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Record W4213423244 · doi:10.3390/en15051677

Self-Healing Silicones for Outdoor High Voltage Insulation: Mechanism, Applications and Measurements

2022· article· en· W4213423244 on OpenAlexaff
Fadi Z. Kamand, Basharat Mehmood, Refat Atef Ghunem, Mohammad K. Hassan, Ayman El‐Hag, Leena Al‐Sulaiti, Ahmed Abdala

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
Fundersnot available
KeywordsSilicone rubberMaterials scienceComposite materialSelf-healingNatural rubberSiliconeElastomer

Abstract

fetched live from OpenAlex

This paper discusses the state of the art in the application of self-healing silicone-based materials for outdoor high-voltage insulation. Both the dynamic behavior of the dimethyl side groups of silicone rubber and the diffusion of a bulk siloxane to maintain low surface energy are respectively reported as intrinsic mechanisms responsible for the self-healing of silicone rubber. Localization, temporality, mobility, and the type of synthesis are the aspects defining the efficiency of the self-healing ability of silicone rubber. In addition, the deterioration of the self-healing ability with filler loaded into silicone rubber insulation housing composites is discussed. Taking the self-healing property into consideration among the other properties of silicone rubber insulators, such as tracking and erosion resistance, can be a useful design practice at the material development stage. Hydrophobicity retention, recovery, and transfer measurements are discussed as useful indicators of the self-healing ability of silicone rubber. Nevertheless, there remains a need to standardize them as design tests at the material development stage. The paper is intended to shed the light on the hydrophobicity recovery, a key material design parameter in the development of silicone rubber outdoor insulating composites, similar to the tracking and erosion resistance.

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 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 score0.248
Threshold uncertainty score0.568

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 studies0.0010.000
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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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