Facile Synthesis of β,β-(Radio)difluoroamines via Amino(radio)fluorination of α-Fluoroalkenes
Bibliographic record
Abstract
Given their prevalent presence in bioactive molecules, the synthesis of radiolabeled gem -difluoromethylene ([ 18 F]–CF 2 −) motifs is of practical relevance for radiotracer development. However, conventional strategies to construct [ 18 F]–CF 2 – motifs are limited, primarily relying on either 18 F-fluorination of bespoke precursors or multistep 18 F-difluoromethylation using radiosynthesized difluoromethylating reagents. Herein, a conceptually distinct alkene difunctionalization strategy is adopted in 18 F- gem -difluoromethylene synthesis, enabling an iron-catalyzed three-component amino(radio)fluorination of α-fluoroalkenes to furnish unprotected β,β-difluoroamines and their 18 F-labeled analogues. This transformation proceeds efficiently under low heat and air-insensitive conditions and accommodates a broad range of substrates, including complex drug-like molecules, without requiring prefunctionalized precursors. Mechanistic studies reveal a radical-polar crossover pathway involving an electrophilic N -centered radical addition and a subsequent nucleophilic ring-opening of an aziridinium intermediate to forge the C–F bond. This work effectively broadens the toolkit for the construction of complex 18/19 F- gem -difluoromethyl compounds.
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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 | 0.001 |
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".