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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 OpenAlex
Adel Zyane, François Brouillette, Ahmed Belfkira, Romain Lucas, Pascal Marchet

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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 BaTiO 3 . 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/BaTiO 3 , the three‐phase composite EVA‐VeoVa/BaTiO 3 /microcrystalline cellulose showed an improvement of the BaTiO 3 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.

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 score0.111
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
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