What Causes the Formation of Water-in-Oil Emulsions?
Bibliographic record
Abstract
ABSTRACT The results of studies conducted over the past 6 years to characterize why water-in-oil emulsions form are summarized. It is shown that water droplets are held in oil by a combination of viscous and interfacial forces. The stability of an emulsion is very important in understanding its formation because stability is the endpoint or measurement of the entire process. Emulsions can be grouped into three categories: stable, unstable, and mesostable. Each has distinct physical properties. For example, the viscosity of a stable emulsion at a shear rate of I reciprocal second is at least three orders-of-magnitude greater than that of the starting oil. An unstable emulsion usually has a viscosity no more than two orders-of-magnitude greater than that of the starting oil. The zero-shear-rate viscosity is at least six orders-of-magnitude greater than the starting oil for a stable emulsion. For an unstable emulsion, it is usually less than two or three orders-of-magnitude greater than the viscosity of the starting oil. and finally, a stable emulsion has a significant elasticity, whereas an unstable emulsion does not. The stability of emulsions has been studied by examining their asphaltene content and their resin content. Results are reported showing that asphaltenes and resins are responsible for stability. It is noted that, given the correct chemical composition, primarily asphaltenes, sea energy is needed. The properties of the starting oil are the important factor in determining what type of water-in-oil state is produced. Composition and property ranges are given for the starting oil to form each of the water-in-oil states. Important property factors are the asphaltene content, resin content, and starting oil viscosity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".