Effect of Ionic Surfactants on the Electrokinetic Control of Viscous Fingering
Bibliographic record
Abstract
The control of interfacial instabilities using an external electric field has been proven to be feasible in the presence of a nonconducting/perfect dielectric fluid such as mineral oil. In these electrokinetic control strategies, there is a competition between the effect of interfacial charges and viscous and interfacial forces on the displacement. The effect of interfacial charges becomes increasingly important in the presence of ionic surfactants as a result of Maxwell stresses induced at the fluid–fluid interface when electric fields are present. We experimentally investigate the combined effect of electrokinetic effects, viscous and interfacial forces, and local charges on the (de)stabilization of the interface between two immiscible fluids in the presence of anionic and cationic surfactants in a rectangular Hele–Shaw cell. Both qualitative and quantitative analyses revealed that the addition of surfactants significantly changes the system behavior and the system response depends on the charge of the surfactant. Using a surfactant that reduces interfacial tension (IFT) more results in a high viscous force, narrower fingers, and smaller swept areas, which is traditionally undesirable in oil recovery operations. On the other hand, using an anionic surfactant resulted in smaller viscous forces, wider finger bifurcations, and larger swept areas, traditionally associated with positive effects in enhanced oil recovery. Changing the direction of the electric field impacts the overall displacement, but the effect of interfacial charges induced by the ionic surfactants stays consistent. Overall, the work shows that applying an electric field can positively influence the overall displacement of oil by water in a porous medium and that ionic surfactants can further enhance or decrease this effect.
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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 | 0.001 |
| 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) | 0.001 | 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".