Ultra‐Stretchable and Environmentally Resilient Hydrogels Via Sugaring‐Out Strategy for Soft Robotics Sensing
Bibliographic record
Abstract
Abstract The adoption of hydrogels in most applications is hampered by their high free water content, which limits their mechanical performance and environmental resilience. Herein, this issue is simultaneously addressed by modulating the state of water and the intermolecular interactions in polyacrylamide (PAM) hydrogels. Specifically, PAM hydrogels are toughened by sugaring‐out using a monosaccharide (glucose, G). Glucose is found to facilitate PAM hydrogen bonding and interchain interactions. Meanwhile, the high hygroscopicity of glucose converts some of the free water to bound state, endowing the hydrogels with remarkable resilience to extreme environmental conditions. The PAM‐G hydrogels are demonstrated as multimodal sensors for soft robotics. Moreover, PAM‐G alcogels produced by solvent exchanging with ethanol are shown as effective opto‐mechanical sensors. Notably, all these properties are obtained by the inclusion of glucose, a green additive showing no negative health and environmental effect.
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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".