Polymer ferroelectret based on polypropylene foam: piezoelectric properties prediction using dynamic mechanical analysis
Bibliographic record
Abstract
Thin polypropylene (PP) foam films were produced by continuous extrusion using supercritical nitrogen (N 2 ) and then charged via corona discharge. The samples were characterized by dynamic mechanical analysis as a simple method to predict the piezoelectric properties of the cellular PP obtained. The results were then related to morphological analysis based on scanning electron microscopy and mechanical properties in tension. The results showed that the presence of a nucleating agent (CaCO 3 ) substantially improved the morphology (in terms of cell size and cell density) of the produced foam. Also, an optimization of the extrusion (screw design, temperature profile, blowing agent, and nucleating agent content) and post‐extrusion (calendering temperature and speed) conditions led to the development of a stretched eye‐like cellular structure with uniform cell size distribution. This morphology produced higher storage and loss moduli in the machine (longitudinal) direction than for the transverse direction, as well as higher piezoelectric properties. The morphological and mechanical results showed that higher cell aspect ratio led to lower Young's modulus, which is suitable to achieve higher piezoelectric properties. Finally, the best quasi‐static piezoelectric d 33 coefficient was 550 pC/N for a cellular PP ferroelectret having a uniform eye‐like cellular structure using N 2 as the ionizing gas inside the cells, while the highest value was only 250 pC/N when air was used. Hence, the value of d 33 can be improved by more than 100% just by replacing air with N 2 as the ionizing gas. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.000 | 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".