Advances in Electrospun Fiber‐Based Flexible Nanogenerators for Wearable Applications
Bibliographic record
Abstract
Abstract In today's digital age, the need and interest in personal and portable electronics shows a dramatic growth trend in daily life parallel to the developments in sensors technologies and the internet. Wearable electronics that can be attached to clothing, accessories, and the human body are one of the most promising subfields. The energy requirement for the devices considering the reduction in device sizes and the necessity of being flexible and light, the existing batteries are insufficient and nanogenerators have been recognized a suitable energy source in the last decade. The mechanical energy created by the daily activities of the human body is an accessible and natural energy source for nanogenerators. Fiber‐structured functional materials contribute to the increase in energy efficiency due to their effective surface to volume ratio while providing the necessary compatibility and comfort for the movements in daily life with its flexibility and lightness. Among the potential solutions, electrospinning stands out as a promising technique that can meet these requirements, allowing for simple, versatile, and continuous fabrication. Herein, wearable electronics and their future potential, electrospinning, and its place in energy applications are overviewed. Moreover, piezoelectric, triboelectric, and hybrid nanogenerators fabricated or associated with electrospun fibrous materials are presented.
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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 | 0.001 |
| 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".