Phase inversion of a solid‐stabilized emulsion: Effect of particle concentration
Bibliographic record
Abstract
Abstract Tiny water droplets in oil emulsions are commonly encountered in the petroleum industry. The high viscosity of the oil hampers the physical separation of the water droplets from the oil. 1 Phase inversion could be a potential workaround for this problem by making water, a much less viscous phase, the continuous medium. In the present work, we focused on triggering phase inversion of a solid‐stabilized emulsion. We induced a catastrophic phase inversion by the continuous addition of a dispersed phase. The evolution of droplet morphology during the phase inversion process was observed and was measured in‐line using a particle vision microscope, which proved to be a powerful tool for monitoring this rapid, unstable process. A linear relationship between the droplet size and the dispersed phase volume fraction before the phase inversion was observed, indicating that a higher dispersed phase volume fraction was needed for the phase inversion to occur with higher particle concentrations. The phase inversion conditions were applied in a regime where the particles were insufficient to fully cover the interface. Our findings indicated that the number of particles per surface area appears to be a crucial parameter in triggering phase inversion, regardless of the particle concentration. The phase inversion mechanism of our solid‐stabilized emulsion can be explained by the relationship between the initial particle coverage of the interface and the coalescence rate of the system.
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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 | 0.000 | 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 | 0.000 | 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".