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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".