Toughening hydrogels through a multiscale hydrogen bonding network enabled by saccharides for a bio-machine interface
Bibliographic record
Abstract
Hydrogels have considerably emerged in a variety of fields, but their weak mechanical properties severely restrict the wide range of implementation. Herein, we propose a multiscale hydrogen bonding toughening strategy using saccharide-based materials to optimize the hydrogel network. The monosaccharide (glucose) at the molecular scale and polysaccharide (cellulose nanofibrils) at the nano/micro scale can effectively form hydrogen bonds across varied scales within the hydrogel network, leading to significantly enhanced mechanical properties. Besides, the toughened hydrogels present excellent environmental resilience and bad solvent resistance, allowing them to retain their performance in various severe environments. Notably, after being exchanged with a bad solvent such as ethanol, the alcogel exhibits strain-depended vivid interference color, allowing it to function as a mechano-optical sensor. Finally, this strategy has been shown to be adaptable across multiple material systems, and the resulting hydrogels have potential as a bioelectronic interface for long-term stable recording of physiological signals, highlighting the potential of sustainable biomaterials in designing high-quality hydrogels for advanced applications.
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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".