Stability of multiple emulsions under shear stress
Bibliographic record
Abstract
Multiple liquid emulsions of the water in oil in water (W1/O/W2) type are used in a variety of consumer or technical applications, for instance in the encapsulation of certain active ingredients. The encapsulation process and release mechanisms of the inner phase of the carrier drops are important in order to properly process and formulate such liquid‐liquid systems. In this work the stability and breakage of multiple W1/O/W2 emulsions under mechanical shear stress are investigated for emulsions with different surfactants and surfactant concentrations of the internal emulsion. Stressing the emulsions in a mechanical stirring process is compared to the membrane emulsification process. The membrane emulsification process results in higher encapsulation efficiencies than the stirring process. The emulsion droplets were subjected to shear stress below and above the critical capillary number for drop breakup. The results show that stable inner emulsions with sufficient surfactant concentrations increase the overall encapsulation efficiency for multiple emulsions subjected to shear stress, although the effect is not prominent. The depletion of the carrier oil droplets could be achieved for Ca numbers below the critical limit, reducing the encapsulation efficiency below 10 %. This shows that even a low shear stress can result in content release from the internal droplet phase. The experimental emulsion release study is supported by a numerical simulation of drop deformation and break‐up under shear stress.
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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 | 0.001 | 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 | 0.001 |
| 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".