Highly Ligand-Controlled Regioselective Pd-Catalyzed Aminocarbonylation of Styrenes with Aminophenols
Bibliographic record
Abstract
Achieving chemo- and regioselectivity simultaneously is challenging in organic synthesis. Transition metal-catalyzed reactions are effective in addressing this problem by the diverse ligand effect on the catalyst center. Ligand-controlled regioselective Pd-catalyzed carbonylation of styrenes with aminophenols was realized, chemoselectively affording amides. Using a combination of boronic acid and 5-chlorosalicylic acid as the additives, linear amides were obtained in high yields and selectivity using tris(4-methoxyphenyl)phosphine (L3) in acetonitrile, while branched amides were obtained in high yields and selectivity in butanone by changing the ligand to 1,3,5,7-tetramethyl-2,4,8-trioxa-6-phenyl-6-phosphaadamantane (L5). Further studies show that the nature of the ligand is key to the regioselectivity. Cone angle and Tolman electronic parameter (TEP) have been correlated to the reactivity and regioselectivity. Studies on the acid additives show that different acids act as the proton source and the corresponding counterion can help enhance the reactivity and selectivity.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".