(Invited) The Role of Gold Cluster Size and Coverage on Hydrogen Production over TiO<sub>2</sub>(110) Single Crystal. An STM and Time Resolved Spectroscopy Study
Bibliographic record
Abstract
Unlike thermally driven catalytic reactions by metals, the reaction rates in photo-catalysis do not scale with neither the amount of metals nor with their size. Because of the complexity of multi-component photo-catalysts in powder forms, this phenomenon that has been routinely observed for over three decades, has so far no fundamental explanations. In order to probe into this, hydrogen production rates from ethanol over Au clusters with different sizes deposited on TiO2(110) rutile single crystal, were studied by scanning tunneling microscopy (STM) and online mass spectrometry. A non-linear increase of the rate of hydrogen with increasing surface coverage of gold was observed. While Au particles with sizes ranging from 4 to 8 Å, marginally affected the reaction rate, the inter-particle distance was found to be crucial. Increasing the separation distance resulted in increasing the normalized reaction rate. These results are explained in terms of competition between particles for excited electrons to reduce hydrogen ions of surface hydroxyls to molecular hydrogen. The reason for nonlinearity is postulated to be due to two considerably different time scale, the picosecond scale (associated with Debye length) of charge transfer at the interface Au/TiO2 and the much slower time scale of electron transfer in chemical reactions.
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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) | 0.003 | 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".