Cellulose nanocrystals for gelation and percolation-induced reinforcement of a photocurable poly(vinyl alcohol) derivative
Bibliographic record
Abstract
Nanomaterials are regularly added to crosslinkable polymers to enhance mechanical properties; however, important effects related to gelation behavior and crosslinking kinetics are often overlooked. In this study, we combine cellulose nanocrystals (CNCs) with a photoactive poly(vinyl alcohol) derivative, PVA-SbQ, to form photocrosslinked nanocomposite hydrogels. We investigate the rheology of PVA-SbQ with and without CNCs to decipher the role of each component in final property development and identify a critical CNC concentration (1.5 wt%) above which several changes in rheological behavior are observed. Neat PVA-SbQ solutions exhibit Newtonian flow behavior across all concentrations, while CNC dispersions are shear-thinning <6 wt% and gel at high concentrations. Combining semi-dilute entangled PVA-SbQ (6 wt%) with >1.5 wt% CNCs forms a percolated microstructure. In situ photocrosslinking experiments reveal how CNCs affect both the gelation kinetics and storage modulus (G') of the resulting hydrogels. The modulus crossover time increases after addition of up to 1.5 wt% CNCs, while no modulus crossover is observed >1.5 wt% CNCs. A sharp increase in G' is observed >1.5 wt% CNCs for fully-crosslinked networks due to favorable PVA-SbQ/CNC interactions. A percolation model is fitted to the G' data to confirm that mechanical percolation is maintained after photocrosslinking. A ∼120% increase in G' for 2.5 wt% CNCs (relative to neat PVA-SbQ) confirms that CNCs provide a reinforcing effect through the percolated microstructure formed from PVA-SbQ/CNC interactions. The results are testament to the ability of CNCs to significantly alter the storage moduli of crosslinked polymer gels at low loading fractions through percolation-induced reinforcement.
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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".