Piezoelectric microstructured fibers via drawing of multimaterial preforms
Bibliographic record
Abstract
We report an all-polymer flexible piezoelectric fiber that uses both judiciously chosen geometry and advanced materials in order to enhance fiber piezoelectric response. The microstructured/nanostructured fiber features a soft hollow polycarbonate core surrounded with a spiral multilayer cladding consisting of alternating layers of piezoelectric nanocomposites (polyvinylidene enhanced with BaTiO3, PZT or CNT) and conductive polymer (carbon filled polyethylene). The conductive polymer layers serve as two electrodes and they also form two spatially offset electric connectors on the fiber surface designed for the ease of connectorization. Kilometer-long piezoelectric fibers of submilimeter diameters are thermally drawn from a macroscopic preform. The fibers exhibit high output voltage of up to 6V under moderate bending, and they show excellent mechanical and electrical durability in a cyclic bend-release test. The micron/nano-size multilayer structure enhances in-fiber poling efficiency thanks to the small distance between the conducting electrodes sandwiching the piezoelectric composite layers. Additionally, spiral structure greatly increases the active area of the piezoelectric composite, thus promoting higher voltage generation and resulting in 10-100 higher power generation efficiency over the existing piezoelectric cables. Finally, we weave the fabricated piezoelectric fibers into technical textiles and demonstrate their potential applications in power generation when used as a sound detector and a wearable textiles
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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) | 0.001 | 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".