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Record W4385783181 · doi:10.1002/pen.26441

Investigation and characterization of dielectric, thermal, and chemical properties of recycled <scp>high‐density polyethylene</scp> blended with virgin <scp>polyethylene</scp>

2023· article· en· W4385783181 on OpenAlexaff
Iman Shirzaei Sani, Nicole R. Demarquette, Éric David

Bibliographic record

VenuePolymer Engineering and Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHigh-density polyethyleneMaterials scienceDielectricPolyethyleneComposite materialImpurityDielectric lossCharacterization (materials science)Organic chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Dielectric performance of post‐consumer recycled HDPE blended with virgin HDPE was investigated to evaluate the possibility of using these materials for the insulation of electrical wires and cables. The presence of organic and inorganic impurities was investigated using thermal and chemical methods (TGA, DSC, and EDX). The characterization of impurities revealed different amount of inorganic impurities in recycled material that was depending on the execution of melt filtration by the recycler to prepare the recycled material. Higher values of both dielectric losses and dielectric constant were observed for post‐consumer recycled PE, with the dielectric loss of recycled material almost 17 times higher than the one of virgin PE at power frequency (60 Hz). The short‐term breakdown strength of post‐consumer recycled HDPE was observed to be slightly lower than that of virgin PE. The experimental result showed that blending the recycled stream with virgin materials was effective in order to enhance dielectric properties of recycled material. In this regards, dielectric losses decreased by almost 50% when 50% of virgin HDPE was added to the recycled material. In addition, breakdown strength was improved when virgin HDPE was added.

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 score0.046
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.187
Teacher spread0.176 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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