Mass‐Producible 3D Hair Structure‐Editable Silk‐Based Electronic Skin for Multiscenario Signal Monitoring and Emergency Alarming System
Bibliographic record
Abstract
Abstract Structurally tunable electronic skin (e‐skin) is beneficial for advancing wearable electronics, prosthetics, and human‐machine interaction (HMI). However, the regulation of e‐skin by traditional nanostructure technology is complex and expensive, moreover, the nanostructure's poor deformability leads to small detection range and low sensitivity. Herein, inspired by the structure of skin‐hair and insect burr, a polypyrrole‐silk/glycerol plasticized silk fibroin (P‐silk/RG) e‐skin fabricated by a simple 3D biomimetic structural strategy is reported. Benefitting from the editability (length, position) of this structure, P‐silk/RG has a signal selectivity, long‐cilia P‐silk/RG demonstrates high sensitivity (respond to weak signal‐airflow), while the short‐cilia P‐silk/RG exhibits wide pressure detection range (0.5–200 g) and high cycle stability (8000 compressions). Therefore, different forms of P‐silk/RG are used in different scenarios (long‐cilia for monitoring breathing and coughing for motion detection and disease diagnosis, short‐cilia for pressure‐sensitive Morse code). Besides, P‐silk/RG exhibits good waterproof, editable conductive points and easy device integration, providing the basis for underwater information transmission, multibit coded command output, and early warning for emergency sports accidents and sedentary. Surprisingly, combining this structure with textile weaving can be mass‐produced. Obviously, this 3D biomimetic structure strategy endows e‐skin with editability and improved scene adaptability to provide a favorable way for mass production.
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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".