Improved Photocatalytic Performance of TiO<sub>2</sub>–Nitrogen-Doped Graphene Quantum Dot Composites Mediated by Heterogeneous Interactions
Bibliographic record
Abstract
Photocatalysis is typically monitored via analysis of phases in isolation and focuses on the removal of a target analyte from the solution phase. Here we analyze the photocatalytic action of a TiO 2 –nitrogen-doped graphene quantum dot (NGQD) composite on a target analyte, phenol, using comprehensive multiphase NMR (CMP-NMR) which observes signals in solid, solution, and gel phases in situ . Phenol preferentially interacts with the composite photocatalyst compared to pure TiO 2, increasing its effective concentration near the catalyst surface and its degradation rate. The presence of NGQDs in the composite reduced the fouling of the catalyst surface and caused a reduction of photogenerated intermediates. Increased heterogeneous interactions, likely mediated by π–π interactions, are hypothesized to cause each of these improvements in the observed photocatalytic performance by TiO 2 –NGQDs. CMP-NMR allows the elucidation of how the photocatalytic mechanism is enhanced via material design and provides a foundation for the development of efficient photocatalysts.
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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".