Shape controllers enhance the efficiency of graphene–TiO<sub>2</sub>hybrids in pollutant abatement
Bibliographic record
Abstract
The addition of graphene nanoplatelets (GNP) to TiO2 nanoparticles (NPs) has been recently considered as a method to improve the photocatalytic efficiency of TiO2 by favoring charge carrier separation. Here, we show that it is possible to improve the efficiency of GNP-TiO2 composites by controlling the shape, stability, and facets of TiO2 NPs grown on GNP functionalized with either COOH or NH2 groups, while adding ethylendiamine (EDA) and oleic acid (OA) during a hydrothermal synthesis. We studied the photocatalytic activity of all synthesized materials under UV-A light using phenol as a target molecule. GNP-TiO2 composites synthesized on COOH-functionalized GNP, exposing {101} facets, were more efficient at abating phenol than those synthesized on NH2-functionalized GNP, exposing {101} and {100} facets. However, neither of these composites was stable under irradiation. The addition of both OA and EDA stabilized the materials under irradiation; however, only the composite prepared on COOH-functionalized GNP in the presence of EDA showed a significant increase in phenol degradation rate, leading to results that were better than those obtained with TiO2 alone. This result can be attributed to Ti-OH complexation by EDA, which protects GNP from oxidation. The orientation of the most reducing {101} facets toward GNP and the most oxidizing {100} facets toward the solution induces faster phenol degradation owing to a better separation of the charge carriers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".