MétaCan
Menu
Back to cohort
Record W4283779758 · doi:10.1021/acscatal.2c02577

Mechanism of Visible Light-Mediated Alkene Aminoarylation with Arylsulfonylacetamides

2022· article· en· W4283779758 on OpenAlexafffund
Anthony R. Allen, Jia‐Fei Poon, Rory C. McAtee, Nicholas B. Watson, Derek A. Pratt, Corey R. J. Stephenson

Bibliographic record

VenueACS Catalysis · 2022
Typearticle
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsUniversity of Ottawa
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaDivision of Graduate EducationCanada Foundation for InnovationUniversity of Michigan
KeywordsAlkeneArylChemistrySteric effectsRadicalSubstrate (aquarium)PhotochemistryStereochemistryCombinatorial chemistryCatalysisAlkylOrganic chemistryBiologyEcology

Abstract

fetched live from OpenAlex

Alkene aminoarylation with arylsulfonylacetamides via a visible-light mediated radical Smiles-Truce rearrangement represents a convenient approach to the privileged arylethylamine pharmacaphore traditionally generated by circuitous, multi-step sequences. Herein, we report detailed synthetic, spectroscopic, kinetic, and computational studies designed to interrogate the proposed mechanism, including the key aryl transfer event. The data are consistent with a rate-limiting 1,4-aryl migration occurring either via a stepwise process involving a radical Meisenheimer-like intermediate or in a concerted fashion dependent on both arene electronics and alkene sterics. Our efforts to probe the mechanism have significantly expanded the substrate scope of the transformation with respect to the migrating aryl group and provide further credence to the synthetic potential of radical aryl migrations.

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 score0.005

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)0.0020.000

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations64
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueACS CatalysisSame topicRadical Photochemical ReactionsFrench-language works237,207