Bibliographic record
Abstract
The formation of water-in-oil emulsions is described. Research has shown that asphaltenes are the prime stabilizers of water-in-oil emulsions and that resins are necessary to solvate the asphaltenes. It has also been shown that many factors play a role, including the amount of saturates and the oil viscosity. Two schemes are given to describe the formation of emulsions using the characteristics of starting oils including the resin and asphaltene contents and the viscosity. Essentially, water droplets injected into the oil by turbulence or wave action can be stabilized temporarily by the oil viscosity and on a longer-term basis by resins and then asphaltenes. Depending on the starting oil properties, four types of water-in-oil types are created: mesostable and stable emulsions, entrained water-in-oil type, and unstable or those that do not form type. Each type is described and has unique properties. For most oils, loss of lighter components by evaporation is necessary before the oils will form a water-in-oil type. It was noted that variability in emulsion formation is, in part, due to the variation in the types of compounds in the asphaltene and resin groups. Certain types of these compounds form more stable emulsions than others within the same asphaltene/resin groupings. A review of numerical modeling schemes for the formation of water-in-oil emulsions is given. A recent model is based on empirical data and the corresponding physical knowledge of emulsion formation. The density, viscosity, and asphaltene and resin contents were correlated with a new stability index.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".