Iron-catalyzed three-component amino(radio)fluorination of alkenes to unprotected β-(radio)fluoroamines
Bibliographic record
Abstract
Abstract Unprotected β-fluoroamines are important motifs in synthetic chemistry, offering versatility for the development of β-fluorinated nitrogen-containing compounds. Existing methods to these motifs require tedious operations and suffer from low efficiencies, which has prevented their use in biologically active molecules, such as drug discovery and positron emission tomography (PET) radiotracer development. Herein, an iron-catalyzed three-component aminofluorination of alkenes using a hydroxylamine reagent and Et 3 N · 3HF is reported, offering a direct entry to unprotected β-fluoroamines. Both aryl and unactivated alkenes are compatible, and the mild conditions along with a short reaction time enable its application in alkene aminoradiofluorination. The synthetic utility of this methodology is demonstrated by diverse follow-up derivatizations, efficient access to drug candidate LY503430, and the radiosynthesis of [ 18 F]KP23, a cannabinoid subtype 2 (CB2) PET radioligand. Mechanistic investigations reveal a radical pathway involving ferryl amino and aziridinium intermediates, and highlight the dual roles of Et 3 N · 3HF as both fluorine source and reductive promotor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".