation · Enzyme Catalysis and Immobilization

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

Affiliations

Jiangxi Province Key Laboratory of Porous Functional Materials, College of Chemistry and Materials

Jiangxi Normal University

Laboratory of Theoretical and Computational Nanoscience

National Center for Nanoscience and Technology of China

CountryBeijing Medical & Health Sci-tech Development Center

Funding

National Key Research and Development Program of China · Narodowe Centrum Nauki · National Natural Science Foundation of China-Liaoning Joint Fund · National Natural Science Foundation of China · Natural Science Foundation of Jiangxi Province · Gan Poyang talents support program · Thousand Talents Plan of Jiangxi Province

References (67)

Show source-provided references
1. 10.1021/acs.accounts.9b0014010.1021/acs.accounts.9b00140 ↗
2. 10.1039/c3cs35486e10.1039/c3cs35486e ↗
3. 10.1039/c8cs00457a10.1039/c8cs00457a ↗
4. 10.1038/nnano.2007.26010.1038/nnano.2007.260 ↗
5. 10.1038/nrm225610.1038/nrm2256 ↗
6. 10.1021/acs.chemrev.8b0062610.1021/acs.chemrev.8b00626 ↗
7. 10.1039/d3nr05592b10.1039/d3nr05592b ↗
8. 10.1002/smll.20220340010.1002/smll.202203400 ↗
9. 10.1021/acs.langmuir.4c0248110.1021/acs.langmuir.4c02481 ↗
10. 10.1021/acssuschemeng.2c0737110.1021/acssuschemeng.2c07371 ↗
11. 10.1097/ot9.000000000000010110.1097/ot9.0000000000000101 ↗
12. 10.1016/j.ccr.2025.21676810.1016/j.ccr.2025.216768 ↗
13. 10.1002/adhm.20240163010.1002/adhm.202401630 ↗
14. 10.1016/j.ccr.2022.21489610.1016/j.ccr.2022.214896 ↗
15. 10.26599/nbe.2024.929005310.26599/nbe.2024.9290053 ↗
16. 10.1016/j.cej.2025.16076210.1016/j.cej.2025.160762 ↗
17. 10.1016/j.biomaterials.2025.12346510.1016/j.biomaterials.2025.123465 ↗
18. 10.1021/acs.jpclett.1c0321910.1021/acs.jpclett.1c03219 ↗
19. 10.1007/s12274-021-3685-410.1007/s12274-021-3685-4 ↗
20. 10.1021/acs.analchem.2c0423910.1021/acs.analchem.2c04239 ↗
21. 10.1016/j.apsusc.2022.15371510.1016/j.apsusc.2022.153715 ↗
22. 10.1016/j.nantod.2020.10093510.1016/j.nantod.2020.100935 ↗
23. 10.1021/acs.analchem.9b0433310.1021/acs.analchem.9b04333 ↗
24. 10.1021/acsanm.1c0121510.1021/acsanm.1c01215 ↗
25. 10.1016/j.jcis.2022.03.02810.1016/j.jcis.2022.03.028 ↗
26. 10.1016/j.cej.2023.14821610.1016/j.cej.2023.148216 ↗
27. 10.1016/j.snb.2024.13624610.1016/j.snb.2024.136246 ↗
28. 10.1021/acs.analchem.0c0298210.1021/acs.analchem.0c02982 ↗
29. 10.1021/acsanm.3c0465010.1021/acsanm.3c04650 ↗
30. 10.1021/acs.chemmater.8b0272610.1021/acs.chemmater.8b02726 ↗
31. 10.1021/acsnano.3c0768010.1021/acsnano.3c07680 ↗
32. 10.1016/j.colsurfb.2025.11492510.1016/j.colsurfb.2025.114925 ↗
33. 10.1016/j.nantod.2021.10131710.1016/j.nantod.2021.101317 ↗
34. 10.1002/anie.20231677910.1002/anie.202316779 ↗
35. 10.1002/adma.20221115110.1002/adma.202211151 ↗
36. 10.1021/acs.accounts.3c0033910.1021/acs.accounts.3c00339 ↗
37. 10.1002/adma.20240779310.1002/adma.202407793 ↗
38. 10.1039/C9NR03473K10.1039/C9NR03473K ↗
39. 10.1039/d0tb01765e10.1039/d0tb01765e ↗
40. 10.1002/adfm.20210519810.1002/adfm.202105198 ↗
41. 10.1016/j.apcatb.2023.12246910.1016/j.apcatb.2023.122469 ↗
42. 10.1016/j.fochx.2024.10209310.1016/j.fochx.2024.102093 ↗
43. 10.1016/j.jcis.2022.05.16710.1016/j.jcis.2022.05.167 ↗
44. 10.1016/j.jelechem.2022.11693210.1016/j.jelechem.2022.116932 ↗
45. 10.1016/j.microc.2022.10818410.1016/j.microc.2022.108184 ↗
46. 10.1007/s12274-023-5393-810.1007/s12274-023-5393-8 ↗
47. 10.1002/smll.20240573610.1002/smll.202405736 ↗
48. 10.1002/adfm.20242608410.1002/adfm.202426084 ↗
49. 10.1007/s12274-023-6138-410.1007/s12274-023-6138-4 ↗
50. 10.1039/d3mh01053h10.1039/d3mh01053h ↗
51. 10.1002/adfm.20100208610.1002/adfm.201002086 ↗
52. 10.1016/0927-0256(96)00008-010.1016/0927-0256(96)00008-0 ↗
53. 10.1103/physrevb.59.175810.1103/physrevb.59.1758 ↗
54. 10.1103/physrevb.50.1795310.1103/physrevb.50.17953 ↗
55. 10.1002/jcc.2175910.1002/jcc.21759 ↗
56. 10.1063/1.338234410.1063/1.3382344 ↗
57. 10.1103/physrevlett.77.386510.1103/physrevlett.77.3865 ↗
58. 10.1103/physrevb.13.518810.1103/physrevb.13.5188 ↗
59. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J. Gaussian 09 , Revision. D.01; Gaussian,Inc 2009; Wallingford, CT.
60. 10.1063/1.46044710.1063/1.460447 ↗
61. 10.1063/1.45506410.1063/1.455064 ↗
62. 10.1021/jp810292n10.1021/jp810292n ↗
63. 10.1103/physrevb.46.180410.1103/physrevb.46.1804 ↗
64. 10.1016/j.surfin.2022.10181510.1016/j.surfin.2022.101815 ↗
65. 10.1002/adfm.20231349110.1002/adfm.202313491 ↗
66. 10.1142/s021798492250189510.1142/s0217984922501895 ↗
67. 10.1039/D3CP04143C10.1039/D3CP04143C ↗