Fully Solution‐Processed Polymeric Multilayer Piezoelectric Devices
Bibliographic record
Abstract
Abstract This study demonstrates fully solution‐processed polymeric multilayer piezoelectric devices. The key challenge, the effective control of the redissolution issue that will cause severe electrical shorting and inconsistent piezoelectric output, is overcome by searching for and using a solvent that offers adequate solubility but extremely slow dissolution for the piezoelectric polymer. The solvent screening methodology is established and demonstrated. The process parameters is systematically optimized to maximize the piezoelectric performance of the multilayer devices. The multilayer devices can output a high charge density of 376 µC m −2 , even higher than the record charge density of 250 µC m −2 achieved by traditional contact electrification‐based triboelectric nanogenerators operated in ambient air. The crucial factors for increasing device fabrication yield, namely resistance to short circuits and poling‐induced breakdown, are analyzed. The potential of multilayer devices in practical applications is demonstrated using a five‐layer device as a direct power source and energy harvester. More importantly, the process developed here is transferrable for cost‐effective high‐throughput roll‐to‐roll production. This work thus lays the foundation for the mass production of polymeric multilayer piezoelectric devices and paves the way for their future commercialization.
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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) | 0.002 | 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".