Physical features’ characterization of the water-in-mineral oil macro emulsion stabilized by a nonionic surfactant
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".