TiO<sub>2</sub>@Carbon Photocatalysts: The Effect of Carbon Thickness on Catalysis
Bibliographic record
Abstract
Nanocomposites composed of TiO2 and carbon materials (C) are widely popular photocatalysts because they combine the advantages of TiO2 (good UV photocatalytic activity, low cost, and stability) to the enhanced charge carrier separation and lower charge transfer resistance brought by carbon. However, the presence of carbon can also be detrimental to the photocatalytic performance as it can block the passage of light and prevent the reactant from accessing the TiO2 surface. Here using a novel interfacial in situ polymer encapsulation-graphitization method, where a glucose-containing polymer was grown directly on the surface of the TiO2, we have prepared uniform TiO2@C core-shell structures. The thickness of the carbon shell can be precisely and easily tuned between 0.5 and 8 nm by simply programming the polymer growth on TiO2. The resulting core@shell TiO2@C nanostructures are not black and they possess the highest activity for the photodegradation of organic compounds when the carbon shell thickness is 1-2 nm, corresponding to ∼3-5 graphene layers. Photoluminescence and photocurrent generation tests further confirm the crucial contribution of the carbon shell on charge carrier separation and transport. This in situ polymeric encapsulation approach allows for the careful tuning of the thickness of graphite-like carbon, and it potentially constitutes a general and efficient route to prepare other oxide@C catalysts, which can therefore largely expand the applications of nanomaterials in catalysis.
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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 | 0.001 |
| 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".