Effect of the addition of cellulose filaments on the relaxation behavior of thermoplastics
Bibliographic record
Abstract
In this work, the effect of cellulose filaments (CFs) dispersion on the relaxation behavior of thermoplastics matrices was studied. The dispersion state of polystyrene/CF composites produced by two different processing methods, leading to two different dispersion qualities, was assessed using scanning electron microscopy (SEM), transmission electron microscopy, and small amplitude oscillatory shear (SAOS). Instead of the generally used plateau value of G′, the melt yield stress of the modified Carreau–Yasuda model was used to find an accurate value of the percolation threshold concentration. It was concluded that in this case, the process involving a solution mixing step led to a better dispersion than the process involving only melt mixing. Then, using the weighted relaxation spectra calculated using the Honerkamp and Weese method on SAOS results, it was shown that the better the dispersion, the more delayed the relaxation process of the polymer matrix. Finally, by studying the relaxation spectra as well as the melt yield stress of polypropylene/CF composites, it was possible to understand the evolution of their morphology upon CF concentration. It was shown that below 5 wt. %, a well dispersed network of CF was obtained, whereas from 5 to 15 wt. % CFs were agglomerating, then leading to a network of agglomerated fibers for concentrations above 15 wt. %. Those assessments done using SAOS results were confirmed by SEM.
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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) | 0.001 | 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".