Stratum Corneum‐Inspired Zwitterionic Hydrogels with Intrinsic Water Retention and Anti‐Freezing Properties for Intelligent Flexible Sensors
Bibliographic record
Abstract
Abstract Hydrogels, which mimic the properties of natural tissues, are essential for flexible electronics in human‐machine interfaces (HMIs). However, traditional hydrogels suffer from dehydration, compromising stability and functionality. To address this issue, a stratum corneum‐inspired, water‐retaining hydrogel is developed using hygroscopic polymers and bound water. Three types of hydrophilic monomers (non‐ionic, mono‐ionic, and zwitterionic) are explored, with polyzwitterions, particularly N,N‐dimethyl (acrylamidopropyl) ammonium propane sulfonate (DMAAPS), forming a quasi‐hydrogel that retains the softness and flexibility of conventional hydrogels. Water acts as a plasticizer, enhancing polymer chain mobility and reducing stiffness. The DMAAPS hydrogel maintains 100% weight retention under specific humidity conditions and shows skin‐like softness across a wide humidity range. The Young's modulus increases from 54 to 118 kPa as relative humidity decreases from 80% to 40%. The absence of free water confers intrinsic anti‐freezing properties. A triple crosslinking mechanism and conductive polymers endow the hydrogel with stretchability (> 2000%), toughness, elasticity, self‐healing, and stable sensing capabilities. The hydrogel functions as an excellent flexible sensor for real‐time, sensitive detection of human motion and physiological signals. An intelligent handwriting recognition platform with high accuracy is also established using double‐channel signal collection and machine learning algorithms, offering insights for next‐generation durable, biomimetic, and smart HMIs.
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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) | 0.000 | 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 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".