MétaCan
Menu
Back to cohort
Record W1528944984 · doi:10.1109/elinsl.1990.109717

The dielectric behavior of plasma-treated insulator surfaces

2002· article· en· W1528944984 on OpenAlexaff
Demin Tu, Xuezhong Liu, Liangyu Gao, Qichang Liu, K. C. Kao

Bibliographic record

VenueIEEE International Symposium on Electrical Insulation · 2002
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPlasmaInsulator (electricity)UltravioletMaterials scienceThermal stabilityDielectricArc flashSolventInfrared spectroscopyComposite materialInfraredChemical engineeringAnalytical Chemistry (journal)ChemistryOptoelectronicsChromatographyOrganic chemistryOptics

Abstract

fetched live from OpenAlex

A novel technique of treating insulator surfaces by means of a suitable plasma is presented. It suppresses the presence of hydrophilic ions or groups on the surfaces of glass and porcelain insulators and makes the formation of a hydrophobic surface possible. Experimental results show that the plasma-treated surfaces of the glass and the porcelain insulators exhibit excellent stability during accelerated aging test under thermal stress and exposure to ultraviolet radiation, and also after immersion in solvent, acid, or alkali solutions. The AC flashover voltage is 56% higher for the insulators with plasma treatment than those without such treatment under an artificial fog condition (with salt density of 0.1 mg/cm/sup 3/). The surface resistance is about three times higher under the same artificial fog condition for the insulators with plasma treatment. On the basis of an analysis of secondary ion mass spectroscopy and infrared spectra results, the mechanisms responsible for the formation of a stable and effective hydrophobic surface by plasma treatment are discussed.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueIEEE International Symposium on Electrical InsulationSame topicHigh voltage insulation and dielectric phenomenaFrench-language works237,207