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 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 | 0.003 |
| 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".