High-intensity ultrasound-assisted formation of cellulose nanofiber scaffold with low and high lignin content and their cytocompatibility with gingival fibroblast cells
Bibliographic record
Abstract
Cellulose nanofiber (CNF) hydrogels with low lignin (8%) (LL-CNF) and high lignin (18%) (HL-CNF) content were produced at nominal powers of 240, 720 and 1200 W using high-intensity ultrasound technology (HIUS). Freeze-dried CNF hydrogels were evaluated as scaffolds for gingival fibroblast cells proliferation aiming biomedical applications. HIUS processing improved the dispersibility of the CNF and increased the water retention value by more than 5 times. The LL-CNF had a maximum fibrillation yield of 46 wt.%, whereas the HL-CNF had a maximum fibrillation yield of 40 wt.% at nominal power of ≥720 W. Regardless of the lignin content, the CNF hydrogels exhibited a typical elastic gel-like behavior with the highest elasticity of 263 Pa. After freeze-drying, the CNF aerogels had porosity ≥ 96.8%, and swelling capacity up to 42.1 g PBS/g aerogel. Moreover, the cell proliferation assay showed no differences in proliferation among the LL-CNF and HL-CNF scaffolds up to 11 days. Therefore, CNF scaffolds prepared with lignin content up to 18% present promising application in the biomedical field.
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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".