A Breathable, Passive‐Cooling, Non‐Inflammatory, and Biodegradable Aerogel Electronic Skin for Wearable Physical‐Electrophysiological‐Chemical Analysis
Bibliographic record
Abstract
Real-time monitoring of human health can be significantly improved by designing novel electronic skin (E-skin) platforms that mimic the characteristics and sensitivity of human skin. A high-quality E-skin platform that can simultaneously monitor multiple physiological and metabolic biomarkers without introducing skin discomfort or irritation is an unmet medical need. Conventional E-skins are either monofunctional or made from elastomeric films that do not include key synergistic features of natural skin, such as multi-sensing, breathability, and thermal management capabilities in a single patch. Herein, a biocompatible and biodegradable E-skin patch based on flexible gelatin methacryloyl aerogel (FGA) for non-invasive and continuous monitoring of multiple biomarkers of interest is engineered and demonstrated. Taking advantage of cryogenic temperature treatment and slow polymerization, FGA is fabricated with a highly interconnected porous structure that displays good flexibility, passive-cooling capabilities, and ultra-lightweight properties that make it comfortable to wear for long periods of time. It also provides numerous permeable capillary channels for thermal-moisture transfer, ensuring its excellent breathability. Therefore, the engineered FGA-based E-skin can simultaneously monitor body temperature, hydration, and biopotentials via electrophysiological sensors and detect glucose, lactate, and alcohol levels via electrochemical sensors. This work offers a previously unexplored materials strategy for next-generation E-skin platforms with superior practicality.
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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".