COMPONENT OPTIMIZATION OF g-C3N4/TiO2 HETERO- STRUCTURE COMPOSITE AND ITS ACTIVITY ENHANCEMENT FOR PHOTOCATALYTIC DEGRADATION OF RHODAMINE B
In this study, g-C3N4/TiO2 heterostructure composites were synthesized using sol–gel and hydrothermal methods. Through X-ray powder diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), the crystalline phase structure and chemical composition of the composite were confirmed. Scanning electron microscopy (SEM) revealed that the g-C3N4 sheets were uniformly dispersed on the block TiO2 particles. Ultraviolet–visible diffuse reflectance spectroscopy (UV–Vis/DRS) indicated that the incorporation of g-C3N4 resulted in a narrowing of the forbidden bandwidths of the composites. Photoluminescence (PL) spectroscopy and electrochemical impedance spectroscopy (EIS) further demonstrated that the g-C3N4/TiO2 heterostructure effectively suppressed the recombination of photogenerated charge carriers. To evaluate the photocatalytic performance of g-C3N4/TiO2 heterojunction composites, different g-C3N4 loadings were tested for degradation of rhodamine B (RhB). The 7.62 w/% g-C3N4/TiO2 heterojunction composite exhibited the highest degradation efficiency for RhB dye, with a constant degradation rate that was twice as high as that of pure TiO2. Additionally, experiments on free radical trapping of the composite revealed the crucial role of hydroxyl radicals (·OH) and photogenerated holes (h+) in the RhB degradation. Moreover, the formation of a Z-scheme heterojunction between g-C3N4 and TiO2 was the main factor in further enhancing the degradation activity of the composite.
Authors
- Sitian Chen
- Zikang Cao (ORCID: https://orcid.org/0009-0006-8104-0267)
- Yan Yu (ORCID: https://orcid.org/0000-0003-4031-252X)
- Li Li (ORCID: https://orcid.org/0009-0004-4593-7929)
- Yating Liu
- Luqi Wang (ORCID: https://orcid.org/0000-0003-1256-0219)
- Xu Gao (ORCID: https://orcid.org/0000-0003-3146-5487)
- Sitian Cheng
Institutions
- Qiqihar University (CN)
Publication Details
- Journal
- Materiali in tehnologije
- Published
- 2025-02-06
- DOI
- https://doi.org/10.17222/mit.2024.1251
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Qiqihar University
- Division of Graduate Education