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Record W2170059059 · doi:10.1109/iseim.1995.496538

Low molecular weight silicone fluid content and diffusion in RTV silicone rubber coating

2002· article· en· W2170059059 on OpenAlexaff
Huiqiu Deng, R. Hackam, E.A. Cherney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsVulcanizationMaterials scienceSilicone rubberCoatingSiliconeComposite materialPolymerDiffusionNatural rubberCuring (chemistry)

Abstract

fetched live from OpenAlex

Room temperature vulcanizing (RTV) silicone rubber has been widely used to coat porcelain insulators to render water repellency to prevent formation of water filming on the surface and thus to suppress the leakage current and consequent flashover. The hydrophobicity is maintained even after a layer of contamination has built-up on the surface. This has been attributed to the diffusion of the low molecular weight (LMW) silicone fluid from the bulk to the surface of the RTV and then on to the surface of the pollution deposits. It has been reported that there was a reduction in the quantity of LMW polymer chains on the surface of an aged RTV specimen when compared to a virgin specimen. This paper explores some of the factors governing the lifetime of the RTV coating. Alumina trihydrate (ATH) filler and various formulations were used. The content of LMW silicone fluid and the diffusion of LMW from the bulk to the surface were determined in RTV coatings having thicknesses from 0.17 to 0.99 mm, ATH particles from 1.0 to 75 /spl mu/m and different carrier solvents, by using extraction techniques in analytical hexane. The roles of the LMW content and the diffusion process in the lifetime of the coating were evaluated for different formulations of RTV.

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: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.998

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)0.0030.000

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 head0.020
GPT teacher head0.212
Teacher spread0.192 · 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

Citations12
Published2002
Admission routes1
Has abstractyes

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