Enhancing the Properties of Chitosan–Pectin Hydrogels With Cellulose Nanowhiskers for Potential Applications in Wound Dressings
Bibliographic record
Abstract
Abstract Chitosan and pectin are employed here in the formation of biodegradable hydrogels. The addition of cellulose nanowhiskers (CNW) into the hydrogels is accomplished through two methods: by swelling, after the polymeric network formation (Method A), and inclusion in polymer solutions before gelation, with varying CNW content (Method B). Comprehensive physico–chemical, thermal, microscopic, gel fraction, and water vapor transmission analyses are conducted, complemented by antibacterial and viability assessments. CNWs are successfully synthesized and incorporated within the hydrogel by both methods. Moreover, the degradation temperature (Tmax) of hydrogels is higher with CNW (0% CNW: 205.5 °C, 10% CNW: 217.0 °C). CNW aggregates are observed in hydrogels with 20% and 30% content but no aggregation is observed with 4% and 10%. The antimicrobial properties are not affected by the addition of CNW, and the cytotoxicity tests reveal better results with 10% CNW compared with 20%. The study signifies the versatility of CNWs as effective reinforcements, influencing structural, thermal, and biological aspects of the hydrogels. The proposed methodology, emphasizing CNW incorporation, lays the groundwork for innovative biomaterials with promising medical implications. This investigation encourages further exploration and validation of these composite hydrogels in vivo for practical medical use in wound dressings.
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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) | 0.000 | 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".