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Record W2513933191 · doi:10.1002/app.44333

BaTiO<sub>3</sub> incorporation effect on the dielectric properties of polymer from aqueous emulsion: An enhanced dispersion technique

2016· article· en· W2513933191 on OpenAlexaff
Adel Zyane, Ahmed Belfkira, François Brouillette, Pascal Marchet, Romain Lucas

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMaterials scienceDielectricComposite materialDispersion (optics)Vinyl acetateEmulsionVinyl esterComposite numberPolymerAqueous solutionCopolymerViscosityChemical engineeringPolymer chemistryOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

ABSTRACT An economic and environment friendly process was adapted to synthesize new dielectric composite materials. Using ethylene vinyl acetate (EVA)/vinyl ester of versatic acid (VeoVa) terpolymer as an aqueous emulsion provides a homogenous dispersion of BaTiO3 (BT) particles, due to the high viscosity and polarity of the vinyl resin (VR). Composites films were obtained from these dispersions by water evaporation. The evolution of the dielectric properties as a function of the BaTiO3 content, was correctly fitted by a Maxwell‐Garnett model. This fitting of the experimental curve shows a good dispersion of filler in the vinyl resin and the particles separation by a layer of resin as expected for the preparation method used in this study. The VR/BT composites show good synergy between the dielectric properties of the different phases of the composites due to the formation of macrodipoles and to the strong interactions between polar EVA/VeoVa groups and the BaTiO3 particles surface. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 44333.

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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
Published2016
Admission routes1
Has abstractyes

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