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Record W2174826641 · doi:10.1109/ceidp.2005.1560607

Silicone rubber dielectrics modified by inorganic fillers for outdoor high voltage insulation applications

2005· article· en· W2174826641 on OpenAlexaff
E.A. Cherney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceComposite materialSilicone rubberDielectricAluminium powderBarium titanateRelative permittivityFiller (materials)CeramicThermal conductivityConductivityPermittivityDielectric lossComposite numberAluminium

Abstract

fetched live from OpenAlex

The paper discusses the mechanisms by which inorganic fillers in silicone rubber dielectrics enhance the properties of thermal conductivity, relative permittivity, and electrical conductivity making them useful in outdoor high voltage insulation applications. The addition of alumina tri-hydrate or silica fillers to silicone elastomers, forming binary composites with enhanced thermal conductivity, is discussed in relation to filler type, particle size, shape, and concentration, and its use as a housing material for non-ceramic insulators to minimize material erosion at dry band arcing sites by lowering hot spot temperature. Also discussed is the enhanced relative permittivity of silicone dielectrics that is obtained through the addition of barium titanate powder which can be further increased with the addition of aluminium powder forming a tertiary composite, resulting in a significant grading of the surface electric field when applied as a housing material to high voltage bushings. Controlled electrical conductivity of silicone dielectrics is discussed through the use of antimony-doped tin oxide filler binary composites and when applied as a housing material to outdoor bushings, the pollution performance is greatly enhanced.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score1.000

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)0.0010.001

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 head0.236
Teacher spread0.225 · 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.

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

Citations23
Published2005
Admission routes1
Has abstractyes

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