Engineering the TiO<sub>2</sub>–Graphene Interface to Enhance Photocatalytic H<sub>2</sub> Production
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Bibliographic record
Abstract
In this work, TiO2 -graphene nanocomposites are synthesized with tunable TiO2 crystal facets ({100}, {101}, and {001} facets) through an anion-assisted method. These three TiO2 -graphene nanocomposites have similar particle sizes and surface areas; the only difference between them is the crystal facet exposed in TiO2 nanocrystals. UV/Vis spectra show that band structures of TiO2 nanocrystals and TiO2 -graphene nanocomposites are dependent on the crystal facets. Time-resolved photoluminescence spectra suggest that the charge-transfer rate between {100} facets and graphene is approximately 1.4 times of that between {001} facets and graphene. Photoelectrochemical measurements also confirm that the charge-separation efficiency between TiO2 and graphene is greatly dependent on the crystal facets. X-ray photoelectron spectroscopy reveals that Ti-C bonds are formed between {100} facets and graphene, while {101} facets and {001} facets are connected with graphene mainly through Ti-O-C bonds. With Ti-C bonds between TiO2 and graphene, TiO2 -100-G shows the fastest charge-transfer rate, leading to higher activity in photocatalytic H2 production from methanol solution. TiO2 -101-G with more reductive electrons and medium interfacial charge-transfer rate also shows good H2 evolution rate. As a result of its disadvantageous electronic structure and interfacial connections, TiO2 -001-G shows the lowest H2 evolution rate. These results suggest that engineering the structures of the TiO2 -graphene interface can be an effective strategy to achieve excellent photocatalytic performances.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it