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Record W2146074173 · doi:10.3762/bjoc.9.155

A<sup>3</sup>-Coupling catalyzed by robust Au nanoparticles covalently bonded to HS-functionalized cellulose nanocrystalline films

2013· article· en· W2146074173 on OpenAlexaff
Jianlin Huang, Derek G. Gray, Chao‐Jun Li

Bibliographic record

VenueBeilstein Journal of Organic Chemistry · 2013
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanocompositeX-ray photoelectron spectroscopyCelluloseCatalysisChemistryNanocrystalline materialDispersityNanoparticleChemical engineeringAldehydeNanocelluloseCovalent bondCoupling reactionPolymer chemistryMaterials scienceNanotechnologyOrganic chemistryCrystallography

Abstract

fetched live from OpenAlex

We decorated HS-functionalized cellulose nanocrystallite (CNC) films with monodisperse Au nanoparticles (AuNPs) to form a novel nanocomposite catalyst AuNPs@HS-CNC. The uniform, fine AuNPs were made by the reduction of HAuCl4 solution with thiol (HS-) group-functionalized CNC films. The AuNPs@HS-CNC nanocomposites were examined by X-ray photoelectron spectroscopy (XPS), TEM, ATR-IR and solid-state NMR. Characterizations suggested that the size of the AuNPs was about 2-3 nm and they were evenly distributed onto the surface of CNC films. Furthermore, the unique nanocomposite Au@HS-CNC catalyst displayed high catalytic efficiency in promoting three-component coupling of an aldehyde, an alkyne, and an amine (A(3)-coupling) either in water or without solvent. Most importantly, the catalyst could be used repetitively more than 11 times without significant deactivation. Our strategy also promotes the use of naturally renewable cellulose to prepare reusable nanocomposite catalysts for organic synthesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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 teacher head, not a consensus.

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

Citations73
Published2013
Admission routes1
Has abstractyes

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