Piezoelectric property improvement of polyethylene ferroelectrets using postprocessing thermal‐pressure treatment
Why this work is in the frame
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Bibliographic record
Abstract
In this work, biaxially stretched polymer foams with well‐defined cellular structures were prepared from polyethylene via blown‐film extrusion and subjected to corona charging to produce a piezoelectric response. The charging parameters were first optimized in terms of charging voltage and needle distance, as well as the gas type and pressure to investigate their effect on the piezoelectric coefficient ( d 33 ). The results show that samples charged under nitrogen (N 2 ) at 100 kPa had better d 33 coefficient than those charged under ambient air or N 2 at 20 kPa. Moreover, 2 different thermal pressure treatments were imposed to obtain an optimized eye‐like cellular structure with different cell aspect ratios (AR). The results showed that when the cells were elongated in both the longitudinal and transverse directions (higher AR), higher d 33 coefficients were achieved. From all the samples produced, the best results were obtained for a longitudinal aspect ratio (AR‐L) of 7.1, a transversal aspect ratio (AR‐T) of 4.6, and a relative foam density of 0.52 leading to a d 33 coefficient of 935 pC/N. This coefficient was further increased using reverse charging and multilayered films, reaching a maximum of 2550 pC/N. This value is much higher than typical ones reported so far for any polyethylene and polypropylene ferroelectrets. These results could increase the use of polyethylene in piezoelectric applications as these materials are very attractive for the large‐scale production of electret‐based sensors and transducers due to their low cost and easy processing.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 it