Rheological behavior of cellulose nanocrystal suspensions in polyethylene glycol
Bibliographic record
Abstract
The rheological behavior of cellulose nanocrystals (CNCs) in polar media based on polyethylene glycol (PEG) was investigated from aqueous suspensions to nanocomposites. The aim of this work is to improve our knowledge on the CNC behavior in polymer media and develop rheological indices to characterize the dispersion of nanoparticles in polymer matrices. CNCs were obtained from sulfuric acid hydrolysis of wood pulp and supplied after a spray- or freeze-drying process. Ultrasonication was used to break agglomerates and disperse CNCs in aqueous suspensions before mixing with an aqueous PEG solution at room temperature. The samples were subsequently dried and compression molded. From capillary and oscillatory shear rheology, no adsorption of PEG chains on CNCs could be detected, as many had previously hypothesized. The increase of PEG concentration in aqueous suspension favored the gelation by depletion effect and suggested CNC orientation. Viscoelastic properties and transmission electronic images of PEG/CNC nanocomposites highlighted the formation of a percolated network of CNCs for low concentrations ≥ 0.15 vol. %. From the model of Shih et al., a fractal dimension of 2 was obtained for these percolated nanocomposites, suggesting a 2D network of CNCs in the PEG matrix.
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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 | 0.001 |
| 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".