From Bifunctional to Trifunctional (Tricomponent Nucleophile–Transition Metal–Lewis Acid) Catalysis: The Catalytic, Enantioselective α-Fluorination of Acid Chlorides
Bibliographic record
Abstract
We report in full detail our studies on the catalytic, asymmetric α-fluorination of acid chlorides, a practical method that produces an array of α-fluorocarboxylic acid derivatives in which improved yield and virtually complete enantioselectivity are controlled through electrophilic fluorination of a ketene enolate intermediate. We discovered, for the first time, that a third catalyst, a Lewis acidic lithium salt, could be introduced into a dually activated system to amplify yields of aliphatic products, primarily through activation of the fluorinating agent. Through our mechanistic studies (based on kinetic data, isotopic labeling, spectroscopic measurements, and theoretical calculations) we were able to utilize our understanding of this "trifunctional" reaction to optimize the conditions and obtain new products in good yield and excellent enantioselectivity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".