Effect of the dispersed phase fraction on particle size in blends with high viscosity ratio
Bibliographic record
Abstract
Abstract It has been reported that for polymer blends with high viscosity ratio (>1), the size of the dispersed particles decreases with increasing volume fraction of the dispersed phase. In order to explain this effect, an equation was derived for the affine deformation of an imaginary plane of the dispersed phase in stratified two‐phase steady, simple, shear flow. The model predicts that for viscosity ratio >1, the deformation rate increases with volume fraction of the dispersed phase, and the shear stress also increases, leading to an increase of the breakup time. Therefore, the total deformation of the dispersed phase, before breakup, increases with increase of volume fraction, resulting in a decrease of the size of the dispersed phase particles. Accordingly, one can expect that in industrial mixers, the particle size of the blends should decrease as the volume fraction increases, if coalescence is suppressed. Experiments were carried out in a Haake batch mixer, using polyethylene/polyamide‐6 blends compatibilized by adding maleic anhydride grafted polyethylene. Particle size decreased up to 20 wt% polyamide‐6, at 100, 150, and 200 RPM, and increased between 20 and 30 wt%. The decrease of the particle size is mainly due to increased deformation of the dispersed phase. The increase of the particle size above 20 wt% is due to coalescence at high fractions.
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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 | 0.001 | 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".