Breathable, Degradable Piezoresistive Skin Sensor Based on a Sandwich Structure for High‐Performance Pressure Detection
Bibliographic record
Abstract
Abstract Wearable intelligent sensor materials have broad application prospects for human health detection and robot kinematics. 3D structure sensors have the advantages of high sensitivity, a wide detection range, and high strength, as is widely reported in existing research. Here, a sandwich structure involving an encapsulation layer, a 3D conductive network, and an encapsulation layer is prepared. Polyaniline acts as an active conductive filler for the 3D conductive network, while silk fibroin and poly (lactic‐co‐glycolic acid) are used to form a network for carrying conductive materials. Additionally, K‐carrageenan is added to encapsulate the 3D conductive network and prepare a high‐performance green skin sensor. The sensor exhibits high sensitivity (2.54 kPa−1) and a wide linear detection range (165.3 kPa). In addition, the pressure sensor possesses excellent durability (>2000 cycles) and a fast response time of 160 ms. Moreover, the sensor is compatible and biodegradable and encapsulated by a nontoxic water‐soluble polymer. On this basis, skin sensors for health monitoring systems and intelligent interactive systems are reported, thus enabling future applications in medical detection and human–machine interaction.
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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".