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Record W3033187441 · doi:10.1002/app.49514

Enhancement in electrical and thermal performance of high‐temperature vulcanized silicone rubber composites for outdoor insulating applications

2020· article· en· W3033187441 on OpenAlexafffund
Sohrab Azizi, Gelareh Momen, Claudiane Ouellet‐Plamondon, Éric David

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversité du Québec à ChicoutimiÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialVulcanizationSilicone rubberThermogravimetric analysisThermal stabilityDielectricFumed silicaSiliconeFiller (materials)Fourier transform infrared spectroscopyNatural rubberChemical engineering

Abstract

fetched live from OpenAlex

Abstract High‐temperature vulcanized silicone rubber composites are highly desirable as outdoor insulating materials due to their immense thermal and electrical performance. The aim of this work is to study the role of co‐combined fillers (modified fumed silica [MFS], titanium dioxide [TiO 2 ], with graphene [G]) on electrical and thermal properties of silicone rubber (S) composites. The dielectric response of S/MFS_10 phr and S/TiO 2 _20 composites tailored with 2 phr G was characterized by broadband dielectric spectroscopy. The hybrid filler/composites were found to show higher thermal stability when 2 phr G was added. In addition, a low quantity of G filler was found to slightly increase the AC dielectric breakdown strength of the S/MFS_10 and S/TiO 2 _20, where an improvement of 3 and 5% was found, respectively. Several steps were observed in the thermal decomposition of the S rubber composites by thermogravimetric analysis‐Fourier‐transform infrared spectroscopy. Our findings revealed great potentials for fabricating hybrid‐filler/silicone rubber composites with enhanced electrical and thermal properties for outdoor insulating applications.

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)0.001
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 score0.004
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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