The Role of LiBr and ZnBr<sub>2</sub> on the Cross‐Coupling of Aryl Bromides with Bu<sub>2</sub>Zn or BuZnBr
Bibliographic record
Abstract
Abstract The impact of LiBr and ZnBr2 salts on the Negishi coupling of alkylZnBr and dialkylzinc nucleophiles with both electron‐rich and ‐poor aryl electrophiles has been examined. Focusing only on the more difficult coupling of deactivated (electron‐rich) oxidative addition partners, LiBr promotes coupling with BuZnBr, but does not have such an effect with Bu2Zn. The presence of exogenous ZnBr2 shuts down the coupling of both BuZnBr and Bu2Zn, which has been shown before with alkyl electrophiles. Strikingly, the addition of LiBr to Bu2Zn reactions containing exogenous ZnBr2 now fully restores coupling to levels seen without any salt present. This suggests that there is a very important interaction between LiBr and ZnBr2. It is proposed that Lewis acid adducts are forming between ZnBr2 and the electron‐rich Pd0 centre and the bromide from LiBr forms inorganic zincates that prevent the catalyst from binding to ZnBr2. This idea has been supported by catalyst design as chlorinating the backbone of the NHC ring of Pd‐PEPPSI‐IPent to produce Pd‐PEPPSI‐IPentCl catalyst now gives quantitative conversion, up from a ceiling of only 50 % with the former catalyst.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".