A mechanistic investigation of carbon–hydrogen bond stannylation: synthesis and characterization of nickel catalysts
Bibliographic record
Abstract
The complex ((i)Pr(3)P)Ni(η(2)-Bu(3)SnCH=CH(2))(2) (1a) was characterized by NMR spectroscopy and was identified as the active species for catalytic C-H bond stannylation of partially fluorinated aromatics, for example in the reaction between pentafluorobenzene and Bu(3)SnCH=CH(2), which generates C(6)F(5)SnBu(3) and ethylene. The crystalline complex ((i)Pr(3)P)Ni(η(2)-Ph(3)SnCH=CH(2))(2) (1b) provides a more easily handled analogue, and is also capable of catalytic stannylation with added Ph(3)SnCH=CH(2) and C(6)F(5)H. Mechanistic studies on 1b show that the catalytically active species remains mononuclear. The rate of catalytic stannylation is proportional to [C(6)F(5)H] and inversely proportional to [Ph(3)SnCH=CH(2)]. This is consistent with a mechanism where reversible Ph(3)SnCH=CH(2) dissociation provides ((i)Pr(3)P)Ni(η(2)-Ph(3)SnCH=CH(2)), followed by a rate-determining reaction with C(6)F(5)H to generate the stannylation products. Kinetic competition reactions between the fluorinated aromatics pentafluorobenzene, 1,2,4,5-tetrafluorobenzene, 1,2,3,5-tetrafluorobenzene, 1,2,4-trifluorobenzene, 1,3,5-trifluorobenzene and 1,3-difluorobenzene all suggest significant Ni-aryl bond formation in the rate-determining step under catalytic conditions. Labelling studies are consistent with an insertion of the hydrogen of the arene into the vinyl group, followed by β-elimination or β-abstraction of the SnPh(3) moiety.
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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 | 0.001 | 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".