Interfacial Hydrogen Bond-Reinforced Adhesion and Cohesion Enabling an Ultrastretchable and Wet Adhesive Hydrogel Strain Sensor
Bibliographic record
Abstract
Historically, research on silicotungstic-acid–based hydrogels has primarily focused on their adhesive properties, often at the expense of mechanical strength (cohesion). In this study, we present a novel approach to fabricate a polysaccharide hydrogel that harmoniously balances both adhesion and cohesion via interfacial hydrogen bonds. This hydrogel, composed of carboxymethyl cellulose (CMC), polyacrylamide (PAM), silicotungstic acid (SiW), and lithium chloride (LiCl), showcases a unique combination of properties: strain-responsive ionic conductivity, superior transparency, remarkable stretchability, and robust adhesion. Contrary to conventional PAM hydrogels, our PAM-SiW networked hydrogel addresses the common challenge of achieving good adhesion without compromising on cohesion. Specifically, our hydrogel demonstrates a maximum toughness of 20.3 MJ/m 3 and a strain of 4079%, an accomplishment rarely observed in other adhesive hydrogel. Furthermore, the hydrogel’s adhesion is both reversible and versatile, adhering effectively to a variety of wet and dry substrates. This makes it a promising candidate for advanced healthcare applications, particularly as a mechanically reinforced underwater adhesive with unparalleled stability. We also provide insights into the role of LiCl in the hydrogel matrix, emphasizing its influence on electrostatic interactions without affecting the hydrogen bonds. This study serves as a testament to the potential of harmonizing adhesive and cohesive properties in hydrogels, paving the way for future innovations in the field.
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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 | 0.001 |
| 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".