Experimental and DFT Insights into Enhanced Charge Transfer and Efficient Photocatalytic Performance of Tubular g-C3N4/BiVO4{001} Heterojunctions
Source abstract
Abstract
Research on the photocatalytic properties of semiconductor photocatalysts is critical for addressing environmental pollution and energy crisis. In this study, the optimal process of hydrothermal preparation of BiVO 4 was determined, and BiVO 4 with highly exposed {001} crystal facets was prepared. The photocatalytic properties of g-C 3 N 4 (Graphite-phase carbon nitride) nanomaterials with different morphologies were prepared and investigated based on DFT (Density Functional Theory). Among these, tubular g-C 3 N 4 (TCN), exhibiting superior photocatalytic activity, was selected to fabricate TCN/BiVO 4 nanocomposites with varying TCN contents. These composites demonstrated narrower band gaps compared to individual TCN and BiVO 4 {001} photocatalysts, along with enhanced electrical and optical properties. Notably, the 3TCN/BiVO 4 nanocomposite exhibited an apparent reaction rate constant for MB (Methylene Blue) degradation of 0.042 min –1, which is 4.5 times that of pristine TCN (0.0093 min –1 ) and 2.3 times that of BiVO 4 {001} (0.018 min –1 ). Theoretical calculations confirm that TCN/BiVO 4 forms a structurally stable van der Waals heterojunction. The photocatalytic mechanism analysis revealed that the heterojunction suppresses the recombination of photogenerated electron–hole pairs, while the built-in electric field facilitates carrier transfer, thereby significantly improving photocatalytic performance. Additionally, repeatability and stability tests were conducted on the synthesized materials to assess their potential for practical applications. This study provides a foundation for further exploration and optimization of these materials for environmental and energy-related applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Photocatalysis Techniques · Perovskite Materials and Applications · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Physics and Electronic Information
Yan’an University
Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data
School of Information Science Technology
Northwest University
Funding
National Natural Science Foundation of China · Yan'an University
References (47)
Show source-provided references
1. 10.1016/j.jallcom.2023.17143210.1016/j.jallcom.2023.171432 ↗
2. 10.1016/j.checat.2023.10051410.1016/j.checat.2023.100514 ↗
3. 10.1016/j.molliq.2023.12189010.1016/j.molliq.2023.121890 ↗
4. 10.1021/acsnano.0c0708310.1021/acsnano.0c07083 ↗
5. 10.1016/S1872-2067(19)63448-010.1016/S1872-2067(19)63448-0 ↗
6. 10.1002/cctc.20160165910.1002/cctc.201601659 ↗
7. 10.1016/j.jwpe.2024.10625710.1016/j.jwpe.2024.106257 ↗
8. 10.1016/j.ijhydene.2023.11.21610.1016/j.ijhydene.2023.11.216 ↗
9. 10.1016/j.seppur.2024.13047210.1016/j.seppur.2024.130472 ↗
10. 10.1016/j.ceramint.2021.08.06310.1016/j.ceramint.2021.08.063 ↗
11. 10.1016/j.apcatb.2017.01.06110.1016/j.apcatb.2017.01.061 ↗
12. 10.1016/j.chemosphere.2022.13383410.1016/j.chemosphere.2022.133834 ↗
13. 10.1016/j.ijhydene.2024.10.41510.1016/j.ijhydene.2024.10.415 ↗
14. 10.1016/j.molliq.2024.12507110.1016/j.molliq.2024.125071 ↗
15. 10.1016/j.mtphys.2023.10108010.1016/j.mtphys.2023.101080 ↗
16. 10.3390/coatings1311190910.3390/coatings13111909 ↗
17. 10.1016/j.seppur.2023.12496610.1016/j.seppur.2023.124966 ↗
18. 10.1016/j.scitotenv.2023.16531610.1016/j.scitotenv.2023.165316 ↗
19. 10.1016/j.cej.2020.12840010.1016/j.cej.2020.128400 ↗
20. 10.1016/j.nanoen.2023.10913710.1016/j.nanoen.2023.109137 ↗
21. 10.1016/j.chemosphere.2021.13273510.1016/j.chemosphere.2021.132735 ↗
22. 10.1016/j.apmt.2021.10111810.1016/j.apmt.2021.101118 ↗
23. 10.1016/j.mseb.2023.11627810.1016/j.mseb.2023.116278 ↗
24. 10.1016/j.jwpe.2024.10614510.1016/j.jwpe.2024.106145 ↗
25. 10.3390/catal1104042610.3390/catal11040426 ↗
26. 10.1016/j.seppur.2022.12050710.1016/j.seppur.2022.120507 ↗
27. 10.1016/j.vacuum.2020.10945810.1016/j.vacuum.2020.109458 ↗
28. 10.1016/j.ijhydene.2021.04.06010.1016/j.ijhydene.2021.04.060 ↗
29. 10.1016/j.chemphys.2024.11242610.1016/j.chemphys.2024.112426 ↗
30. 10.1016/j.jallcom.2021.16321410.1016/j.jallcom.2021.163214 ↗
31. 10.1016/j.envres.2023.11784810.1016/j.envres.2023.117848 ↗
32. 10.1016/j.ceramint.2021.02.20410.1016/j.ceramint.2021.02.204 ↗
33. 10.1016/j.apsusc.2019.14441710.1016/j.apsusc.2019.144417 ↗
34. 10.1016/j.jssc.2021.12229110.1016/j.jssc.2021.122291 ↗
35. 10.1016/j.apcatb.2018.04.05610.1016/j.apcatb.2018.04.056 ↗
36. 10.1016/j.seppur.2019.11593710.1016/j.seppur.2019.115937 ↗
37. 10.1016/j.jtice.2018.10.01110.1016/j.jtice.2018.10.011 ↗
38. 10.1016/j.apsusc.2018.12.13510.1016/j.apsusc.2018.12.135 ↗
39. 10.1021/acs.jpcc.8b0626410.1021/acs.jpcc.8b06264 ↗
40. 10.1016/j.mseb.2022.11567610.1016/j.mseb.2022.115676 ↗
41. 10.1016/j.apcatb.2018.04.02610.1016/j.apcatb.2018.04.026 ↗
42. 10.1016/j.mtener.2021.10082910.1016/j.mtener.2021.100829 ↗
43. 10.1016/j.mtsust.2024.10104310.1016/j.mtsust.2024.101043 ↗
44. 10.1016/j.coche.2024.10105410.1016/j.coche.2024.101054 ↗
45. 10.1039/D4EN00955J10.1039/D4EN00955J ↗
46. 10.1016/j.materresbull.2021.11129010.1016/j.materresbull.2021.111290 ↗
47. 10.1016/j.molstruc.2024.13824810.1016/j.molstruc.2024.138248 ↗