Visible-Light Driven Photocatalytic Degradation of 4-Chlorophenol Using Graphitic Carbon Nitride-Based Nanocomposites
Bibliographic record
Abstract
4-chlorophenol (4-CP), a hydroxylated aromatic compound (HAC), is a recalcitrant and toxic organic pollutant found in industrial wastewater and various environmental media. In this paper, visible-light-activated photocatalysis using graphitic carbon nitride (GCN) was used to treat 4-CP in an aqueous media. Graphitic carbon nitride from different precursors (dicyanamide, urea, and melamine), as well as GCN/silver nanocomposites (AgBr, Ag3PO4, Ag2CrO4, and Ag), were successfully synthesized and characterized by BET, XRD, SEM, EDS, and UV-Vis DRS. The band gaps of the photocatalysts were estimated using the UV-Vis DRS characterization results and Tauc plots. The evaluation of the efficacy of the GCN-based catalysts in degrading 4-CP was conducted with different photoreactors such as a royal blue light-emitting diode (LED), a UV-A LED, LUZCHEM cool white lamps, and a solar simulator. The results showed that GCNs with royal blue LED can effectively degrade 4-CP from aqueous media. Among the different precursors, urea-derived GCN showed the best performance in degrading 4-CP due to its large surface area. GCN/0.3Ag2CrO4 nanocomposite showed a synergistic effect for the enhanced photocatalytic degradation of 4-CP. The degradation of 4-CP with a rate constant of 2.64 × 10−2 min−1 was achieved with a GCN/0.3Ag2CrO4 nanocomposite under royal blue LED irradiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".