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Record W2910479233 · doi:10.1002/app.47540

Effect of the inflation strategy on the piezoelectric response of cellular poly(vinylidene fluoride) ferroelectret

2019· article· en· W2910479233 on OpenAlexafffund
Nusrat Jahan, Frej Mighri, Denis Rodrigue, Abdellah Ajji

Bibliographic record

VenueJournal of Applied Polymer Science · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversité LavalPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePiezoelectricityComposite materialFluoridePolymerDiffusionPiezoelectric coefficientMicrostructureChemistryThermodynamicsInorganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT Cellular poly(vinylidene fluoride) (PVDF)‐montmorillonite (MMT)‐calcium carbonate (CaCO3) based piezoelectret films were produced using uniaxial stretching and various gas diffusion expansion (GDE) treatments followed by corona charging. The cross section micrographs revealed that a cellular structure was developed at the interface between the solid CaCO3 particles and the polymer matrix. Sample characterization showed that the piezoelectric coefficient (d33) was a function of the external gas pressure and treatment temperature, as well as the way they were applied. The results also showed that the maximum d33 was obtained when the inflation pressure was increased stepwise from 3 to 5 MPa at a constant treatment temperature of 130°C for a certain period of time. Finally, the overall electromechanical performance of the cellular PVDF piezoelectrets is discussed in terms of the GDE procedure and the developed microstructures. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47540.

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 head0.211
Teacher spread0.206 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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