Half-Sandwich Ruthenium Catalyst Bearing an Enantiopure Primary Amine Tethered to an N-Heterocyclic Carbene for Ketone Hydrogenation
Bibliographic record
Abstract
By using a copper transmetalation reagent [Cu(Kaibene) 2 ]I, the NHC ligand ( S,S )-MeNC 3 H 2 NCHPhCHPhNH 2 “Kaibene” was transferred to ruthenium to make a precatalyst [RuCp*(Kaibene)(MeCN)](PF 6 ) (Cp* = 1,2,3,4,5-pentamethylcyclopentadienyl), 7, in high yield as a mixture of two diastereomers. Under relatively mild conditions (0.02 mol % Ru, 0.16 mol % KOtBu, iPrOH, 50 °C, 25 bar of H 2 ), this compound catalyzes the hydrogenation of aryl ketones and one alkyl ketone effectively with excellent activity and productivity (TOF up to 48 s –1, TON up to 10 4 ). At higher hydrogenation pressure (46 bar), the catalytic hydrogenation of N -phenyl-benzylimine to the corresponding amine is efficiently achieved. The hydrogenation of prochiral ketones resulted in low ee (35% for 4-chloroacetophenone). NMR spectroscopy was used to observe diastereomeric hydrides RuCp*(Kaibene)(H) 13- R/S that were generated by reaction of 7 with H 2 and base in THF- d 8 . Complementary DFT studies suggest that either the heterolytic splitting of dihydrogen to form 13- R/S or the hydride transfer to the substrate can be rate-determining depending on the substrate. Experimental and computational results support mechanisms that involve the heterolytic splitting of dihydrogen to the nitrogen of the amide-ligated form of Kaibene in THF or the heterolytic splitting to an outer-sphere alkoxide derived from the product alcohol or 2-PrOH solvent. An unusual feature is the rapid drop in ee of the product alcohol from as high as 60% ( R ) to 0% in some cases; this might be due to racemization of the Kaibene ligand in THF caused by the strong base or competitive inhibition of one diastereomer of the catalyst by reaction with the product ( R )-alcohol.
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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 | 0.001 |
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".