Enhanced Solar Photocatalytic Degradation of Phenol with Coupled Graphene-Based Titanium Dioxide and Zinc Oxide
Bibliographic record
Abstract
In this study, graphene-based titanium dioxide and zinc oxide composites (TiO 2 -G, ZnO-G) were synthesized using a hydrothermal process. Materials were characterized using X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, UV–vis spectroscopy, scanning electron microscopy, and transmission electron microscopy. Photocatalytic activity of the composite materials under simulated solar light was studied using phenol as a model compound. A ca. 30% improvement on the degradation performance by the TiO 2 -G composite (and ZnO-G) was observed when reaction rate constants were compared with TiO 2 (and ZnO) only. This demonstrates the positive effect of graphene on suppressing charge recombination and extending the light absorption range. Further improvement on the photocatalytic degradation rate of phenol was obtained by coupling the two composites, ZnO-G and TiO 2 -G. This is attributed to more efficient charge separation and longer lifetime of the charge carriers, which eventually enhances the photocatalytic activity. The optimum stoichiometric amount of each component was obtained experimentally. Systematic parametric studies were also performed to study the effect of catalyst loading, initial phenol concentration, solution pH, and solar light intensity. Complete solar degradation of 40 ppm phenol was achieved within 60 min while using the coupled ZnO-G/TiO 2 -G photocatalysts at the optimum conditions.
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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".