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Record W2160791413 · doi:10.1109/ceidp.2010.5724058

Study of ice accumulation on nanocomposite semiconducting coatings

2010· article· en· W2160791413 on OpenAlexafffund
Gelareh Momen, M. Farzaneh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaHydro-QuébecUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversité du Québec à Chicoutimi
KeywordsMaterials scienceNanocompositeDielectricSilicone rubberComposite materialContact angleCoatingNanoparticleDielectric lossNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

Nanocomposite materials are attracting the attention of many researchers in the field of dielectric and electrical insulation. In this paper, we produced a dielectric coating on a glass surface by incorporating ZnO nanoparticles into RTV silicone rubber. Different concentrations of nanofiller (up to 40 % ZnO) were investigated. Physicochemical characterization of these coatings was performed using SEM and water contact angle (WCA). The influence of the nanofiller concentration in a specific frequency range (40 Hz to 2 MHz) on the dielectric behavior of the nanocomposites was analyzed as well. The results showed that the dielectric constant of nanocomposites increases with the ZnO weight content. Then, ice was accreted on the surfaces to evaluate the minimum energy required to prevent ice accumulation based on the Joule heating effect. The results are discussed and emphasis is placed on the semiconducting properties influencing ice accumulation.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.044
GPT teacher head0.316
Teacher spread0.272 · 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

Citations11
Published2010
Admission routes2
Has abstractyes

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