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Record W2759590533 · doi:10.1021/jacs.7b08685

Catalyst-Free and Redox-Neutral Innate Trifluoromethylation and Alkylation of Aromatics Enabled by Light

2017· article· en· W2759590533 on OpenAlexafffund
Peng Liu, Wenbo Liu, Chao‐Jun Li

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicFluorine in Organic Chemistry
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersNatural Sciences and Engineering Research Council of CanadaMcGill UniversityFonds Québécois de la Recherche sur la Nature et les TechnologiesChina Scholarship CouncilCanada Foundation for InnovationCanada Research Chairs
KeywordsChemistryTrifluoromethylationAlkylationAlkylReagentRadicalCatalysisRedoxOrganic chemistryCombinatorial chemistryPhotoredox catalysisContext (archaeology)PhotochemistryPhotocatalysisTrifluoromethyl

Abstract

fetched live from OpenAlex

The Minisci alkylation is useful to functionalize aromatics via alkyl radical addition. Current approaches to prepare alkyl radicals follow either oxidative or reductive pathways from various functional groups. Developing new strategy beyond these traditional methods remains elusive yet highly significant. In this article, we present a redox-neutral and catalyst-free protocol to engender alkyl radicals in the context of trifluoromethylation and general alkylation of arenes. This protocol, via the Norrish type I concept to produce alkyl radicals, accommodates various functional groups and delivers the product in good yields. This method identified a series of compounds as the trifluoromethylation and alkylation reagents assisted by light. It is expected that these compounds can find potential applications in other radical-involved reactions.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.356
Teacher spread0.328 · 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

Citations199
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicFluorine in Organic ChemistryFrench-language works237,207