Injectable Polysaccharide Hydrogels Reinforced with Cellulose Nanocrystals: Morphology, Rheology, Degradation, and Cytotoxicity
Bibliographic record
Abstract
Injectable hydrogels based on carboxymethyl cellulose and dextran, reinforced with rigid rod-like cellulose nanocrystals (CNCs) and aldehyde-functionalized CNCs (CHO-CNCs), were prepared and characterized. The mechanical properties, internal morphology, and swelling of injectable hydrogels with unmodified and modified CNCs at various loadings were examined. In all cases, gelation occurred within seconds as the hydrogel components were extruded from a double-barrel syringe, and the CNCs were evenly distributed throughout the composite, as observed by scanning and transmission electron microscopy. When immersed in purified water or 10 mM PBS, all CNC-reinforced hydrogels maintained their original shape for more than 60 days. The maximum storage modulus was observed in hydrogels with 0.250 wt % of unmodified CNCs and 0.375 wt % of CHO-CNCs. CHO-CNCs acted as both a filler and a chemical cross-linker, making the CHO-CNC-reinforced hydrogels more elastic, more dimensionally stable, and capable of facilitating higher nanoparticle loadings compared to hydrogels with unmodified CNCs, without sacrificing mechanical strength. No significant cytotoxicity to NIH 3T3 fibroblast cells was observed for the hydrogels or their individual components. These properties make CNC-reinforced injectable hydrogels of potential interest for various biomedical applications such as drug delivery vehicles or tissue engineering matrices.
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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".