Semiempirical Approach for Predicting the Mean Size of Solid-Stabilized Emulsions
Bibliographic record
Abstract
The final mean droplet size of concentrated solid-stabilized emulsions was predicted using a semiempirical approach. The first step was to estimate the coverage and interface generation potentials. The coverage potential was predicted from the properties of the particles while the interface generation potential was predicted from the correlation developed by Calabrese et al. (1986), which was modified to include the effect of coalescence since the effects of high oil and particle concentrations on the breakage and coalescence processes are not accounted for in this correlation. The second step was to compare the coverage and interface generation potentials and to deduce the theoretical stabilized interface by assuming that it is governed by the lowest potential. The last step was to determine the effectively covered interface from the theoretical stabilized interface by considering stabilization efficiencies. The stabilization efficiency was defined by analyzing the stabilization process at the droplet and the individual particle scales. At the droplet scale, assuming that emulsions are stabilized by covering the droplets with a network of particles, coverage efficiency was defined by comparing the coverage rate to the coalescence rate. At the particle scale, assuming that the droplet coverage results from the attachment of individual particles, three other efficiencies were defined from the particle attachment steps: The particle and the droplet first enter into contact during the collision step; the three-phase contact line is then formed and evolves until reaching the equilibrium position at which the attachment force should be strong enough to avoid detachment. This multistep approach generated size predictions that were in good agreement with the experimental measurements for a broad range of viscosities, at different impeller speeds, and particle concentrations. This led to highlight the impact of operating conditions on the emulsification process. We show that it is possible to link emulsification systems to different geometries by taking the mechanisms driving the emulsification process into consideration suggesting new avenues for scaling up emulsification processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".