Review of Piezoelectric Properties and Power Output of PVDF and Copolymer-Based Piezoelectric Nanogenerators
Bibliographic record
Abstract
The highest energy conversion efficiencies are typically shown by lead-containing piezoelectric materials, but the harmful environmental impacts of lead and its toxicity limit future use. At the bulk scale, lead-based piezoelectric materials have significantly higher piezoelectric properties when compared to lead-free piezoelectric materials. However, at the nanoscale, the piezoelectric properties of lead-free piezoelectric material can be significantly larger than the bulk scale. The piezoelectric properties of Poly(vinylidene fluoride) (PVDF) and Poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) lead-free piezoelectric nanomaterials are reviewed and their suitability for use in piezoelectric nanogenerators (PENGs) is determined. The impact of different PVDF/PVDF-TrFE composite structures on power output is explained. Strategies to improve the power output are given. Overall, this review finds that PVDF/PVDF-TrFE can have significantly increased piezoelectric properties at the nanoscale. However, these values are still lower than lead-free ceramics at the nanoscale. If the sole goal in developing a lead-free PENG is to maximize output power, lead-free ceramics at the nanoscale should be considered. However, lead-free ceramics are brittle, and thus encapsulation of lead-free ceramics in PVDF is a way to increase the flexibility of these PENGs. PVDF/PVDF-TrFE offers the advantage of being nontoxic and biocompatible, which is useful for many applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".