Graphene-wrapped hierarchical TiO2 nanoflower composites with enhanced photocatalytic performance
Bibliographic record
Abstract
Graphene-wrapped titanium dioxide nanoflower composites (G–TiO2) consisting of nanosheets and nanoparticles were synthesized using a two-step solvo/hydrothermal process. Materials were characterized using SEM, TEM, high-resolution TEM (HRTEM), XRD, Raman spectroscopy, and FTIR. Further analysis was performed using Branauer–Emmett–Teller (BET) specific surface area analysis, electrochemical impedance spectroscopy (EIS), UV-Vis spectroscopy, and diffuse reflectance UV-Vis spectroscopy. Photocatalytic activity was determined by the photo-degradation of methylene blue under UV irradiation. Results show that the TiO2 nanoflower exhibits a higher photocatalytic activity than commercial P25 by a factor of 1.49. This is attributed to the highly crystalline, hierarchical nature of the nanoflower structure, which provides improved charge transport and a reduced recombination rate of photo-generated electron–hole pairs. After wrapping with graphene, the G–TiO2 composite can further improve the photocatalytic performance by providing a planar conjugated surface for dye adsorption, by further reducing recombination through accepting electrons from TiO2, and by causing a red shift in light absorption. The highest photocatalytic performance was found using a graphene loading of 5 wt%, which outperforms commercial P25 by a factor of 3.4.
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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) | 0.001 | 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 | 0.001 | 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".