Selective [1,4]-Hydrovinylation of 1,3-Dienes with Unactivated Olefins Enabled by Iron Diimine Catalysts
Bibliographic record
Abstract
The selective, intermolecular [1,4]-hydrovinylation of conjugated dienes with unactivated α-olefins catalyzed by α-diimine iron complexes is described. Value-added “skipped” diene products were obtained with exclusive [1,4]-selectivity, and the formation of branched, ( Z )-olefin products was observed with no evidence for alkene isomerization. Mechanistic studies conducted with the well-defined, single-component iron precatalyst ( Mes DI)Fe(COD) ( Mes DI = [2,4,6-Me 3 -C 6 H 2 -N═CMe] 2 ); COD = 1,5-cyclooctadiene) provided insights into the origin of the high selectivity. An iron diene complex was identified as the catalyst resting state, and one such isoprene complex, ( iPr DI)Fe(η 4 -C 5 H 8 ), was isolated and characterized. A combination of single crystal X-ray diffraction, Mößbauer spectroscopy, magnetic measurements, and DFT calculations established that the complex is best described as a high-spin Fe(I) center ( S Fe = 3/2) engaged in antiferromagnetic coupling to an α-diimine radical anion ( S DI = −1/2), giving rise to the observed S = 1 ground state. Deuterium-labeling experiments and kinetic analyses of the catalytic reaction provided support for a pathway involving oxidative cyclization of an alkene with the diene complex to generate an iron metallacycle. The observed selectivity can be understood in terms of competing steric interactions in the transition states for oxidative cyclization and subsequent β-hydrogen elimination.
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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".