Me<sub>2</sub>AlCH<sub>2</sub>PMe<sub>2</sub>: A New, Bifunctional Cocatalyst for the Ni(II)-Catalyzed Oligomerization of PhSiH<sub>3</sub>
Bibliographic record
Abstract
The role of methylaluminoxane (MAO) in the Ni-catalyzed dehydrogenative homologation of PhSiH3 has been investigated with a view to designing new cocatalysts possessing well-defined chemical compositions and structures. These studies show that species such as the bifunctional reagent (Me2PCH2AlMe2)2, 3, should act as co-catalyst for the Si-Si bond formation reactions. Thus, it was found that the combination of (1-Me-indenyl)Ni(PPh3)Me, 2a, and 3 (Ni/Al ratio of 1:1) converts PhSiH3 to cyclic oligomers (PhSiH)n with a turnover frequency (TOF) of >500 h(-1), 50 times faster than with 2a alone. Detailed NMR studies have indicated that this acceleration is due to the formation of the intermediate (1-Me-indenyl)Ni(Me)(Me2PCH2AlMe2), 4. Coordination of the PMe2 moiety in this complex to the Ni center allows the tethered AlMe2 moiety to interact with the Ni-Me moiety in such a way that promotes fairly slow Al-Me/Ni-CD3 exchange (t(1/2) ca. 12 h) but accelerates the Si-H bond activation and Si-Si bond formation reactions. The catalysis promoted by 2a/3 proceeds even faster in the presence of NEt3 or THF (TOF > 1600 h(-1)), because these Lewis bases favor the monomeric form of 3, which in turn favors the formation of 4. On the other hand, the much more nucleophilic base quinuclidine suppresses the catalysis (TOF < 300 h(-1)) by hindering the Ni.R.Al interactions. These observations point to an emerging strategy for using bifunctional reagents such as 3 to place geometrically constrained Lewis acid moieties adjacent to metal centers, thereby activating certain metal-ligand bonds.
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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".