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Record W2887249078 · doi:10.1155/2018/4682076

Dielectric Properties of TiO<sub>2</sub>/Silicone Rubber Micro‐ and Nanocomposites

2018· article· en· W2887249078 on OpenAlexafffund
Fatima Zahra Madidi, Gelareh Momen, M. Farzaneh

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversité du Québec à Chicoutimi
FundersHydro-QuébecUniversité du Québec à Chicoutimi
KeywordsMaterials scienceSilicone rubberNanocompositeComposite materialDielectricSiliconeNatural rubberOptoelectronics

Abstract

fetched live from OpenAlex

Room temperature vulcanized (RTV) silicone rubber SR/TiO2nanocomposites and microcomposites are developed and characterized, and their dielectric behaviour and electrical conductivity are studied in this paper. We demonstrate that the surfactant Triton X‐100 greatly improves the dispersal of micro‐ and nanoparticles across the surface to produce more homogeneous composites that have improved dielectric properties. This heightened dispersal with the presence of a surfactant is also confirmed by SEM analysis. We also discuss the influence of the filler concentration and particle size on the dielectric behaviour of the nanocomposites and the microcomposite surfaces having a frequency range of 40 Hz to 2 MHz. The dielectric properties are improved by the introduction of 5 wt.% and 10 wt.% TiO2nano‐ and microparticles. Furthermore, there is an improvement in the permittivity values for the microcomposites compared to the nanocomposites for all frequencies. This finding is of great importance for high‐voltage electrical insulation.

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 score0.002

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 head0.003
GPT teacher head0.179
Teacher spread0.175 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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