Porous Graphitic Carbon Nitride-Based Photocatalysts for Antibiotic Degradation
Bibliographic record
Abstract
Review Porous Graphitic Carbon Nitride-Based Photocatalysts for Antibiotic Degradation Zhaoqiang Wang 1, Guixiang Ding 1, Juntao Zhang 1, Peng Wang 2, Qi Lv 3, Yonghao Ni 4 and Guangfu Liao 1,* 1 National Forestry and Grassland Administration Key Laboratory of Plant Fiber Functional Materials, College of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China 2 Shandong Chambroad Petrochemicals Co., Ltd., Binzhou 256500, China 3 PCFM Lab, GD HPPC Lab, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China 4 Department of Chemical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada * Correspondence: liaogf@mail2.sysu.edu.cn Received: 29 November 2023; Revised: 18 January 2024; Accepted: 24 January 2024; Published: 5 March 2024 Abstract: Photocatalytic technology is a promising strategy for solving antibiotic pollution present in the water system. Porous carbon nitride (PCN) material has been considered as a potential candidate to solve the above problem due to the abundant reaction sites, large specific surface area and narrow band gap. Recently, substantial research focus on promoting photocatalytic activity of PCN-based material via improving photogenerated carrier separation and band gap structure has been completed. However, only a few works summarize and discuss the results of research on photocatalytic antibiotic degradation by PCN-based photocatalysts in recent years. Thus, a review on recent developments in PCN-based photocatalysts research is urgently needed to further promote its advancement. In this review, the synthesis strategies, structure design and photocatalytic application of antibiotic degradation over PCN-based photocatalysts are listed in detail. Finally, a brief conclusion has been discussed deeply, which focuses on the future challenges and expectations of PCN-based photocatalysts for photocatalytic antibiotic degradation. This review offers a novel viewpoint on the use of PCN-based material in photocatalytic antibiotic degradation and highlights its significant potential as a photocatalyst. In short, the application of PCN-based materials in the photocatalytic degradation of antibiotics is very promising, according to objective assessments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".