Mechanism of Photocatalytic Degradation of Diclofenac in Water by a Novel Carbon Nitride
[Purposes] Carbon nitrides in graphite phase (g-C3N4, GCN) have been widely used in photocatalytic degradation of organic pollutants in water. However, traditional GCN is limited in practical application because of its low specific surface area and few active sites. [Methods] In this study, photocat...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Journal of Taiyuan University of Technology
2025-01-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2365.html |
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| Summary: | [Purposes] Carbon nitrides in graphite phase (g-C3N4, GCN) have been widely used in photocatalytic degradation of organic pollutants in water. However, traditional GCN is limited in practical application because of its low specific surface area and few active sites. [Methods] In this study, photocatalyst (PCN) was prepared by thermal polymerization method by using urea as precursor. PCN was characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy. Diclofenac (DCF) was selected as target pollutant to study photocatalytic degradation. [Findings] The results show that the specific surface area of PCN (133.963 m2/g) is significantly higher than that of GCN (2.605 m2/g), about 67 times that of GCN. PCN (0.20 g/L) can achieve 100% catalytic degradation of DCF (10 mg/L) within 10 min at pH of 5. Free radical quenching experiments confirm that superoxide radical (•O2−) and hydroxyl radical (•OH) are the primary reactive oxygen species. The degradation pathway of DCF is determined by the Fukui index-based density functional theory and the determination of intermediates. The experimental results show that PCN has high stability and has promising applications in the field of photocatalytic degradation of emerging pollutants in water. |
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| ISSN: | 1007-9432 |