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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".