Mechanically Robust and Transparent Organohydrogel‐Based E‐Skin Nanoengineered from Natural Skin
Bibliographic record
Abstract
Abstract Electronic skins (e‐skins), which are mechanically compliant with human skin, are regarded as ideal electronic devices for noninvasive human–machine interaction and wearable devices. In order to fully mimic human skin, e‐skins should possess reliable mechanical properties and be able to resist external environmental factors like heat, cold, desiccation, and bacteria, while perceiving multiple external stimuli, such as temperature, humidity, and strain. Here, a transparent, mechanically robust, environmentally stable, versatile natural skin‐derived organohydrogel (NSD‐Gel) is nanoengineered through the integration of betaine, silver nanoparticles, and sodium chloride in a glycerol/water binary solvent. The transparent NSD‐Gel e‐skin exhibits outstanding tensile strength (7.33 MPa), puncture resistance, moisture retention, self‐regeneration, and antibacterial properties. Additionally, the NSD‐Gel e‐skin possesses enhanced cold/heat resistance and stimuli‐responsive characteristics that effectively sense environmental temperature and humidity changes, as well as physiological human body motion signals. In vitro and in vivo experiments show that the NSD‐Gel e‐skin confers desired biocompatibility and tissue protective properties even in extremely harsh environments (−196 °C to 100 °C). The NSD‐Gel e‐skin has great potential for applications in multidimensional wearable electronic devices, human‐machine interfaces, and artificial intelligence, generating a versatile platform for the development of high‐performance e‐skins with on‐demand properties.
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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".