Size Effect of Au Nanoparticles on TiO<sub>2</sub>Crystalline Phase of Nanocomposite Thin Films and Their Photocatalytic Properties
Bibliographic record
Abstract
This work focuses on the size effects of Au nanoparticles (AuNPs) on the TiO 2 crystalline phase of nanocomposite AuNPs-embedded TiO 2 (Au-TiO 2 ) thin films, their adsorption ability, and photocatalytic activity. Au-TiO 2 films were synthesized through a sol−gel method using polyvinylpyrrolidone-protected AuNPs (AuNPs@PVP). The mean diameters of AuNPs@PVP dispersed in a sol solution were 2.0 ± 0.7 or 7.9 ± 3.1 nm and the heat-treatment temperature of the films was 400−900 °C. XRD and Ti L 3,2 -edge X-ray absorption near-edge structure (XANES) analysis revealed that AuNPs doping could suppress an anatase to rutile phase transformation. In addition, the larger size of AuNPs doped in TiO 2 film tended to prevent the transformation more effectively. The film doped with the smaller AuNPs@PVP and annealed at 500 °C showed the highest photocatalytic activity among the obtained films because it had the well-crystallized anatase phase and the high adsorption ability, which was attributed to the existence of a five-coordinated Ti site that was revealed from Ti K -edge XANES measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".