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Record W2613066396 · doi:10.1080/03602559.2017.1326138

Fabrication and Dielectric, Mechanical, and Thermal Properties of Low-Density Polyethylene (LDPE) Composites Containing Surface-Passivated Silicon (Si/SiO<sub>2</sub> Core/Shell Nanoparticles)

2017· article· en· W2613066396 on OpenAlex
Seyedbehzad Ghafarizadeh, M.F. Frechétte, Éric David

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymer-Plastics Technology and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsHydro-QuébecÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialDielectricLow-density polyethyleneNanocompositeNanoparticlePermittivitySiliconDielectric lossPolyethyleneFabricationNanotechnology

Abstract

fetched live from OpenAlex

Composites of low-density polyethylene containing between 1 and 5 wt% of Si/SiO2 core/shell nanoparticles were prepared by ball milling method. The thermal, mechanical, and dielectric properties of composites were investigated in terms of composition, frequency, and temperature. The results showed that the dielectric permittivity increased smoothly with a rise of Si/SiO2 particle. The dielectric permittivity and loss decreases and increases with temperature, respectively. The resistance of composites to erosion due to partial discharge was significantly improved by adding nanoparticles. The results have demonstrated that ball milling was an effective method for producing relatively homogeneous nanocomposite up to 4 wt% Si/SiO2.

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.

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 score0.099
Threshold uncertainty score0.944

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 head0.175
Teacher spread0.168 · 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