Ketone Asymmetric Hydrogenation Catalyzed by P-NH-P′ Pincer Iron Catalysts: An Experimental and Computational Study
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Bibliographic record
Abstract
Our group previously reported the development of iron carbonyl catalysts bearing chiral tridentate P–N–P′ ligands for the asymmetric hydrogenation of prochiral ketones in THF. An NMR study into the activation process identified the amine hydride alkoxide complexes Fe(P-NH-P′)(CO)(H)(OR 1 ) with R 1 = Me, tBu, or tAmyl and P-NH-P′ = PPh 2 CH 2 CH 2 NHCH 2 CH 2 P i Pr 2 or ( S,S )-PPh 2 CHPhCHMeNHCH 2 CH 2 PCy 2 . These still required treatment with excess KOtBu and H 2 (g) to be catalytically active in THF. Both experimental methods and density functional theory (DFT) calculations were used to show that this treatment leads to the formation of a hydride amide complex Fe(P–N–P′)(CO)(H), which reacts with dihydrogen to form cis and trans dihydride complexes Fe(P-NH-P′)(CO)(H) 2, identified by NMR spectroscopy. In the presence of KOtBu, NaOtBu, or KOtBu/2,2,2-cryptand and H 2 (g), these species are active for the catalytic hydrogenation of acetophenone, whereas in the absence of H 2 (g), inactive Fe(0) complexes are formed. Ketone hydrogenation is proposed to occur in an outer-sphere stepwise process, and this enantio-determining step has been modeled by DFT. The calculations suggest that the energy barriers for hydride attack on the ketone or dihydrogen splitting—either to the nitrogen of the amide complex in the inner coordination sphere or to the oxygen of an alkoxide group in the outer sphere—are similar and that either hydride transfer or dihydrogen splitting could determine the turnover frequency depending on the nature of the ketone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it