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Record W2062476905 · doi:10.1109/tdei.2013.6508763

Study of dielectric relaxation of epoxy composites containing micro and nano particles

2013· article· en· W2062476905 on OpenAlexafffund
Hugues Couderc, M. Fréchette, Éric David, Sylvio Savoie

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsHydro-QuébecÉcole de Technologie Supérieure
FundersMitacs
KeywordsEpoxyMaterials scienceDielectricGlass transitionComposite materialRelaxation (psychology)Differential scanning calorimetryNanoparticleFragilityContext (archaeology)PolymerChemistryThermodynamicsNanotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

The influences of micro and nanoparticles on relaxation kinetics were studied using Differential Scanning Calorimetry and Dielectric Spectroscopy. The samples are composed of Quartz and/or Organically Modified Montmorillonite dispersed in an epoxy matrix. The heat capacity step, normalized to epoxy quantity, did not show significant variations, contrary to the glass transition temperature, which decreased for the nanostructured microcomposite. The main α relaxation parameters, fragility index and relaxation time at the glass transition temperature combined to show that, even though molecular movements are hindered by the presence of particles acting as obstacles, the global mobility of the molecular chains is increased because reticulation of the epoxy has been prevented. The local β relaxation, associated with crankshaft motions of the hydroxylether groups, is not affected by micro and nanoparticles. The modification of the γ relaxation associated with the ending epoxy groups suggests that different interfacial interactions occur with nanoparticles/epoxy and microparticles/epoxy. The relaxation parameters will be situated in the context of the dielectric breakdown results. Dielectric breakdown strengths are more modified by inhomogeneity in sample preparation than by addition of organically modified Montmorillonite to the composite material.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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.

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

Citations23
Published2013
Admission routes2
Has abstractyes

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