Exploring the decomposition pathways of iron asymmetric transfer hydrogenation catalysts
Bibliographic record
Abstract
Our group has developed a series of iron-based asymmetric transfer hydrogenation (ATH) catalysts for the reduction of polar double bonds. The activation of the precatalysts as well as the catalytic mechanism have been thoroughly investigated, but the decomposition pathways of these systems are poorly understood. Herein, we report a study of the deactivation pathways for an iron ATH catalyst under catalytically relevant conditions. The decomposition pathways were examined using experimental techniques and density functional theory (DFT) calculations. The major decomposition products that formed, Fe(CO)((Et)2PCH2CH2CHCHNCH2CH2P(Et)2) (3a) and Fe(CO)((Et)2PCH2CH2C(Ph)C(Ph)NCH2CH2P(Et)2) (3b), had two amido donors as well as a C=C bond on the diamine backbone of the tetradentate ligand. These species were identified by NMR studies and one was isolated as a bimetallic complex with Ru(II)Cp*. Two minor iron hydride species also formed concurrently with 3a, as determined by NMR studies, one of which was isolated and contained a fully saturated ligand as well as a hydride ligand. None of the compounds that were isolated were found to be active ATH catalysts.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".