Exploring Electrophilic Hydrophosphination via Metal Phosphenium Intermediates
Bibliographic record
Abstract
Abstract Two Mo(0) phosphenium complexes containing ancillary secondary phosphine ligands have been investigated with respect to their ability to participate in electrophilic addition at unsaturated substrates and subsequent P−H hydride transfer to “quench” the resulting carbocations. These studies provide stoichiometric “proof of concept” for a proposed new metal‐catalyzed electrophilic hydrophosphination mechanism. The more strongly Lewis acidic phosphenium complex, [Mo(CO)4(PR2H)(PR2)]+ (R=Ph, Tolp), cleanly hydrophosphinates 1,1‐diphenylethylene, benzophenone, and ethylene, while other substrates react rapidly to give products resulting from competing electrophilic processes. A less Lewis acidic complex, [Mo(CO)3(PR2H)2(PR2)]+, generally reacts more slowly but participates in clean hydrophosphination of a wider range of unsaturated substrates, including styrene, indene, 1‐hexene, and cyclohexanone, in addition to 1,1‐diphenylethylene, benzophenone, and ethylene. Mechanistic studies are described, including stoichiometric control reactions and computational and kinetic analyses, which probe whether the observed P−H addition actually does occur by the proposed electrophilic mechanism, and whether hydridic P−H transfer in this system is intra‐ or intermolecular. Preliminary reactivity studies indicate challenges that must be addressed to exploit these promising results in catalysis.
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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".