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Record W2765467460 · doi:10.1002/anie.201709059

Intramolecular Thioether Migration in the Rhodium‐Catalyzed Ene‐Cycloisomerization of Alkenylidenecyclopropanes by a Metal‐Mediated β‐Sulfide Elimination

2017· article· en· W2765467460 on OpenAlexafffund
Yu-Cheng Su, Phillip A. Inglesby, P. Andrew Evans

Bibliographic record

VenueAngewandte Chemie International Edition · 2017
Typearticle
Languageen
FieldChemistry
TopicSulfur-Based Synthesis Techniques
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaAstraZenecaCanada Research ChairsRoyal SocietyEngineering and Physical Sciences Research CouncilChina Scholarship Council
KeywordsCycloisomerizationAllylic rearrangementSulfideThioetherIntramolecular forceChemistryRhodiumEne reactionCatalysisSelenideSulfurMedicinal chemistryStereochemistryPhotochemistryCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Reported is the first example of a rhodium-mediated β-sulfide elimination, which represents a new mode of reactivity for late-transition-metal chemistry. This serendipitous discovery facilitates an ene-cycloisomerization of allylic-sulfide-containing alkenylidenecyclopropanes (ACPs) to afford five-membered carbo- and heterocyclic rings with concomitant intramolecular thioether migration. Interestingly, similar selectivity is obtained with both E- and Z-allylic sulfides and the reaction is also feasible with an allylic selenide. Mechanistic studies are consistent with an inner-sphere transfer of the sulfide, which is remarkable given the propensity for sulfides to poison transition-metal catalysts. Finally, this type of atom-economical rearrangement is envisioned to prompt the development of related processes given the utility of sulfides in target-directed synthesis.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes2
Has abstractyes

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