Polymer Induced Gelation of Aqueous Suspensions of Cellulose Nanocrystals
Bibliographic record
Abstract
We investigated the gelation of cellulose nanocrystals (CNCs) in polyelectrolyte and neutral polymer solutions. Cellulose nanocrystals (CNCs) with half-ester sulfate groups produced by acid hydrolysis of wood pulp were used in this study. The microstructure of CNCs/polymer suspensions was investigated in semidilute concentration regimes by selecting carboxymethyl cellulose (CMC700) as an anionic polymer and poly(ethylene oxide) (PEO600) as a neutral polymer solution. Together with quartz crystal microbalance with dissipation monitoring (QCM-D), rheology, scanning electron microscopy (SEM), and cryo-transmission electron microscopy (cryo-TEM), we characterized CNCs-polymer interactions, the suspension microstructure, and the macroscopic gel flow. Significant viscosity increases at low shear rates coupled with high shear-thinning behaviors were observed in CNC colloid-CMC700 polymer mixtures, but not those CNCs in PEO600 solutions. The apparent differences between CNCs-CMC700 and CNCs-PEO600 mixtures were due to their chain confirmations. On the basis of the evaluations from STEM, cryo-TEM, and polarized optical microscopy, we proposed that the excess CMC700 molecules in solutions result in the depletion of CNCs and the formation of anisotropic domains.
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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".