Characterization of Cellulose Fiber Derived from Hemp and Polyvinyl Alcohol-Based Composite Hydrogel as a Scaffold Material
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) were successfully extracted and purified from hemp using an alkaline treatment and bleaching process and subsequently used in conjunction with polyvinyl alcohol to form a composite hydrogel. Cellulose nanocrystals (1–10% (w/v)) were integrated into polyvinyl alcohol, and sodium tetraborate (borax) was employed as a crosslinking agent. Due to the small number of cellulose nanocrystals, no significant peak change was observed in the FT-IR spectra compared to pristine polyvinyl alcohol. The porosity was created upon the removal of the water molecules, and the material was thermally stable up to 200 °C. With the presence of cellulose nanocrystals, the melting temperature was slightly shifted to a higher temperature, while the glass transition temperature remained practically unchanged. The swelling behavior was examined for 180 min in deionized water and PBS solution (pH 7.4) at 37 °C. The degree of swelling of the composite with cellulose nanocrystals was found to be higher than that of pristine PVA hydrogel. The cell viability (%) of the prepared hydrogel with different proportions of cellulose nanocrystals was higher than that of pristine PVA hydrogel. Based on the results, the prepared composite hydrogels from cellulose nanocrystals extracted from hemp and polyvinyl alcohol were revealed to be an excellent candidate for scaffold material for medical usage.
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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".