Hydrosilylation of alkenes and ketones catalyzed by nickel(II) indenyl complexes
Bibliographic record
Abstract
Abstraction of Cl from the complexes (indenyl)Ni(PPh3)Cl generates cationic species that are effective precatalysts for the hydrosilylation of some olefins and ketones. For instance, the mixture of (1-Me-indenyl)Ni(PPh3)Cl and NaBPh4 (or methylaluminoxane) reacts at room temperature with ca. 100 equiv. each of PhSiH3 and styrene to produce [1-phenyl-1-ethyl](phenyl)silane, PhCH(CH3)(SiPhH2), in 50%80% yield. The same system can also catalyze the hydrosilylation of 1-hexene and norbornene, but the products arising from these substrates consist of mixtures of regio- and stereoisomers. On the other hand, ketone hydrosilylation is regiospecific, giving the corresponding silyl ethers in high yields. A number of experimental observations have indicated that the initially generated Ni-based cation is not the catalytically active species. Indeed, the cationic initiators may be replaced by LiAlH4 or AlMe3, which generate the corresponding Ni-H or Ni-Me derivatives, respectively. Moreover, the observed regioselectivity for the addition of PhSiH3 to styrene (i.e., predominant addition of the silyl fragment to the α-C) is opposite of what would be expected if the reaction mechanism involved carbocationic intermediates. A new mechanism is proposed in which the active species is a Ni-H species originating from the transfer of H from PhSiH3 to the initially generated Ni cation. Key words: hydrosilylation, nickel indenyl complexes, cationic complexes, hydride intermediates.
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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) | 0.002 | 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".