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Analysis of Dielectric Properties of Hybrid Silicone Rubber Composites at Elevated Temperatures

2020· article· en· W3171697311 on OpenAlexaff
Khadija Kanwal Khanum, Shesha Jayaram

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceComposite materialSilicone rubberDielectricPermittivityFiller (materials)Dielectric lossBoron nitrideNatural rubberDissipation factorSiliconeRelative permittivity

Abstract

fetched live from OpenAlex

In this study, silicone rubber, is filled with micro-silica and hexagonal boron nitride (h-BN) nanosheets, by varying the ratio between the two fillers, but keeping the filler to matrix ratio same in all composites. The effect of increase in h-BN content on dielectric properties at elevated temperature is analyzed. Negligible effect on relative permittivity and dissipation factor at 60 Hz and 1 kHz, even at 100 °C implies that low amounts of h-BN do not alter the dielectric responses of these hybrid composites. Furthermore, addition of h-BN has improved the thermal properties as reflected by a lower weight loss compared to only silica filled composites with laser erosion.

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 score0.073
Threshold uncertainty score0.445

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)0.0000.000

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 head0.009
GPT teacher head0.174
Teacher spread0.165 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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