Piezoelectric Polymer and Paper Substrates: A Review
Bibliographic record
Abstract
Polymers and papers, which exhibit piezoelectricity, find a wide range of applications in the industry. Ever since the discovery of PVDF, piezo polymers and papers have been widely used for sensor and actuator design. The direct piezoelectric effect has been used for sensor design, whereas the inverse piezoelectric effect has been applied for actuator design. Piezo polymers and papers have the advantages of mechanical flexibility, lower fabrication cost and faster processing over commonly used piezoelectric materials, such as PZT, BaTiO₃. In addition, many polymer and paper materials are considered biocompatible and can be used in bio applications. In the last 20 years, heterostructural materials, such as polymer composites and hybrid paper, have received a lot of attention since they combine the flexibility of polymer or paper, and excellent pyroelectric and piezoelectric properties of ceramics. This paper gives an overview of piezoelectric polymers and papers based on their operating principle. Main categories of piezoelectric polymers and papers are discussed with a focus on their materials and fabrication techniques. Applications of piezoelectric polymers and papers in different areas are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".