Iron‐ and Bismuth‐Catalyzed Asymmetric Mukaiyama Aldol Reactions in Aqueous Media
Bibliographic record
Abstract
We have developed asymmetric Mukaiyama aldol reactions of silicon enolates with aldehydes catalyzed by chiral Fe(II) and Bi(III) complexes. Although previous reactions often required relatively harsh conditions, such as strictly anhydrous conditions, very low temperatures (-78 °C), etc., the reactions reported herein proceeded in the presence of water at 0 °C. To find appropriate chiral water-compatible Lewis acids for the Mukaiyama aldol reaction, many Lewis acids were screened in combination with chiral bipyridine L1, which had previously been found to be a suitable chiral ligand in aqueous media. Three types of chiral catalysts that consisted of a Fe(II) or Bi(III) metal salt, a chiral ligand (L1), and an additive have been discovered and a wide variety of substrates (silicon enolates and aldehydes) reacted to afford the desired aldol products in high yields with high diastereo- and enantioselectivities through an appropriate selection of one of the three catalytic systems. Mechanistic studies elucidated the coordination environments around the Fe(II) and Bi(III) centers and the effect of additives on the chiral catalysis. Notably, both Brønsted acids and bases worked as efficient additives in the Fe(II) -catalyzed reactions. The assumed catalytic cycle and transition states indicated important roles of water in these efficient asymmetric Mukaiyama aldol reactions in aqueous media with the broadly applicable and versatile catalytic systems.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".