Employing Cellulose Nanofiber-Based Hydrogels for Burn Dressing
Bibliographic record
Abstract
The aim of this research was to fabricate a burn dressing in the form of hydrogel films constructed with cellulose nanofibers (CNF) that has pain-relieving properties, in addition to wound healing. In this study, the hydrogels were prepared in the form of film. For this, CNF at weight ratios of 1, 2, and 3 wt.%, 1 wt.% of hydroxyethyl cellulose (HEC), and citric acid (CA) crosslinker with 10 and 20 wt.% were used. FE-SEM analysis showed that the structure of the CNF was preserved after hydrogel preparation. Cationization of CNF by C6H14NOCl was confirmed by FTIR spectroscopy. The drug release analysis results showed a linear relationship between the amount of absorption and the concentration of the drug. The MTT test (assay protocol for cell viability and proliferation) showed the high effectiveness of cationization of CNF and confirmed the non-toxicity of the resulting hydrogels.
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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".