Kinetic isotope effect evidence for the concerted transfer of hydride and proton from hydroxycyclopentadienyl ruthenium hydride in solvents of different polarities and hydrogen bonding ability
Bibliographic record
Abstract
The tolyl analogue of Shvo's hydroxycyclopentadienyl ruthenium hydride (4) efficiently transfers a hydride and proton to benzaldehyde or acetophenone to produce an alcohol. This reduction can be performed in toluene, methylene chloride, and THF. Reduction of benzaldehyde in toluene and methylene chloride occurs approximately 300 times faster than in THF at 0 °C. Reduction of acetophenone occurs between 75 and 150 times slower than benzaldehyde at 0 °C in each respective solvent. Despite the differences in rate, mechanistic studies have provided evidence for a similar concerted transfer of acidic and hydridic hydrogens in each solvent. Addition of water to THF led to further rate decrease coupled with an increase in the OH/D kinetic isotope effect and a decrease in the RuH/D kinetic isotope effect. Addition of excess alcohol to toluene or methylene chloride results in the significant retardation of the rate of reduction. The slower rate in THF and in the presence of alcohol is attributed to the stabilization of the ground state of ruthenium hydride 4 by hydrogen bonding and the additional energy required to break these bonds prior to carbonyl reduction.Key words: ruthenium hydrogenation catalysis, hydrogenation mechanism, kinetic isotope effects, ligandmetal bifunctional catalysis.
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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) | 0.002 | 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".