Multifunctional Textile Electronic with Sensing, Energy Storing, and Electrothermal Heating Capabilities
Bibliographic record
Abstract
The development of wearable devices has stimulated significant engineering and technologies of textile electronics (TEs). Improving sensing, energy-storing, and electro-heating capabilities of TEs is still challenging but crucial for their practical applications. Herein, a drip-coating method that constructs a dense β-FeOOH scaffold on a nylon strip for enhancing polypyrrole loading is proposed, which facilitates the fabrication of highly conductive and hydrophobic PFCNS (polypyrrole/β-FeOOH/nylon strip). The space provided by the β-FeOOH scaffold increases the mass of polypyrrole on fibers from 1.1 (polypyrrole/nylon strip) to 3.0 mg cm –2 (polypyrrole/β-FeOOH/nylon strip), which decreases the resistance from 104.96 to 34.29 Ω cm –1 . The PFCNS exhibits a linear elastic modulus of 0.758 MPa within 150% strain, performs a unique resistance variation mechanism, and enables great sensing capability with rapid response time (140 ms), long durability (10,000 stretching-recovering), and effective movement monitoring (e.g., breathing, back bending, jumping). The sensing signals for knee bending have been analyzed in detail by combining with both stretching and pressing response mechanisms. The PFCNS electrode attains a diffusion-controlled capacitance of 574 mF cm –2 and discharging-capacitance of 916 mF cm –2 . Furthermore, an interdigitally parallel connection is proposed, which assists the PFCNS heater in achieving high temperature (84 °C) at a low voltage (4 V). This work provides a simple route for nylon-based TEs and promises satisfactory application in wearable sensors, power sources, and heaters.
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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".