Experimental Evidence of Enhanced Adsorption Dynamics at Liquid–Liquid Interfaces under an Electric Field
Bibliographic record
Abstract
Abstract In this work, we investigate the adsorption of surface-active compounds at the water–oil interface subjected to an electric field. A fluid system comprising a pendent water drop surrounded by an asphaltene-rich organic phase is exposed to a DC uniform electric field. Two subfractions of asphaltenes having contrasting affinities to the water–oil interface are used as surface-active compounds. The microscopic changes in the drop shape, as a result of asphaltene adsorption, are captured and the drop profiles are analyzed using our in-house code for axisymmetric drop shape analysis (ADSA) under an electric field. The estimates of dynamic interfacial tension under different strengths of the field (E0) and concentrations of the asphaltene subfractions (C) are used to calculate adsorption dynamics and surface excess. The experimental observations and careful analyses of the data suggest that the externally applied electric field significantly stimulates the mass-transfer rate at the liquid–liquid interface. The enhancement in mass transport at the water–oil interface can be attributed to the axisymmetric electrohydrodynamic fluid flows generated on either side of the interface. The boost in mass transport is evident from the growing decay in equilibrium interfacial tension (γeq) and increased surface excess (Γeq) upon increasing strength of the applied electric field. The mass-transfer intensification does not increase monotonously with the electric field strength above an optimum E0, which is in agreement with the previous theoretical studies in the literature. However, these first explicit experimental measurements of adsorption at an interface under an electric field suggest that the optimum E0 is determined by characteristics of the surface-active molecules.
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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) | 0.002 | 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".