Ligand- and Additive-Controlled Pd-Catalyzed Aminocarbonylation of Alkynes with Aminophenols: Highly Chemo- and Regioselective Synthesis of α,β-Unsaturated Amides
Bibliographic record
Abstract
This work describes the chemo- and regioselective direct aminocarbonylation of alkynes and aminophenols to form hydroxy-substituted α,β-unsaturated amides in good to excellent yields. The latter are valuable compounds in pharmaceuticals and natural products. By a simple choice of different ligands and additives, branched or linear isomers could be selectively formed in excellent regioselectivity. Using a combination of boronic acid and 5-chlorosalicylic acid (“BCSA”) as the additives, linear amides were obtained in high yields and selectivities using 1,2-bis(di- tert -butylphosphinomethyl)benzene (DTBPMB) as the ligand. On the other hand, branched amides could be approached by introducing 1,3-bis(diphenylphosphino)propane as the ligand and p -TsOH·H 2 O as the additive. In addition to the hydroxyl group, other functional substituents, such as carboxyl and vinyl groups, could also be tolerated using this method. As an application of this strategy, the natural product avenanthramide A could be synthesized directly in 84% yield and in 99% regioselectivity via the carbonylation of 2-amino-5-hydroxybenzoic acid and 4-ethynylphenol. Further studies show that the ligands and the additives are keys to good yields and selectivities.
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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) | 0.000 | 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) | 0.001 | 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".