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Record W1751230434 · doi:10.3390/app5030241

N-Vinylcarbazole: As an Additive for Thermal Polymerization at Room Temperature with in situ Formation of Ag(0) Nanoparticules

2015· article· en· W1751230434 on OpenAlexaff
Mohamad‐Ali Tehfe, Saïd Elkoun, Mathieu Robert

Bibliographic record

VenueApplied Sciences · 2015
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials sciencePolymerizationFourier transform infrared spectroscopySilaneDifferential scanning calorimetryChemical engineeringMonomerPolymerPolymer chemistryIn situ polymerizationRadical polymerizationSilver nanoparticleNanoparticleNanocompositeComposite materialNanotechnology

Abstract

fetched live from OpenAlex

N-vinylcarbazole (NVK) is proposed as an additive for acrylates thermal free radical polymerization (FRP) and epoxy thermal ring opening polymerization (ROP) at room temperature. The new initiating systems are based on a silane/silver salt/N-vinylcarbazole interaction, which ensures good to excellent polymerization. Moreover, the polymerization is much more efficient under air than under argon. The effects of the N-vinylcarbazole, silane, silver salt and monomer structures are investigated. Interestingly, silver nanoparticles Ag(0) are formed in situ. The as-synthesized nanocomposite materials contained spherical nanoparticles homogeneously dispersed in the polymer matrices. Polymers and nanoparticles were characterized by Differential Scanning Calorimetry (DSC), Transmission Electron Microscopy (TEM), Energy-Dispersive X-ray Spectrometry (EDS), Fourier Transform Infrared Spectroscopy (FTIR), and UV-vis spectroscopy. A coherent picture of the involved chemical mechanisms is presented.

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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