Effect of Viscosity on Solvent-Free Extrusion Emulsification: Molecular Structure
Bibliographic record
Abstract
A new continuous emulsification technique known as solvent-free extrusion emulsification (SFEE) was recently introduced to prepare submicron particles (100–500 nm) from high viscosity polymers (100–1000 Pa·s) with a twin screw extruder. The present study examined the influence of matrix viscosity on its dispersion mechanism using cross-linked polyester as a viscosity modifier. The investigation used an inline rheometer for transient and steady state viscosity measurements, and offline characterizations including Soxhlet extraction, colorimetric titration, and particle size analysis. Though it remained possible to produce particles close to their target size of 100–200 nm, particle size was notably increased by varying the matrix viscosity from 250 Pa·s for the neat polyester up to 630 Pa·s with the added modifier. The results point to thicker striated lamellae from less effective mixing prior to phase inversion when the matrix viscosity was increased without a corresponding increase in surface active species. The study was primarily focused on the dispersion zone revealing that a longer mixing zone for dispersing the water into the polyester was beneficial to forming smaller particles. A preliminary investigation on the downstream dilution zone was included, finding that a longer region produced smaller particles as well so long as the water temperature remained high.
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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 | 0.001 |
| 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".