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Record W1977192865 · doi:10.1109/eic.2014.6869401

Comparative study of synthetic and natural clay filled PP films under surface discharges

2014· article· en· W1977192865 on OpenAlexfundno aff
Rohitha Dhara, Md. Afzalur Rab, Prathap Basappa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsnot available
FundersThomas Jefferson National Accelerator FacilityNational Research Council Canada
KeywordsMaterials scienceOrganoclayPolypropyleneNanocompositeComposite materialProfilometerSheet resistanceSurface roughness

Abstract

fetched live from OpenAlex

It is known from literature that addition of small wt% of nanofillers into pure polypropylene matrix improves the electrical characteristics by many folds. This study is intended to compare the effect of natural and synthetic nanocomposites on the PD resistance when introduced in small concentrations in the base PP material. This work investigates the effect of type of nanofiller and its content on the PD resistance of PP films under surface discharges. The sample aging is performed under a voltage that constantly increases with time (Ramp voltage) to minimize the effect of space charge redistribution during the aging process on the sample PD behavior and PD characteristics. Samples with 0, 2, 6 wt% of natural organoclay nanocomposites and 2, 4, 8 wt% of synthetic organo clay nanocomposites are considered for this purpose. These samples are subjected to surface PDs and the degree of surface erosion is quantified by Optical microscope and surface Profilometer.

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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