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Record W4286489322 · doi:10.1021/acscatal.2c02233

Desulfonylative Coupling of Alkylsulfones with <i>gem-</i>Difluoroalkenes by Visible-Light Photoredox Catalysis

2022· article· en· W4286489322 on OpenAlexafffund
Masakazu Nambo, Koushik Ghosh, Jacky C.‐H. Yim, Yasuyo Tahara, Naoto Inai, Takeshi Yanai, Cathleen M. Crudden

Bibliographic record

VenueACS Catalysis · 2022
Typearticle
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsQueen's University
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and TechnologyCanada Foundation for Innovation
KeywordsIsomerizationPhotoredox catalysisChemistryPhotoisomerizationCatalysisIntramolecular forceSelectivityStereoselectivityPhotochemistrySubstrate (aquarium)AlkylElectron transferCombinatorial chemistryStereochemistryOrganic chemistryPhotocatalysis

Abstract

fetched live from OpenAlex

The desulfonylative radical addition of tertiary alkyl groups to gem -difluoroalkenes by photoredox Ir-catalyst is described. This method exhibits broad substrate scope, affording structurally diverse ( E )-fluoroalkene derivatives in a highly stereoselective manner. The resulting ( E )-fluoroalkenes were converted into complex fused cyclic compounds by intramolecular cyclization reactions. Control experiments and theoretical calculations are consistent with a single Ir catalyst playing the dual role of generating radical species from sulfones via single electron transfer and mediating Z/E isomerization via energy transfer. A subset of fluoroalkenes provided Z stereoisomers with >90% selectivity, but the same alkenes could also be obtained as E isomers with high selectivity by taking advantage of a secondary Z to E photoisomerization.

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)0.0010.000
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)0.0010.000

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations41
Published2022
Admission routes2
Has abstractyes

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