Protein Gel Phase Transition: Toward Superiorly Transparent and Hysteresis‐Free Wearable Electronics
Bibliographic record
Abstract
Abstract The next generation of wearable electronics for health monitoring, Internet‐of‐Things system, “interface‐on‐invisible,” and green energy harvesting require electrically conductive material that is superiorly transparent, negligibly hysteretic, industrially feasible, and highly stretchable. The practical potential of ionic hydrogel is challenged with obvious hysteresis and a limited sensing range due to relative delamination and viscoelastic performance. Herein, a novel liquid conductor, termed as egg white liquid, is developed from self‐liquidation of egg white hydrogel, and the liquid not only inherits the designed architecture from a hydrogel predecessor but also achieves comparable conductivity (20.4 S m−1) to the ionic hydrogel and ultrahigh transparency (up to 99.8%) . Moreover, the 3D‐printed liquid–elastomer hybrid exhibits excellent conformability, remarkable sensitivity with negligible hysteresis (0.77%), and the capability of monitoring human motions and dynamic moduli is further demonstrated. The liquid nature inspires a gesture‐controlled touchless user interface for front‐end electronic systems. Furthermore, mechanical energy harvesting and pressure sensing are evidenced by exploiting this liquid conductor into a triboelectric nanogenerator. Notably, the as‐prepared liquid via subsequent phase transition possessing superior transparency, ultralow hysteresis, economic benefit, and unique liquid phase may potentially fuel the development of a new class of wearable electronics, human–machine interface, and clean energy.
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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".