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Record W3156900385 · doi:10.2202/1553-779x.1477

Comparison of Available Silicone Rubber Coatings for High Voltage Applications

2008· article· en· W3156900385 on OpenAlexaff
Majid Sanaye‐Pasand, Ali Naderian Jahromi, Ayman El‐Hag, Shesha Jayaram

Bibliographic record

VenueInternational Journal of Emerging Electric Power Systems · 2008
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSilicone rubberMaterials scienceComposite materialVulcanizationFiller (materials)Thermogravimetric analysisCoatingThermal conductivityNatural rubberSilicone

Abstract

fetched live from OpenAlex

This paper presents the results of an investigation on evaluating room-temperature-vulcanized (RTV) silicone rubber coatings for high-voltage insulators of substations and overhead transmission lines. It is based on tests conducted for three different available RTVs that are currently used by utilities. The study compared three commercial coatings: RTV-A and RTV-B filled with ATH (aluminum tri-hydrate) as the primary filler, and RTV-C filled with silica. The relative comparison between them is based on their electrical and chemical characteristics. The evaluation employed standard and research-based evaluation methods, including the salt fog test, the inclined plane test, thermo-gravimetric analyses (TGA), thermal conductivity measurement, soaking in water, the mechanical test, and SEM photographs. Results of the inclined plane test showed that the silica-filled coating is not resistive against tracking and erosion compared with ATH-filled coatings. It was observed that filler size is one of the main reasons for better performance of RTV-A compared with RTV-B, whereas both have ATH as the primary filler. The higher thermal conductivity of RTV-A resulted in a very good performance in the salt fog test and the IPT.

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 score0.297
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.306
Teacher spread0.273 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

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