Effect of Cellulose Nanocrystals (CNC) Particle Morphology on Dispersion and Rheological and Mechanical Properties of Polypropylene/CNC Nanocomposites
Bibliographic record
Abstract
Polypropylene (PP) nanocomposites containing spray-dried cellulose nanocrystals (CNC), freeze-dried CNC, and spray-freeze-dried CNC (CNCSFD) were prepared via melt mixing in an internal batch mixer. Polarized light, scanning electron, and atomic force microscopy showed significantly better dispersion of CNCSFD in PP/CNC nanocomposites compared with the spray-dried and freeze-dried CNCs. Rheological measurements, including linear and nonlinear viscoelastic tests, were performed on PP/CNC samples. The microscopy results were supported by small-amplitude oscillatory shear tests, which showed substantial rises in the magnitudes of key rheological parameters of PP samples containing CNCSFD. Steady-shear results revealed a strong shear thinning behavior of PP samples containing CNCSFD. Moreover, PP melts containing CNCSFD exhibited a yield stress. The magnitude of the yield stress and the degree of shear thinning behavior increased with CNCSFD concentration. It was found that CNCSFD agglomerates with a weblike structure were more effective in modifying the rheological properties. This effect was attributed to better dispersion of the agglomerates with the weblike structure. Dynamic mechanical analysis showed considerable improvement in the modulus of samples containing CNCSFD agglomerates. The percolation mechanical model with modified volume percolation threshold and filler network strength values and the Halpin-Kardos model were used to fit the experimental results.
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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 | 0.000 |
| 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".