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Record W2611780056 · doi:10.1002/pen.24602

Dielectric characterization of thermally aged recycled Polyethylene Terephthalate and Polyethylene Naphthalate reinforced with inorganic fillers

2017· article· en· W2611780056 on OpenAlexafffund
Fouzia Mebarki, Éric David

Bibliographic record

VenuePolymer Engineering and Science · 2017
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolyethylene terephthalateComposite materialPolyethylene naphthalateDielectricCrystallinityDifferential scanning calorimetryPolyethyleneGlass transitionPolymer

Abstract

fetched live from OpenAlex

In order to assess the influence of the operating temperature on the dielectric properties of recycled Polyethylene Terephthalate (PET) and Polyethylene Naphthalate (PEN) reinforced with inorganic fillers, a dielectric and thermal investigation was undertaken. Specimens were thermally aged at several temperatures between 90 and 200°C for 360 h. The effect of thermal aging time on dielectric and thermal properties was also investigated. The dielectric response and breakdown strength properties were evaluated. Differential scanning calorimetry (DSC) measurements showed that the degree of crystallinity and the glass transition temperature increased with aging temperature and duration. The data obtained showed that these materials exhibited a good resistance to thermal aging at temperatures below 140°C. Furthermore, it was found that the dielectric strength of the recycled PET and PEN and their composites decreased considerably for temperatures of over 170°C. POLYM. ENG. SCI., 2017. © 2017 Society of Plastics Engineers

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 score0.002

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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
Published2017
Admission routes2
Has abstractyes

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