Electrodiffusiophoresis of Spherical Hydrophobic Colloids
Bibliographic record
Abstract
The present study investigates the controlled electrokinetic motion of spherical colloids under the combined influence of an applied electric field and an electrolyte concentration gradient. The primary goal is to demonstrate how a concentration gradient impacts particle electrophoresis. When a concentration gradient exists in the bulk electrolyte─whether introduced intentionally or not─it drives particle motion through a synergistic effect involving both conventional electrophoresis and an additional mechanism known as diffusiophoresis. In this study, the concentration gradient is aligned to either reinforce or oppose the applied electric field. Besides, the particle is assumed to be charged and hydrophobic. We derived an analytical expression for the electrodiffusiophoretic mobility of such particles within the Debye–Hückel electrostatic limit. We further deduced numerical results for the electrodiffusiophoretic mobility considering the impact of the ion steric effect. The deduced numerical results are validated using both the derived analytical expression for electrodiffusiophoretic mobility under the low charge limit as well as existing experimental data for particle motion driven by either an electric field or an electrolyte concentration gradient. We observed that parameter R, which defines the ratio between the applied electric field strength and the concentration gradient strength, is crucial. It plays a vital role in determining both the magnitude and the propulsion direction of the particle’s mobility. Furthermore, the propulsion direction of the particle can be precisely controlled by adjusting other key parameters, including the choice of electrolytes (and their bulk concentration), hydrodynamic slippage, and the surface charge density of the particle.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".