Cellulose Nanocomposite Hydrogels: From Formulation to Material Properties
Bibliographic record
Abstract
Poly(vinyl alcohol) (PVA) hydrogels produced using the freeze-thaw method have attracted attention for a long time since their first preparation in 1975. Due to the importance of polymer intrinsic features and the advantages associated with them, they are very suitable for biomedical applications such as tissue engineering and drug delivery systems. On the other hand, there is an increasing interest in the use of biobased additives such as cellulose nanocrystals, CNC. This study focused on composite hydrogels which were produced by using different concentrations of PVA (5 and 10%) and CNC (1 and 10 wt.%), also, pure PVA hydrogels were used as references. The main goal was to determine the impact of both components on mechanical, thermal, and water absorption properties of composite hydrogels as well as on morphology and initial water content. It was found that PVA had a dominating effect on all hydrogels. The effect of the CNC addition was both concentration-dependent and case-dependent. As a general trend, addition of CNC decreased the water content of the prepared hydrogels, decreased the crystallinity of the PVA, and increased the hydrogels compression modulus and strength to some extent. The performance of composite hydrogels in a cyclic compression test was studied; the hydrogel with low PVA (5) and high CNC (10) content showed totally reversible behavior after 10 cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".