Surface Interaction of Water-in-Oil Emulsion Droplets with Interfacially Active Asphaltenes
Bibliographic record
Abstract
Adsorption of interfacially active components at the water/oil interface plays critical roles in determining the properties and behaviors of emulsion droplets. In this study, the droplet probe atomic force microscopy (AFM) technique was applied, for the first time, to quantitatively study the interaction mechanism between water-in-oil (W/O) emulsion droplets with interfacially adsorbed asphaltenes. The behaviors and stability of W/O emulsion droplets were demonstrated to be significantly influenced by the asphaltene concentration of organic solution where the emulsions were aged, aging time, force load, contact time, and solvent type. Bare water droplets could readily coalesce with each other in oil (i.e., toluene), while interfacially adsorbed asphaltenes could sterically inhibit droplet coalescence and induce interfacial adhesion during separation of the water droplets. For low asphaltene concentration cases, the adhesion increased with increasing asphaltene concentration (≤100 mg/L), but it significantly decreased at relatively high asphaltene concentration (e.g., 500 mg/L). Experiments in Heptol (i.e., mixture of toluene and heptane) showed that the addition of a poor solvent for asphaltenes (e.g., heptane) could enhance the interfacial adhesion between emulsion droplets at relatively low asphaltene concentration but could weaken the adhesion at relatively high asphaltene concentration. This work has quantified the interactions between W/O emulsion droplets with interfacially adsorbed asphaltenes, and the results provide useful implications into the stabilization mechanisms of W/O emulsions in oil production. The methodology in this work can be readily extended to other W/O emulsion systems with interfacially active components.
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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 | 0.000 | 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".