Strong and Rapidly Self‐Healing Hydrogels: Potential Hemostatic Materials
Bibliographic record
Abstract
Benzaldehyde-terminated telechelic four-armed polyethylene glycol (PEG-BA) is synthesized and cross-linked with carboxymethyl chitosan (CMC) to form dynamic hydrogels with strong mechanical performance. The gelation temperature and time, mechanical performance, and self-healing behaviors are systematically investigated. The hydrogels have good storage modulus up to 3162.06 ± 21.06 Pa, comparable to conventional bulk hydrogels. The separated alternate hydrogel lines connect together to become an integrated hydrogel film after 5 min at room temperature without any external intervention. This is due to the dynamic equilibrium between the Schiff base linkages and the aldehyde groups of PEG-BA and amine groups on CMC backbone. The hydrogel shows excellent cytocompatibility and the cell viability is as high as 90.7 ± 6.8% after 2 d 3D encapsulation in the hydrogel. In vivo tests indicate that the hydrogels can effectively stop bleeding when the hydrogel is directly injected into a rabbit liver incision. The total blood loss is reduced from 0.65 ± 0.10 g to 0.29 ± 0.11 g, and the hemostasis time is decreased from 167 ± 21 s to 120 ± 10 s, when compared to a gauze treatment with physical compression. These self-healing hydrogels have potential to be used as a novel hemostatic material.
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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".