An In Situ Polymerization‐Encapsulation Approach to Prepare TiO<sub>2</sub>–Graphite Carbon–Au Photocatalysts for Efficient Photocatalysis
Bibliographic record
Abstract
Abstract A complex nanoarchitecture composed of TiO2 nanobelts, graphite‐like carbon, and Au nanoparticles (NPs) is developed using an in situ surface polymeric encapsulation technique. The precise arrangement of the carbon layer and Au NPs on a TiO2 surface can be programmed to form three different core@shell structures by simply varying the addition sequence of materials during the encapsulation process. The photocatalytic activity of the three nanoarchitectures is assessed in H2 generation, degradation of dye molecules as well as photoelectrochemical cells under solar and visible light irradiation. In the reaction of H2 generation, no activity can be detected for all samples under visible‐light, while under solar light the sample with Au on the surface of carbon layers wrapped on TiO2 shows the highest activity. By stark contrast, in the photodegradation test, significant difference in activity under visible light is observed, where the sample with Au NPs sandwiched between carbon layers demonstrates the highest activity. All the results indicate that the synergistic effect of carbon‐layers and Au NPs is essential for the catalytic activity enhancement. Moreover, the activity of the photocatalysts is not only highly dependent on the architecture of the catalyst, but also on the type of reaction investigated.
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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".