Diselenides and Allyl Selenides as Glutathione Peroxidase Mimetics. Remarkable Activity of Cyclic Seleninates Produced in Situ by the Oxidation of Allyl ω-Hydroxyalkyl Selenides
Bibliographic record
Abstract
A series of aliphatic diselenides and selenides containing coordinating substituents was tested for glutathione peroxidase (GPx)-like catalytic activity in a model system in which the reduction of tert-butyl hydroperoxide with benzyl thiol to afford dibenzyl disulfide and tert-butyl alcohol was performed under standard conditions and monitored by HPLC. Although the diselenides showed generally poor catalytic activity, allyl selenides proved more effective. In particular, allyl 3-hydroxypropyl selenide (25) rapidly generated 1,2-oxaselenolane Se-oxide (31) in situ by a series of oxidation and [2,3]sigmatropic rearrangement steps. The remarkably active cyclic seleninate 31 proved to be the true catalyst, reacting with the thiol via a postulated mechanism in which the thioseleninate 32 is first produced, followed by further thiolysis to selenenic acid 33 and oxidation-dehydration to regenerate 31. In contrast to catalysis with GPx, formation of the corresponding selenenyl sulfide 34 comprises a competing deactivation pathway in the catalytic cycle of 31, as a separate experiment revealed that authentic 34 was a much less effective catalyst than 31. 1,2-Oxaselenane Se-oxide (37), the six-membered homologue of 31, was formed similarly from allyl 4-hydroxybutyl selenide (26), but proved a less effective catalyst than 31. Compounds 31 and 37 are the first examples of unsubstituted monocyclic seleninate esters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".