In-line ultrasonic characterization of shear dispersion processes of polydisperse fillers in polymer melts
Bibliographic record
Abstract
Shear break-up processes of polydisperse fractal clusters are investigated by the ultrasound scattering technique. Within the framework of fractal aggregation and the hybrid approach model for polydisperse correlated scatterers, the concept of variance in the local filler concentration is used to derive a new expression for the scattering cross-section for polydisperse fractal aggregates in the Rayleigh scattering regime. Considering the scaling laws for the shear-induced disruption of the clusters, the shear stress dependence of the ultrasound scattered intensity for polydisperse fractal aggregates is also derived. The fractal scattering regime is further discussed for both monodisperse and polydisperse clusters of size larger than the wavelength. In-line ultrasonic measurements for the shear disruption processes of silica fume fillers compounded with polypropylene during extrusion are investigated. A critical disaggregation shear stress is determined and is found to decrease with the filler surface treatment concentration. This stress is representative of the particle adhesiveness and aggregate dispersion in the matrix. This is confirmed by the improvement in impact resistance tests. On the basis of the scaling laws and the self-consistent-field approximation usually used in the microrheological models, the shear-thinning behaviour of silica fume clusters is successfully simulated.
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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) | 0.003 | 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".