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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".