A Universal Interfacial Strategy Enabling Ultra‐Robust Gel Hybrids for Extreme Epidermal Bio‐Monitoring
Bibliographic record
Abstract
Abstract A seamless and tough interface to integrate incompatible/immiscible soft materials is highly desired for flexible/wearable electronics and many soft devices with multi‐layer structures. Here, a surfactant‐mediated interfacial chemistry is introduced to achieve seamless and tough interfaces in soft multi‐layer structures, with an ultra‐high interfacial toughness up to ≈1300 J m −2 for the architectural gel hybrid (AGH). The reversible noncovalent interfacial interactions efficiently dissipate energy at the interface, thereby providing excellent durability. The interfacial toughness only decreases by ≈6.9% after 10 000 tensile cycles. This strategy can be universally applied to hybrid systems with various interfaces between an interior hydrogel (PAA, PVA, PAAm, and gelatin) and an exterior hydrophobic soft matter (ionogel, lipogel and elastomer). The AGH‐based mechano‐sensor presents high robustness and stability in a wide range of conditions, including open air, underwater, and various solvents and temperatures. Epidermal bio‐monitoring, tactile trajectory, and facial expression recognition are demonstrated using the AGH sensors in various environments. A rich set of electrophysiological signals of high quality are acquired.
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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".