(<i>N</i>-Phosphinoamidinate)Iron Pre-Catalysts for the Room Temperature Hydrosilylation of Carbonyl Compounds with Broad Substrate Scope at Low Loadings
Bibliographic record
Abstract
The synthesis and structural characterization of three-coordinate iron(II) and cobalt(II) complexes supported by new N -phosphinoamidinate ligands is reported, along with the successful application of these complexes as precatalysts for the challenging room-temperature hydrosilylation of carbonyl compounds to afford alcohols upon workup. Under the rigorous screening conditions employed (0.015 mol % Fe) for the reduction of acetophenone, the well-defined iron(II) amido precatalyst 2b, featuring bulky N -2,6-diisopropylphenyl and di- tert -butylphosphino moieties within the N -phosphinoamidinate ligand, exhibited exceptional catalytic performance. Further experimentation revealed that the yield achieved in the hydrosilylation of acetophenone employing 2b was unaltered when conducting reactions in the absence of light, in the presence of excess mercury, or under solvent-free conditions. Notably, precatalyst 2b was found to exhibit the broadest substrate scope reported to date for such room-temperature iron-catalyzed carbonyl hydrosilylations en route to alcohols, enabling the chemoselective reduction of structurally diverse aldehydes and ketones, as well as for the first time esters, at remarkably low loadings (0.01–1.0 mol % Fe) and using only 1 equiv of phenylsilane reductant.
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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".