Fabrication and characterization of β-poly(vinylidene fluoride)/silane-treated titanium dioxide dielectric nano-composites
Bibliographic record
Abstract
Dielectric materials are commonly known as electrical insulators that can be polarized under strong electrical field. Currently, emerging dielectric research interests are focusing on nanoparticles mixed polymer based composites, since such materials demonstrated an astonishing increase in dielectric performance when compared to neat polymer matrix, due to the exponential increase in the interfacial area between the nanoparticles and polymer. Such findings infer that particle dispersion plays a critical role for the overall dielectric performance. In this study, we present a continuous manufacturing process consists of extrusion and stretching for Poly(vinylidene fluoride)/silane-treated titanium dioxide (PVDF/silane-treated TiO2) flexible organic/inorganic polymer nanocomposites and the experimental result. Our results show that melt blending process is able to break down both silane treated and untreated micro-size TiO2 agglomerates with extremely well dispersion in PVDF matrix. Follow-up studies and characterizations indicated that the material performances such as dielectric constant and dielectric loss are either similar or surpass the sample prepared via solvent casting and the effects of silane treatment are also discussed. A number of methods was used to characterize the composites, including AFM for dispersion verification and dielectric spectroscopy for dielectric analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".