Selenosulfonation and related processes: Methods, mechanisms and synthetic applications
Bibliographic record
Abstract
Selenosulfonates (RSO 2 SeR’) are versatile reagents that undergo free-radical or electrophilic additions to alkenes, alkynes, allenes and other unsaturated compounds with complementary regioselectivity. Such “selenosulfonations”, thus introduce two orthogonal functionalities that lend themselves to further useful transformations, such as selenoxide eliminations to form unsaturated sulfones and addition-elimination processes with a variety of organometallic reagents and other nucleophiles. The sulfone moiety is capable of stabilizing α-anions for further modification with electrophiles, as well as promoting conjugate additions and facilitating various cycloadditions when adjacent to a double or triple bond. Selenosulfonations of dienes and other polyunsaturated compounds can be employed in radical cyclizations. Furthermore, selenosulfonates serve as selenylating agents capable of introducing selenium substituents, including fluorinated alkylseleno groups, into a wide variety of substrates. The unique properties of selenosulfonates have led to a number of useful photoredox and polymer-supported processes, and have provided unsaturated sulfones for key steps in several total syntheses. While the emphasis of this review is on synthetic methodology, mechanistic explanations and applications to total synthesis are also covered.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".