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Record W2041657343 · doi:10.1002/adsc.201400957

Organoselenium‐Catalyzed Baeyer–Villiger Oxidation of α,β‐Unsaturated Ketones by Hydrogen Peroxide to Access Vinyl Esters

2015· article· en· W2041657343 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Synthesis & Catalysis · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicOrganoselenium and organotellurium chemistry
Canadian institutionsUniversity of Toronto
FundersYangzhou UniversityNational Natural Science Foundation of China
KeywordsChemistryHydrogen peroxideCatalysisBaeyer–Villiger oxidationDiselenideArylOrganic chemistryAlkylEnonePeroxideSelenium

Abstract

fetched live from OpenAlex

Abstract By carefully screening the organoselenium pre‐catalysts and optimizing the reaction conditions, simple dibenzyl diselenide was found to be the best pre‐catalyst for Baeyer–Villiger oxidation of ( E )‐α,β‐unsaturated ketones with the green oxidant hydrogen peroxide at room temperature. The organoselenium catalyst used in this reaction could be recycled and reused several times. This new method was suitable not only for methyl unsaturated ketones, but also for alkyl and aryl unsaturated ketones. Therefore, it provided a direct, mild, practical, highly functional group‐tolerant process for the chemoselective preparation of the versatile ( E )‐vinyl esters from the readily available ( E )‐α,β‐unsaturated ketones. A possible mechanism was also proposed to rationalize the activity of the organoselenium catalyst in the presence of hydrogen peroxide in this Baeyer–Villiger oxidation reaction. magnified image

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.067
GPT teacher head0.393
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it