Piezoelectric Elements with PVDF–TrFE/MWCNT-Aligned Composite Nanowires for Energy Harvesting Applications
Bibliographic record
Abstract
A self-sustainable power supply function with flexibility, mechanical stability, and lightweight quality is among the required properties for pressure sensors and other low-power-consuming electronics and wearable devices. In this work, a poly(vinylidene fluoride-trifluoroethylene)/multi-walled carbon nanotube (P(VDF–TrFE)/MWCNT) composite was prepared to increase the electrical conductivity of the piezoelectric polymer and, thus, improve its electrical power generation capabilities. It was soaked by injection molding through an anodic aluminum oxide membrane to align vertically with the dipoles and exclude the possibility of dipole moment quenching. The composite membrane-type element exhibited an excellent piezoelectric coefficient d33 of 42 pC/N at a frequency of 50 Hz and an applied force intensity of 10 N, while the sensitivity was ~375 µV/g, which is favorable for self-powered pressure sensor application. The resulting composite element was utilized to generate the piezoelectric signal and to investigate the dependence of the electromechanical behavior on the surface roughness, morphology, and contact interface resistance.
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 | 0.000 |
| 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) | 0.001 | 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".