An Anti‐Freezing, Ambient‐Stable and Highly Stretchable Ionic Skin with Strong Surface Adhesion for Wearable Sensing and Soft Robotics
Bibliographic record
Abstract
Abstract Natural living systems such as wood frogs develop tissues composed of active hydrogels with cryoprotectants to survive in cold environments. Recently, hydrogels have been intensively studied to develop stretchable electronics for wearables and soft robots. However, regular hydrogels are inevitably frozen at the subzero temperature and easily dehydrated, and have weak surface adhesion. Herein, a novel hydrogel‐based ionic skin (iSkin) capable of strain sensing is demonstrated with high toughness, high stretchability, excellent ambient stability, superior anti‐freezing capability, and strong surface adhesion. The iSkin consists of a piece of ionically and covalently cross‐linked tough hydrogel with a thin bioadhesive layer. With the addition of biocompatible cryoprotectant and electrolyte, the iSkin shows good conductivity in wide ranges of relative humidity (15–90%) and temperature (−95–25 °C). In addition, the iSkin can adhere firmly to diverse material surfaces under different conditions, including cloth fabric, skin, and elastomers, in both dry and wet conditions, at subzero temperature, and/or with dynamic movement. The iSkin is demonstrated for applications including strain sensing on both human body and winter coat, human–machine interaction, motion/deformation sensing on a soft gripper and a soft robot at extremely cold conditions. This work provides a new paradigm for developing high‐performance artificial skins for wearable sensing and soft robotics.
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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".