Efficient Photocatalytic Degradation of Aqueous Atrazine over Graphene-Promoted g-C3N4 Nanosheets
Bibliographic record
Abstract
Atrazine is a systemic herbicide widely used in weed control. In recent years, it has been largely detected in surface and groundwater in several locations all over the world. Photocatalysis is a green and sustainable technology with huge application prospects in pollution control and the degradation of organic water pollutants. In this work, photodegradation of aqueous atrazine was investigated over pristine graphitic carbon nitride (g-C3N4) synthesized via urea pyrolysis and graphene/g-C3N4 composite synthesized via the in situ growth method involving direct deposition of g-C3N4 nanosheets on the graphene surface. The obtained photocatalysts were characterized using transmission and scanning electron microscopy, Fourier-transformed infrared spectroscopy, UV-visible spectroscopy, photoluminescence spectroscopy, X-ray diffraction, and surface area measurements. It was demonstrated that the composite material exhibited remarkable photocatalytic properties for the efficient degradation of aqueous atrazine under visible light at ambient temperature. After 5 h of reaction, atrazine conversion reached 100% in the presence of graphene/g-C3N4 composite, while the pristine g-C3N4 allowed 40% conversion under the same conditions, thus demonstrating the positive effect of graphene on the photocatalytic activity of g-C3N4. Moreover, graphene/g-C3N4 was shown to keep its activity even when it was recycled five times, thus proving its stability and its potential to be used at the industrial scale.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".