Triflic Acid‐Catalyzed Dehydrative Amination of 2‐Arylethanols with Weak <i>N</i>‐Nucleophiles in Hexafluoroisopropanol
Bibliographic record
Abstract
The catalytic deoxyamination of readily available 2-arylethanols offers an appealing, simple, and straightforward means of accessing β-(hetero)arylethylamines of biological interest. Yet, it currently represents a great challenge to synthetic chemistry. In most cases, the alcohol has to be either pre-activated in situ or converted into a reactive carbonyl intermediate, limiting the substrate scope for some methods. Examples of direct dehydrative amination of 2-arylethanols are thus still scarce. Here, we describe a catalytic protocol based on the synergy of triflic acid and hexafluoroisopropanol, which enables the direct and stereospecific amination of a broad array of 2-arylethanols, and does not require any pre-activation of the alcohol. This approach yields high value-added products incorporating sulfonamide, amide, urea, and aniline functionalities. In addition, this approach was applied to the sulfidation of 2-arylethanols. Mechanistic experiments and DFT computations indicate the formation of phenonium ions as key intermediates in the reaction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".