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Record W3046446233 · doi:10.1177/0021955x20945666

Microcellular structure assisted phase transformation of polyvinylidene fluoride/titanium dioxide nanocomposites

2020· article· en· W3046446233 on OpenAlexafffund
Yu‐Chen Sun, Jennifer D’Cunha, Hani E. Naguib

Bibliographic record

VenueJournal of Cellular Plastics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaGovernment of Ontario
KeywordsMaterials sciencePolyvinylidene fluorideNanocompositeComposite materialTitanium dioxideSupercritical carbon dioxideDielectricPhase (matter)CompoundingComposite numberFluorideFoaming agentChemical engineeringSupercritical fluidPolymerPorosity

Abstract

fetched live from OpenAlex

A novel nanocomposite foam with microcellular structures based on poly(vinylidene fluoride) (PVDF) and titanium dioxide (TiO 2 ) was fabricated by the combination of melt compounding and supercritical carbon dioxide (scCO 2 ) foaming. To improve its dielectric performance, silane modified and unmodified titanium dioxide nanoparticles were added as reinforcing fillers at low weight percentages (0.5, 1, and 5 wt%) during the melt blending process. It was found that the incorporation of nanoparticles had a strong influence on cell morphology. As a result, the foaming process significantly altered the dielectric, and mechanical properties of the composite foams. The dielectric constants of the composite foams were no longer frequency dependent while tan delta was lowered at least by a factor of 10. Furthermore, the porous structure generated by foaming also assisted the α-to-β phase transformation of the PVDF matrix in a way similar to mechanical stretching. Such method is superior to other phase transformation techniques since β-phase PVDF can be produced in bulk geometries instead of a thin film configuration.

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 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 score0.296
Threshold uncertainty score0.697

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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 teacher head, 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

Citations3
Published2020
Admission routes2
Has abstractyes

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