Effect of Processing Parameters on the Production of Pickering Emulsions
Bibliographic record
Abstract
Emulsification experiments were performed in an unbaffled tank using an off-centered pitched-blade turbine. The effects of mixing time, particle concentration, impeller speed, and oil viscosity on the production of Pickering emulsions were investigated. Emulsification efficiency was quantified by size distribution measurements using a Mastersizer 3000 (Malvern). Mixing and circulation times were measured using a decolorization technique to investigate the effect of impeller speed on the stabilization process. There was a strong interaction among all the parameters affecting mixing tank hydrodynamics. Optimal conditions were determined in order to produce the smallest droplets with the narrowest distribution. Droplet production and coverage were involved simultaneously in the interaction, which is very different from surfactant-based systems where the stabilization step is much faster than the droplet production step and where droplet breakage is promoted by reducing the interfacial tension. The impeller speed and emulsification time results indicated that the shear level in the impeller zone, the energy dissipation rate, and the fluid circulation time are important drivers of the stabilization mechanism. The particle concentration results showed that the particle effect played a role in the production of an interface and in stabilization efficiency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".