Physical features’ characterization of the water-in-mineral oil macro emulsion stabilized by a nonionic surfactant
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Bibliographic record
Abstract
Water-in-oil (w/o) emulsions are widely used in the food and pharmaceutical industries, among others. Moreover, the most common type of emulsion produced and handled in the oil industry processes is the w/o emulsion. This study investigates the features of a water-in-mineral oil macro-emulsion formulated with mineral oil as the continuous phase and Span 83 as the nonionic surfactant. Emulsions are prepared at room temperature according to the hydrophilic–lipophilic difference (HLD) theory and were tested for the mean droplet size and droplet size distribution, viscosity, and kinetic stability. An empirical correlation was introduced that estimates the viscosity of the water-in-mineral oil macro-emulsions and captures the non-Newtonian behavior at larger water fractions. The effect of electrolyte and internal phase concentration was specifically assessed on the emulsion flocculation and the stability of the system. Stability tests show a threshold electrolyte concentration exists after which droplets coalesce upon collision and flocculation. Salting out is most likely the responsible mechanism of phase separation in the emulsions with higher electrolyte concentrations. The results imply that sedimentation is accountable for the formation of different layers in emulsion with time. The sedimentation rate was intensified for emulsion with smaller water content (64% variation in 3 days between 10% emulsion and 40% emulsion) and concentrated emulsions were found to be more stable. Also, the size of the droplets was influenced by the NaCl concentration, surfactant concentration, and phase ratio.
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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 | 0.000 | 0.000 |
| 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 it