Tuning TiO<sub>2</sub>nanoparticle morphology in graphene–TiO<sub>2</sub>hybrids by graphene surface modification
Bibliographic record
Abstract
We report the hydrothermal synthesis of graphene (GNP)-TiO2 nanoparticle (NP) hybrids using COOH and NH2 functionalized GNP as a shape controller. Anatase was the only TiO2 crystalline phase nucleated on the functionalized GNP, whereas traces of rutile were detected on unfunctionalized GNP. X-Ray Photoelectron spectroscopy (XPS) showed C-Ti bonds on all hybrids, thus confirming heterogeneous nucleation. GNP functionalization induced the nucleation of TiO2 NPs with specific shapes and crystalline facets exposed. COOH functionalization directed the synthesis of anatase truncated bipyramids, bonded to graphene sheets via the {101} facets, while NH2 functionalization induced the formation of belted truncated bipyramids, bonded to graphene via the {100} facets. Belted truncated bipyramids formed on unfunctionalized GNP too, however the NPs were more irregular and rounded. These effects were ascribed to pH variations in the proximity of the functionalized GNP sheets, due to the high density of COOH or NH2 groups. Because of the different reactivity of anatase {100} and {101} crystalline facets, we hypothesize that the hybrid materials will behave differently as photocatalysts, and that the COOH-GNP-TiO2 hybrids will be better photocatalysts for water splitting and H2 production.
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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".