Ultrastretchable, Adhesive, and Antibacterial Hydrogel with Robust Spinnability for Manufacturing Strong Hydrogel Micro/Nanofibers
Bibliographic record
Abstract
Abstract The ultrastretchable (over 12 400%) hydrogel with long‐lasting adhesion, strong antibacterial activity, and robust spinnability is developed based on the oxidative decarboxylation and quinone‐catechol reversible redox reaction induced by Ag‐lignin nanoparticles in a precursor solution containing citric acid (CA), acrylic acid (AA), and poly (acrylamide‐co‐acrylic acid) (P(AAm‐co‐AA)). With massive reversible interactions including hydrogen bonds and electrostatic forces, such hydrogel exhibits promising injectability and is facilely spun via manual drawing, draw‐spinning, and electrospinning for manufacturing strong hydrogel micro/nanofibers. The resulting fibers exhibit excellent mechanical properties, including tensile stress of 422.0 MPa, strain of 86.5%, Young's modulus of 8.7 GPa, and toughness of 281.6 MJ m−3. The hydrogel microfibers obtained from a house‐built spinner are scaled‐up fabricated while retaining promising mechanical properties, as evidenced by lifting a load (317.2 g) using the spun fibers of ≈33 000 times lighter weight (9.5 mg), indicating their great potentials in the applications such as net and safety cord which require robust mechanical properties. Moreover, assisted by a commercial electrospinning machine, nanosized hydrogel fibers are facilely spun on personal protective equipment such as a mask to offer an antiseptic coating with near 100% killing efficiency against airborne bacteria aerosols, demonstrating the capability of spun hydrogel fibers on disinfection‐related 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".