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Record W2063629880 · doi:10.1109/ceidp.2013.6748127

Thermal and dielectric properties of clay/epoxy nanocomposites with low percentage of graphite oxide

2013· article· en· W2063629880 on OpenAlexaff
I. Preda, J. Castellon, M.F. Frechétte, S. Agnel, Fengge Gao, Rinat Nigmatullin, Simon Thompson, Nicola Freebody, A. S. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMaterials scienceGraphite oxideComposite materialNanocompositeEpoxyGraphiteDielectricOxideThermal conductivityPercolation thresholdPercolation (cognitive psychology)Dielectric strengthThermal conductionElectrical resistivity and conductivityElectrical engineering

Abstract

fetched live from OpenAlex

This work is concerned with hybrid nanocomposite materials that contain both clay and graphite oxide incorporated in an epoxy matrix. In order to assess the challenge of improving the thermal conductivity while not changing or even improving the electrical properties, a new composite system was designed by applying an additional thermal conductive phase represented by the graphite oxide. In this paper, extremely low quantities of graphite oxide were used (going from 0.0012 to 0.0025 wt%) in order to avoid the forming of an electrical conduction percolation network. The impact of the additional filler on the thermal and electrical properties of the clay/graphite oxide/epoxy nanocomposites were investigated. Using thermal conductivity measurements, it was found that even for low quantities of GO filler added to the clay/epoxy nanocomposite material, the thermal conductivity is improved significantly. Moreover, using dielectric characterization techniques (Dielectric Spectroscopy, Space Charge or Dielectric Breakdown measurements), it was found that the electrical properties of the material remain unchanged or are slightly improved by the extra graphite oxide filler.

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 score0.005
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.181
Teacher spread0.174 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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