Iron(II) Complexes Containing Unsymmetrical P–N–P′ Pincer Ligands for the Catalytic Asymmetric Hydrogenation of Ketones and Imines
Bibliographic record
Abstract
After their treatment with LiAlH4 and then alcohol, new iron dicarbonyl complexes mer-trans-[Fe(Br)(CO)2(P-CH═N-P')][BF4] (where P-CH═N-P' = R2PCH2CH═NCH2CH2PPh2 and R = Cy or iPr or P-CH═N-P' = (S,S)- Cy2PCH2CH═NCH(Me)CH(Ph)PPh2) are catalysts for the hydrogenation of ketones in THF solvent with added KOtBu at 50 °C and 5 atm H2. Complexes with R = Ph are not active. With the enantiopure complex, alcohols are produced with an enantiomeric excess of up to 85% (S) at TOF up to 2000 h(-1), TON of up to 5000, for a range of ketones. An activated imine is hydrogenated to the amine in 90% ee at a TOF 20 h(-1)and TON 99. This is a significant advance in asymmetric pressure hydrogenation using iron. The complexes are prepared in two steps: (1) a one-pot reaction of phosphonium dimers ([cyclo-(PR2CH2CH(OH)(-))2][Br]2), KOtBu, FeBr2, and Ph2PCH2CH2NH2 (or (S,S)-Ph2PCH(Ph)CH(Me)NH2 for the enantiopure complex) in THF under a CO atmosphere to produce the complexes cis- and trans-[Fe(Br)2(CO)(P-CH═N-P')]; (2) the reaction of these with AgBF4 under CO(g) to afford the dicarbonyl complexes in high yield (50-90%). NMR and DFT studies of the process of precatalyst activation show that the dicarbonyl complexes are converted first to hydride-aluminum hydride complexes where the imine of the P-CH═N-P' ligand is reduced to an amide [P-CH2N-P'](-) with aluminum hydrides still bound to the nitrogen. These hydride species react with alcohol to give monohydride amine iron compounds FeH(OR')(CO)(P-CH2NH-P'), R' = Me, CMe2Et as well as the iron(0) complex Fe(CO)2(P-CH2NH-P') under certain conditions.
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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 | 0.000 |
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".