Self-Assembly of Nanocellulose Hydrogels Mimicking Bacterial Cellulose for Wound Dressing Applications
Bibliographic record
Abstract
The self-assembly of nanocellulose in the form of cellulose nanofibers (CNFs) can be accomplished via hydrogen-bonding assistance into completely bio-based hydrogels. This study aimed to use the intrinsic properties of CNFs, such as their ability to form strong networks and high absorption capacity and exploit them in the sustainable development of effective wound dressing materials. First, TEMPO-oxidized CNFs were separated directly from wood (W-CNFs) and compared with CNFs separated from wood pulp (P-CNFs). Second, two approaches were evaluated for hydrogel self-assembly from W-CNFs, where water was removed from the suspensions via evaporation through suspension casting (SC) or vacuum-assisted filtration (VF). Third, the W-CNF-VF hydrogel was compared to commercial bacterial cellulose (BC). The study demonstrates that the self-assembly via VF of nanocellulose hydrogels from wood was the most promising material as wound dressing and displayed comparable properties to that of BC and strength to that of soft tissue.
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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".