Advances in Free Radical Reactions of Organoselenium and Organotellurium Compounds
Bibliographic record
Abstract
Abstract Free radical reactions of organoselenium and tellurium compounds have been widely studied and exploited since the first two volumes of this series appeared. Alkyl aryl selenides and tellurides undergo alkyl C‐Se or C‐Te cleavage with stannanes or silanes to generate alkyl radicals that can be employed in reductions, allylations and both intermolecular and intramolecular additions to a large variety of unsaturated substrates, terminated by either hydrogen or arylchalcogen transfer. Selenoesters, telluroesters and related species generate acyl radicals that are useful in overall decarboxylations of carboxylic acids, and in addition and radical cyclization processes, including cascade reactions of polyenes to afford polycyclic products. Homolytic cleavage of diselenides and ditellurides produces selenyl and telluryl radicals that can be employed in additions to unsaturated acceptors, while the dichalcogenides themselves function as effective trapping agents for radicals produced in a variety of other ways, as from Barton thiohydroxamates. Selenols are efficient hydrogen donors that are of importance in kinetic studies of radical processes. When selenium and tellurium are joined to other heteroatoms, the resulting reagents effect radical 1,2‐additions to unsaturated substrates, as in the case of selenosulfonation and azidoselenenylation reactions. Other radical processes include oxidations, S H 2 substitutions, single electron transfers, extrusions and fragmentations, and reactions performed on solid supports. The synthetic utility of these processes, as well as mechanistic considerations are covered in this chapter.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".