Electrokinetic transport of charged solutes in micro‐ and nanochannels: The influence of transverse electromigration
Bibliographic record
Abstract
The accurate prediction of electrokinetic migration velocity and dispersion is crucial to separating electrophoretically charged solutes in micro- or nanochannels. In this paper, we investigate numerically the influence of transverse electromigration (TEM) on the solute electrokinetic transport in a series of micro- and nanochannels. The TEM, often ignored in previous studies, is demonstrated to significantly affect the solute migration velocity in nanochannels and the electrokinetic dispersion in microchannels. This is because the TEM can force either positively charged solutes into or negatively charged solutes out of the electrical double layer that forms adjacent to the negatively charged channel wall and contains the velocity gradients. Analytical solutions are also derived for characterizing the electrokinetic transport of charged solutes in nanochannels, which has been validated to be in good agreement with the numerical simulation. Moreover, we demonstrate that the proposed analytical formula for the solute migration velocity actually applies to channels of any size.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".