A Sustainable Approach to Selective Hydrogenation of Unsaturated Esters and Aldehydes with Ruthenium Catalysts
Bibliographic record
Abstract
Abstract The reduction of esters and aldehydes to alcohols is an important reaction in the chemical industry to produce a wide range of bulk and fine chemicals. Herein, the unexpected behavior of three state‐of‐the‐art, commercially available Ru‐catalysts for the hydrogenation of these feedstocks is reported. For ester and aldehydes containing a C=C bond, it was possible to carry out the selective hydrogenation of the ester or aldehyde functionality while keeping the C=C double‐bond essentially untouched. Furthermore, it is demonstrated that these substrates can be reduced under very mild reaction conditions (as low as 40 °C and 5 bar of H 2 ) and that anisole, a solvent with a high sustainability rank, is suitable for these catalytic hydrogenations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".