Flow Patterns and Electrokinetic Mixing Performance in Heterogeneous Microchannels
Bibliographic record
Abstract
Due to the recent advances in microfabrication techniques, it is possible to produce microchannels with positive, negative, or even neutral surface charges. According to several numerical and experimental investigations, such a combination of charge patterns on the microchannel walls results in complex flow fields with circulation zones that are highly desirable for fluid mixing requirements as in lab-on-a-chip devices. In this paper, the mixing efficiency associated with electro-osmotic flows in heterogeneous microchannels is investigated. The Navier-Stokes equations are solved for the flow field along with species transport equations to obtain the concentration field. The effects of the Electric Double Layer (EDL) on the flow field are considered using the Helmholtz-Smoluchowski model in which the EDL effects on the fluid adjacent to the walls are replaced by velocity slip at walls. Different configurations and profiles for the wall charges can be applied to the microchannel walls. In the present study, heterogeneous patterns consisting of different patches with constant zeta-potentials are considered. The flow pattern of a single patch consists of a single vortex attached to the channel wall, which significantly increases the mixing performance. It is expected that a combination of several patches would increase the mixing performance considerably. Therefore, the effects of the size, number, and locations of multiple patches on the mixing performance are investigated in detail. The results for a single patch indicate that the mixing efficiency increases with the size of the patch and its proximity to the microchannel inlet. It is expected that with a suitable combination of patches, an optimized configuration can be found in which the mixing efficiency is maximized and the length of the mixing section is minimized. The results can be applied to the design of micro-mixers to minimize their size while achieving the desired mixing requirements.
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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) | 0.000 | 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".