Droplet Electrophoresis with Internal Free Ions: Effect of Permittivity Changes in the Electric Double Layer
Bibliographic record
Abstract
Droplet electrophoresis (EP) is of interest in biological systems, microfluidics, and separation techniques. We investigate EP of an oil droplet that contains free ions and is stabilized in an electrolyte solution through an amphoteric surfactant. The presence of mobile ions within the droplet leads to the creation of a distinct nonzero space charge density inside the droplet and consequently, formation of an inner EDL inside the droplet in addition to the traditionally considered outside EDL. While we assume the permittivity inside the inner EDL to remain constant, we consider both the case of constant and variable permittivity in the outer EDL. Our findings demonstrate a change in the droplet direction of motion in the electric field when transitioning from acidic to alkaline pH, regardless of permittivity and ionic strength in both oil and electrolyte. We further find a significant reduction in the magnitude of droplet velocity in the case of a variable permittivity due to reduction of the local space charge density within the EDL surrounding the droplet. When decreasing the viscosity ratio of the oil to the electrolyte, in all cases we find a reduction in droplet velocity. This decline is attributed mostly to the formation and strength of a vortex around the droplet. We finally demonstrate that with constant permittivity in the outer EDL, the variation in κa outer has a more significant effect on the droplet’s EP velocity than altering κa inner . However, in cases where the body forces inside of the droplet dominate, minor changes in the outer electrolyte concentration have no influence on the droplet motion, which is relevant for biological colloids that can contain significant free internal charges. Our results are important for the manipulation of biological colloids, water and waste treatment such as lubricant removal from processing streams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".