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Record W3038997006 · doi:10.1149/2162-8777/aba1fe

Evaluation and Optimization of Dielectric Properties of PVDF/BaTiO<sub>3</sub> Nanocomposites Film for Energy Storage and Sensors

2020· article· en· W3038997006 on OpenAlex

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.

Bibliographic record

VenueECS Journal of Solid State Science and Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsMaterials scienceBarium titanatePolyvinylidene fluorideDielectricComposite materialNanocompositeResponse surface methodologyDielectric strengthBall millVolume fractionPolymerCeramicOptoelectronicsComputer science

Abstract

fetched live from OpenAlex

Flexible nanodielectric are largely used in sensors and power sources for new generation of electronic devices. The most conventional methods used to design and manufacture these nanodielectric materials with desired properties are time-consuming and unable to determine interactions between the input parameters. In this study, a response surface methodology (RSM) is proposed to design polyvinylidene fluoride (PVDF)/barium titanate (BT) nanocomposites film prepared by ball milling process with optimized dielectric breakdown strength. Interaction effects of three individual control variables on nanocomposites dielectric strength were studied using RSM. Numerical optimization was employed to obtain the optimum factors for maximum dielectric breakdown strength. It is indicated that the optimum value of dielectric breakdown strength was 219.01 kV mm −1 , when input control factors were BT size of 6 nm, BT volume fraction of 10 vol% and milling time of 43.74 min.

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 score0.283
Threshold uncertainty score0.221

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 science0.0000.000
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 head0.010
GPT teacher head0.208
Teacher spread0.199 · 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