Photocatalytic Semi-Hydrogenation of Alkynes: A Game of Kinetics, Selectivity and Critical Timing
Bibliographic record
Abstract
The semi-hydrogenation reaction of alkynes is important in the fine chemicals and pharmaceutical industries, and it is thus important to find catalytic processes that will drive the reaction efficiently and at a low cost. The real challenge is to drive the alkyne-to-alkene reaction while avoiding over-hydrogenation to the saturated alkane moiety. The problem is more difficult when dealing with aromatic substitution at the alkyne center. Simple photocatalysts based on Palladium tend to proceed to the alkane, and stopping at the alkene with good selectivity requires very precise timing with basically no timing tolerance. We report here that the goal of high conversion with high selectivity could be achieved with TiO2-supported copper (Cu@TiO2), although with slower kinetics than for Pd@TiO2. A novel bimetallic catalyst, namely, CuPd@TiO2 (0.8% Cu and 0.05% Pd), with methanol as the hydrogen source could improve the kinetics by 50% with respect to Cu@TiO2, while achieving selectivities over 95% and with exceptional timing tolerance. Further, the low Palladium content minimizes its use, as Palladium is regarded as an element at risk of depletion.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".