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Record W2781311218 · doi:10.1021/acssuschemeng.7b02992

Light-Induced Sonogashira C–C Coupling under Mild Conditions Using Supported Palladium Nanoparticles

2017· article· en· W2781311218 on OpenAlexafffund
Ayda Elhage, Anabel E. Lanterna, J. C. Scaiano

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsSonogashira couplingPalladiumCatalysisArylPhotochemistryChemistryIrradiationAbsorption (acoustics)ExcitationCombinatorial chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The Sonogashira reaction can easily be photocatalyzed by supported palladium nanoparticles. Herein, we demonstrate that the direct excitation of PdNPs can catalyze the C–C coupling between different aryl iodides and acetylenes under very mild conditions in short reaction times. The catalyst is air- and moisture-tolerant and can be supported on a wide range of materials, including inert ones such as nanodiamonds. Study of the action spectrum demonstrates that direct excitation of the PdNPs is required, and in the case of Pd@TiO 2, for example, visible excitation works well whereas UVA (368 nm) irradiation is ineffective because of TiO 2 shielding the Pd absorption. The catalyst can be reused a couple of times, but when it loses activity, it can be readily reactivated by a simple reductive photochemical strategy.

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 score0.004

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 integrity0.0000.001
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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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

Citations61
Published2017
Admission routes2
Has abstractyes

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