Mapping Photogenerated Electron–Hole Behavior of Graphene Oxide: Insight into a New Mechanism of Photosensitive Pollutant Degradation
Bibliographic record
Abstract
The use of graphene oxide (GO) photogenerated electron–hole (e–h+) pairs to degrade pollutants is a novel green method for wastewater treatment. However, the interaction between photosensitive pollutants and a GO–light system remains unclear. In this work, the mechanism of degradation of photosensitive pollutant tetracycline (TC) promoted by GO photogenerated e–h+ pairs was studied. Our studies encompassed the determination of TC removal kinetics, analysis of active substances for TC degradation, identification of degradation products, and computational modeling. Clear evidence shows that a new reaction mechanism of enhanced adsorption and induced generation of reactive oxygen species (ROS) was involved. This mechanism was conducive to significantly enhanced TC removal. Kinetic studies showed a first-order behavior that can be well described by the Langmuir–Hinshelwood model. Radical scavenging experiments confirmed that 1O2, •O2−, and holes (h+) were the main active substances for TC degradation. Electron spin resonance analysis indicated that photoexcited TC molecules may transfer electrons to the conduction band of GO to induce the generation of additional ROS. A major transformation product (m/z 459) during TC degradation was identified with liquid chromatography–mass spectrometry. Density functional theory calculation indicated a stronger adsorption between TC and GO under photoirradiation. This mechanism of photo-enhanced adsorption and synergistic induced generation of ROS provides a new strategy for the removal of emerging pollutants in water. Overall, the new mechanism revealed in this work expands the knowledge of applying GO to wastewater treatment and is of great reference value for research in this field.
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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) | 0.001 | 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".