Part I: Mechanistic Insight in Alkyl-Alkyl and Aryl-Aryl Negishi Cross-Coupling \nPart II: Large Scale Synthesis of NHC Precursors: 2,6-DI(3-Pentyl) Aniline and 2,6-DI \n(4-Heptyl) Aniline
Bibliographic record
Abstract
Part I: An efficient method for alkyl-alkyl Negishi cross-coupling reaction of unactivated primary alkyl halides with higher-order zincate species (synthesized from dialkylzinc and a non-coordinating halide salt in situ) using Pd-PEPPSI-IPent (Pyridine-Enhanced Pre-catalyst Preparation Stabilization and Initiation) pre-catalyst has been developed. The method requires no additive (such as LiX) or use of a polar co-solvent (such as THF/DMI) and reactions proceed in THF solvent alone. The transmetalating species in alkyl-alkyl Negishi cross-coupling has been identified and the catalytic cycle has been amended to include the formation of the higher-order zincate species. \n\tSubsequent investigation into diarylzinc as reagents in Negishi cross-coupling have shown they transmetalate directly without the use of halide additive in THF alone producing arylzinc halide as a resting state. Once the dielectric of the solvent becomes sufficiently polar, coupling resumes, and the aryl moiety transmetalates to palladium. The optimized protocol using Pd-PEPPSI-IPent allowed for the coupling of a variety of alkyl- and aryl- organozinc reagents providing the corresponding products in good to quantitative yields. Moreover, the developed protocol is generally applicable and tolerant of a variety of functional groups including esters, and nitriles. \n\tPart II: An improved and efficient method for the preparation of sterically demanding Pd PEPPSI-IPent, IPentCl, IHept, and IHeptCl pre-catalysts are presented and the results of optimization for the multi-gram synthesis of 2,6-disubstituted anilines is discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".