Dispersions of Nanocrystalline Cellulose in Aqueous Polymer Solutions: Structure Formation of Colloidal Rods
Bibliographic record
Abstract
The steady-state shear and linear viscoelastic deformations of semidilute suspensions of rod-shaped nanocrystalline cellulose (NCC) particles in 1.0% hydroxyethyl cellulose and carboxymethyl cellulose solutions were investigated. Addition of NCC at the onset of semidilute suspension concentration significantly altered the rheological and linear viscoelastic properties of semidilute polymer solutions. The low-shear viscosity values of polymers solutions were increased 20-490 times (depending on polymer molecular weight and functional groups) by the presence of NCC. NCC suspensions in polymer solutions exhibited yield stresses up to 7.12 Pa. Viscoelasticity measurements also showed that NCC suspended polymer solutions had higher linear elastic moduli than the loss moduli. All of those results revealed the gel formation of NCC particles and presence of internal structures. The formation of a weak gel structure was due to the nonadsorbing macromolecules which caused the depletion-induced interaction among NCC particles. A simple interaction energy model was used to show successfully the flocculation of NCC particles in the presence of nonadsorbing polymers. The model is based on the incorporation of the depletion interaction term between two parallel plates into the DLVO theory for cubic prismatic rod shaped NCC particles.
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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".