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Record W2777960266 · doi:10.1002/app.46147

Toward green three‐phase composites with enhanced dielectric permittivity

2017· article· en· W2777960266 on OpenAlexafffund
Adel Zyane, François Brouillette, Ahmed Belfkira, Romain Lucas, Pascal Marchet

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrocrystalline celluloseMaterials scienceComposite materialDielectricComposite numberPermittivityPhase (matter)Ethylene-vinyl acetateDispersion (optics)CelluloseRelative permittivityDielectric lossCopolymerChemical engineeringOrganic chemistryPolymerChemistryOptics

Abstract

fetched live from OpenAlex

ABSTRACT This work aims to investigate the dielectric potential of microcrystalline cellulose, a green biosourced material, as a third constituent in the three‐phase composites based on ethylene vinyl acetate‐vinyl ester of versatic acid (EVA‐VeoVa) terpolymer and BaTiO3. For that, new green three‐phase composites were prepared using an economic and green process, with simple implementation at room temperature and using water as a solvent. Compared with the binary composite EVA‐VeoVa/BaTiO3, the three‐phase composite EVA‐VeoVa/BaTiO3/microcrystalline cellulose showed an improvement of the BaTiO3 particles dispersion, enhanced relative permittivity, and reduced dielectric loss, which explains the significance of this study. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46147.

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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