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Record W2150732076 · doi:10.1109/ceidp.2009.5377780

Dielectric properties of polypropylene loaded with synthetic organoclay

2009· article· en· W2150732076 on OpenAlexaff
A. Bulinski, S.S. Bamji, M. Abou-Dakka, Y. Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials sciencePolypropyleneDielectricDielectric lossComposite materialOrganoclayNanocompositeConductivityNanoparticleDielectric strengthDielectric spectroscopyElectric fieldRelaxation (psychology)Electrical resistivity and conductivityElectrodeOptoelectronicsNanotechnologyElectrical engineeringChemistry

Abstract

fetched live from OpenAlex

The incorporation of synthetic silica nanoparticles into polypropylene is shown to increase the ac breakdown strength compared to unfilled material. This breakdown strength stays unchanged during application of a 40 kV/mm dc field for up to 500 h at both, room temperature and 90°C. Dielectric spectroscopy shows an increase of dielectric loss factor, tan¿, with nanoflller concentration and a distinct relaxation around 60°C. The increase of tan¿ caused by nanoparticles is moderate and thus manageable in practical applications. Subjecting specimens to a dc field did not significantly change the dielectric loss spectra. The dc conductivity of the materials with nano-filler was found to be higher than for unfilled materials. This is believed to be caused by the overlapping of the diffuse double layers surrounding nanoparticles, which provide a path for the migration of electric charge. Aging nanocomposites in a dc field resulted in the increase of conductivity but the increments were significantly smaller than those observed in the material without organoclay.

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 score0.003

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

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