TiO<sub>2</sub> Nanoparticles-Functionalized N-Doped Graphene with Superior Interfacial Contact and Enhanced Charge Separation for Photocatalytic Hydrogen Generation
Bibliographic record
Abstract
Titanium dioxide (TiO2) nanoparticles-functionalized N-doped graphene (NGR) composites (NGR/TiO2) were prepared through solvothermal treatment approach using exfoliated NGR and tetrabutyl titanate as the staring materials. The composites were characterized by transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, UV-vis diffuse reflectance spectra, photoelectrochemical, and electrochemical measurements. Nitrogen doping provides favorable nucleation and anchor sites for TiO2 nanocrystals formation on NGR sheets, helping to form an intimate interfacial contact between NGR and TiO2 nanoparticles. Moreover, NGR has higher electrical conductivity than the reduced graphene oxide (RGO) due to the recovery of the sp(2) graphite network and decrease of defects, resulting in more effective charge transfer and charge separation in the NGR/TiO2 composite. NGR/TiO2 nanocomposite demonstrated a higher photocatalytic activity for hydrogen production as compared to its counterpart, TiO2-functionalized RGO composite (RGO/TiO2). This work provides new insights to design new more efficient graphene-based nanocomposite photocatalysts for solar energy conversion.
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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 | 0.000 | 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".