A bioinspired hydrogen bond crosslink strategy toward toughening ultrastrong and multifunctional nanocomposite hydrogels
Bibliographic record
Abstract
Developing physical hydrogels with advanced mechanical performance and multi-functionalities as alterative materials for load-bearing soft tissues remains a great challenge. Biological protein-based materials generally exhibit superior strength and toughness owing to their hierarchical structures via hydrogen-bonding assembly. Inspired by natural biological protein materials, tannic acid (TA) is exploited as a molecular coupling bridge between cellulose nanocrystals (CNCs) and poly(vinyl alcohol) (PVA) chains for the fabrication of a bio-based advanced physical hydrogel via strong multiple H-bonds. When exposed to mechanical stress, the sacrificial H-bonds can dissipate energy effectively on the molecular scale via dynamic rupture and reformation, endowing these biomimetic hydrogels with remarkable toughness, ultrahigh strength, large elongation, and good self-recoverability, which are much superior to those of most hydrogen bond-based hydrogels. Moreover, the characteristics of TA endow these biomimetic hydrogels with versatile adhesiveness and good antibacterial properties. This work presents an innovative biomimetic strategy for robust biocompatible hydrogels with superior mechanical strength and functionalities, which holds great promise for applications in tissue engineering and biomedical fields.
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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".