Enhanced Mechanical Properties in Cellulose Nanocrystal–Poly(oligoethylene glycol methacrylate) Injectable Nanocomposite Hydrogels through Control of Physical and Chemical Cross-Linking
Bibliographic record
Abstract
While injectable hydrogels have several advantages in the context of biomedical use, their generally weak mechanical properties often limit their applications. Herein, we describe in situ-gelling nanocomposite hydrogels based on poly(oligoethylene glycol methacrylate) (POEGMA) and rigid rod-like cellulose nanocrystals (CNCs) that can overcome this challenge. By physically incorporating CNCs into hydrazone cross-linked POEGMA hydrogels, macroscopic properties including gelation rate, swelling kinetics, mechanical properties, and hydrogel stability can be readily tailored. Strong adsorption of aldehyde- and hydrazide-modified POEGMA precursor polymers onto the surface of CNCs promotes uniform dispersion of CNCs within the hydrogel, imparts physical cross-links throughout the network, and significantly improves mechanical strength overall, as demonstrated by quartz crystal microbalance gravimetry and rheometry. When POEGMA hydrogels containing mixtures of long and short ethylene oxide side chain precursor polymers were prepared, transmission electron microscopy reveals that phase segregation occurs with CNCs hypothesized to preferentially locate within the stronger adsorbing short side chain polymer domains. Incorporating as little as 5 wt % CNCs results in dramatic enhancements in mechanical properties (up to 35-fold increases in storage modulus) coupled with faster gelation rates, decreased swelling ratios, and increased stability versus hydrolysis. Furthermore, cell viability can be maintained within 3D culture using these hydrogels independent of the CNC content. These properties collectively make POEGMA-CNC nanocomposite hydrogels of potential interest for various biomedical applications including tissue engineering scaffolds for stiffer tissues or platforms for cell growth.
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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".