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Record W4323047900 · doi:10.18280/mmep.100108

Electrical Properties of Polyethylene Terephthalate under Hydrothermal Aging

2023· article· en· W4323047900 on OpenAlexvenueno aff
Zohra Ait-Saadi, Mohammed Nedjar

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsnot available
Fundersnot available
KeywordsPolyethylene terephthalateHydrothermal circulationMaterials sciencePolyethyleneComposite materialChemical engineeringEngineering

Abstract

fetched live from OpenAlex

During the working of rotating machines, dielectric materials are submitted to the action of humidity.At long term, their properties can degrade and the lifetime of devices will be reduced.This paper deals with the influence of hydrothermal aging on the electrical properties of polyethylene terephthalate.The insulation was aged in water at 80℃ and 100℃.The study shows that dielectric loss factor, permittivity, volume resistivity and dielectric strength were affected by aging.The change is attributed to the fact that the aging reduces molecular bonds causing a decrease in the viscosity.Thereby, the free volume and the mean free path raise.This process leads to the raise in the mobility of charge carriers.The evolution of activation energy versus aging time exhibits minimums and maximums resulting to the plasticization and the crosslinking of the material, respectively.The TGA thermograms indicate a variation of onset temperature with aging time.It was highlighted by TGA that the decomposition occurs following one step.The FTIR analysis points out a change in the intensities of the vibrational spectra after aging.The penetration of water within the dielectric induces differential inflations.The physical chemical analysis shows a modification in the molecular conformation of the polymer.The results are analysed and discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.615

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 head0.022
GPT teacher head0.187
Teacher spread0.166 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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