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Record W4390697235 · doi:10.1021/acs.joc.3c02354

Nickel-Catalyzed Reductive Arylation of Redox Active Esters for the Synthesis of α-Aryl Nitriles: Investigation of a Chlorosilane Additive

2024· article· en· W4390697235 on OpenAlexafffund
Nicholas W. M. Michel, Alexis L. Gabbey, Racquel K. Edjoc, Emmanuel Fagbola, Jonathan M. E. Hughes, Louis‐Charles Campeau, Sophie A. L. Rousseaux

Bibliographic record

VenueThe Journal of Organic Chemistry · 2024
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKennarshoreUniversity of TorontoCanada Foundation for InnovationOntario Research FoundationMerck
KeywordsChlorosilaneArylCatalysisNickelChemistryRedoxOrganic chemistryCombinatorial chemistrySiliconAlkyl

Abstract

fetched live from OpenAlex

-hydroxyphthalimide (NHP) esters and iodoarenes for the synthesis of α-aryl nitriles is described. The NHP ester substrate is derived from cyanoacetic acid, which allows for a modular synthesis of substituted α-aryl nitriles, an important scaffold in the pharmaceutical sciences. The reaction exhibits a broad scope, and many functional groups are compatible under the reaction conditions, including complex highly functionalized medicinal agents. Mechanistic studies reveal that reduction and decarboxylation of the NHP ester to the reactive radical intermediate are accomplished by a combination of a chlorosilane additive and Zn dust. We demonstrate that stoichiometric chlorosilane is essential for product formation and that chlorosilane plays a role beyond activation of the metal reductant.

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 score0.002

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)0.0010.000

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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

Citations10
Published2024
Admission routes2
Has abstractyes

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