Mechanistic Insights on the Semihydrogenation of Alkynes over Different Nanostructured Photocatalysts
Bibliographic record
Abstract
A single-molecule microscopy study of the interaction of dye-modified alkanes, alkenes, and alkynes with nanostructured catalysts based on TiO 2 reveals significant differences in the desorption kinetics of the probe molecules depending on the chemical nature of the catalyst. A comparison of TiO 2 with materials decorated with palladium (Pd@TiO 2 ) or molybdenum/cobalt (MoCo@TiO 2 ) reveals kinetic differences that in part justify the better performance of MoCo@TiO 2 in semihydrogenation reactions. Whereas the single-molecule desorption rate is ∼50% higher for MoCo@TiO 2 than Pd@TiO 2, analysis at the bench scale indicate alkene-to-alkane conversion is around 10 times faster for Pd (k 2 Pd = 0.02 s –1 ) than for MoCo (k 2 MoCo = 0.002 s –1 ) catalysts. This suggests selectivity is not solely determined by the desorption processes and that the hydrogenation rate constant (k 21 ) for the on-surface hydrogenation of alkenes to alkanes is much faster for Pd@TiO 2 than for MoCo@TiO 2 . Thus, the latter shows greater selectivity toward partial hydrogenation reactions compared with Pd@TiO 2, whose great hydrogenation performance becomes a disadvantage for the selectivity needed for semihydrogenation processes.
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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) | 0.001 | 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".