Improving piezoelectric and pyroelectric properties of electrospun PVDF nanofibers using nanofillers for energy harvesting application
Bibliographic record
Abstract
In this study, it was aimed to increase the piezoelectric and pyroelectric properties of electrospun polyvinylidene fluoride (PVDF) nanofibers simultaneously by using specific nanofillers. Graphene oxide (GO), graphene, and halloysite nanotubes with different concentrations (0, 0.05, 0.4, and 1.6% wt/wt) were combined with PVDF solution and were fabricated in the form of nanofibers through electrospinning. Pyroelectric properties of samples were measured by submerging sealed samples in hot water (360°K) and ice (270°K). The piezoelectric properties of the samples were evaluated through bending tests. The microstructural, mechanical, and thermal properties of the electrospun PVDF nanocomposite were investigated using scanning electron microscope, Instron instrument, and thermogravimetric analysis, respectively. To further support the experimental observations for generating electric voltage in the bended nanogenerator, the PVDF nanogenerator (PNG) was also modeled by a finite element analysis based on the theory of linear piezoelectricity using COMSOL Multiphysics simulation software. Experimental results showed that adding nanofillers could improve the piezoelectric and pyroelectric properties of all samples, associated with the increment of β‐phase in the nanofibers. It was concluded that adding nanofillers could increase pyroelectricity about 50% more than piezoelectricity in pristine PVDF nanofiber web. The PNG containing 1.6 wt% GO showed the highest efficiency in terms of piezoelectricity and pyroelectricity. In addition, the results showed that the ratio of piezoelectric to pyroelectric coefficients was constant (~1.5) and it was independent of the nanofiller type and content. The effect of external force and vibration frequency on the output voltage was also investigated. Increasing the compressive force and vibration frequency caused a greater output voltage. Finally, the fabricated nanogenerator was integrated on insole and elbow to investigate its energy harvesting capabilities from body movement.
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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".