Fiber‐Shaped Electronic Devices
Bibliographic record
Abstract
Abstract Textile electronics embedded in clothing represent an exciting new frontier for modern healthcare and communication systems. Fundamental to the development of these textile electronics is the development of the fibers forming the cloths into electronic devices. An electronic fiber must undergo diverse scrutiny for its selection for a multifunctional textile, viz., from the material selection to the device architecture, from the wearability to mechanical stresses, and from the environmental compatibility to the end‐use management. Herein, the performance requirements of fiber‐shaped electronics are reviewed considering the characteristics of single electronic fibers and their assemblies in smart clothing. Broadly, this article includes i) processing strategies of electronic fibers with required properties from precursor to material, ii) the state‐of‐art of current fiber‐shaped electronics emphasizing light‐emitting devices, solar cells, sensors, nanogenerators, supercapacitors storage, and chromatic devices, iii) mechanisms involved in the operation of the above devices, iv) limitations of the current materials and device manufacturing techniques to achieve the target performance, and v) the knowledge gap that must be minimized prior to their deployment. Lessons learned from this review with regard to the challenges and prospects for developing fiber‐shaped electronic components are presented as directions for future research on wearable electronics.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".