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Record W2083089847 · doi:10.1109/ceidp.2008.4772775

Water as a Digression Relative to the Dielectric Response in the Frequency Domain for Polymer Composites

2008· article· en· W2083089847 on OpenAlexaff
A. Sami, M. Frechette, Éric David, S. Savoie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsHydro-QuébecÉcole de Technologie Supérieure
Fundersnot available
KeywordsDielectricComposite materialMaterials scienceEpoxyPolymerDielectric lossRelaxation (psychology)

Abstract

fetched live from OpenAlex

The presence of water in insulating solids is known to affect the dielectric properties. Although relaxation processes evolved with the excitation frequency and temperature, further characteristics unfold when contaminant with a high dielectric constant, such as water is involved. When the material consists of a microcomposite, and especially in the advent of incorporation of nanofillers, it is speculated that water molecules may intervene in several different manners. In order to investigate further the potential digression caused by water, dielectric spectroscopy was used on prepared composite specimen. Different types of epoxy microcomposites exhibiting a high content of quartz, i.e. 60% wt., were prepared. The effects due to the variation of the water content and presence of nanostructures (nanoclay) on the dielectric response will be discussed. With a weight intake of 0.2% of water, the capacity of the epoxy microcomposite exhibits a slight increase (2%) at lower frequencies and losses are found to increase while a shift towards higher frequencies occurs.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.000

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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