Electrokinetic Transport through Rough Microchannels
Bibliographic record
Abstract
Surface roughness is present in most microfluidic devices as a result of the microfabrication techniques or particle adhesion. It is highly desirable to understand the roughness effect on microscale transport processes. In this study, we developed a 3-D, finite-volume-based numerical model to simulate electroosmotic transport in microchannels with rectangular prism rough elements on the surfaces. Various configurations of roughness were investigated, and the results show different degrees of an even-out effect on liquid transport due to the roughness-induced local pressure field and the variation of the electroosmotic slip boundary velocities. 3D-sample transport through rough microchannels was analyzed. The results demonstrate that the sample's transport under the electrical field is much faster in the pathway between the rough elements; the concentration field in the height and width direction is not uniform. The influence of the electrokinetic properties on liquid flow and sample transport was studied. It was found that the increase of the electroosmotic mobility or the decrease of the electrophoretic mobility can dramatically enhance the uniformity of the concentration field.
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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 | 0.001 |
| 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 | 0.001 | 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".