Shear rheology of cellulose nanocrystal (CNC) aqueous suspensions
Bibliographic record
Abstract
Scientific and commercial interests in renewable nanomaterials have been receiving increasing attention over the years. Cellulose nanocrystal (CNC) derived from entirely renewable resources promises wide applicability owing to its high strength, chirality, self-assembly and electromagnetic properties. In this thesis the rheology of CNC aqueous suspensions was studied and the rheological behaviour was correlated with their microstructure. It has been found that the CNC aqueous suspensions experience two microstructural transitions by increasing CNC concentration: a transition from isotropic to chiral nematic liquid crystal occurs above a first critical concentration, and by further increasing concentration, the suspensions go through another transition from chiral nematic liquid crystal to gel above a second critical concentration. The viscosity profile of anisotropic suspensions shows a three-region behaviour characteristic of liquid crystals, and after gel formation a single shear thinning is observed over the whole investigated range. CNC suspensions possessing a higher degree of sulfation have more tendency to form anisotropic chiral nematic structures, and form gels at relatively higher concentration compared to those with a lower degree of sulfation. Sonication up to 1000 J/g CNC, breaks all the aggregates in the system and significantly decreases the viscosity. Although the sonication-induced decrease in viscosity levels off through further sonication (>1000 J/g CNC), it still affects the viscosity of anisotropic suspensions at low shear rates by increasing the size of chiral nematic domains. The effects of adding NaCl to CNC aqueous suspensions have been evaluated in different concentration regimes: isotropic, anisotropic chiral nematic, and gel. For isotropic samples and gels, the viscosity decreases by the addition of NaCl up to 5 mM. For anisotropic samples, on the other hand, the viscosity at low shear rates increases by addition of NaCl up to 5 mM due to decrease in chiral nematic domain size. However, at high shear rates, where all the domains are broken, the viscosity decreases when adding NaCl. Further addition of NaCl (>5 mM) results in extensive aggregation in suspension, and thus the viscosity increases.
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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 | 0.001 |
| 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".