Bibliographic record
Abstract
The Negishi cross‐coupling reaction is a carbon‐carbon bond forming reaction between an organozinc reagent and an organo(pseudo)halide. The reaction has an extremely broad scope and can form bonds between all hybridizations of the reactive carbon atoms in both coupling partners. The reaction tolerates many protic or electrophilic functionalities and is celebrated for its relative ease in forging linkages to heteroarenes. The reaction is generally palladium‐catalyzed but other first‐row metal catalysts can be used effectively in certain cases. Many organozinc reagents are commercially available while others can be made from more reactive organometallics, aryl halides, or by direct zincation of (hetero)arenes. This chapter covers some of the major breakthroughs since the reaction's inception but mainly focuses on the current state‐of‐the‐art (up to mid‐2017) in cross‐coupling as well as organozinc reagent formation. An overview of the mechanistic features of the reaction for both palladium‐ and nickel‐catalyzed reactions is provided as well as an analysis of the resulting regio‐ and stereoselectivity issues. A summary of the scope of the reaction partners is provided as well as a Tabular Survey providing examples covering the gambit of potential nucleophile/electrophile combinations. A thoughtful selection of examples from the literature along with their experimental procedures, and a comparison of the Negsihi reaction with other cross‐coupling methodologies are also included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".