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Record W2093431699 · doi:10.1021/cm103284g

Covalently Assembled Gold Nanoparticle-Carbon Nanotube Hybrids via a Photoinitiated Carbene Addition Reaction

2011· article· en· W2093431699 on OpenAlexaff
Hossein Ismaili, François Lagugné‐Labarthet, Mark S. Workentin

Bibliographic record

VenueChemistry of Materials · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsWestern University
Fundersnot available
KeywordsCarbeneColloidal goldCarbon nanotubeCovalent bondMaterials scienceNanoparticlePhotochemistryDiazirineSurface modificationRaman spectroscopyMonolayerReactivity (psychology)Conjugated systemHybrid materialNanomaterialsNanotubeChemical engineeringChemistryNanotechnologyOrganic chemistryCatalysisPolymer

Abstract

fetched live from OpenAlex

A nanohybrid consisting of monolayer protected gold nanoparticles (AuNPs) covalently attached to multiwalled carbon nanotubes (CNT) was prepared using a photoinitiated carbene addition approach. Photolysis of 3-aryl-3-(trifluoromethyl)diazirine-modified gold nanoparticles (Diaz-AuNPs) of two core sizes (3.9 ± 0.9 nm and 1.8 ± 0.3 nm in diameter) resulted in the generation of reactive carbene intermediates on the monolayer of the AuNPs. In the presence of untreated CNT the reactive carbenes undergo an addition reaction with the π-conjugated carbon skeleton. The AuNPs are well dispersed on the sidewall of the CNT and the hybrids are robust enough to survive vigorous washing in a variety of solvents. In the absence of photolysis no covalent attachment occurs with the same AuNP. The nanohybrid AuNP-CNTs were characterized using transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman, and UV−visible spectroscopy. All of the characterization studies confirm the presence of the AuNPs. This methodology provides support that this photoinitiated carbene reactivity can be utilized for the functionalization of other materials with nanoparticles.

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)0.000
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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

Citations75
Published2011
Admission routes1
Has abstractyes

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