Investigation of electrokinetic mixing in 3D non-homogenous microchannels
Bibliographic record
Abstract
A numerical study of 3D electrokinetic flows through micromixers was performed. The micromixers considered here consisted of heterogeneous rectangular microchannels with prescribed patterns of zeta-potential at their walls. Numerical simulation of electroosmotic flows within heterogeneous channels requires solution of the Navier-Stokes, Ernest-Plank and species concentration equations. It is known that a 3D solution of these equations is computationally very intensive. Therefore, the well-known Helmholtz-Smoluchowski model is often used in numerical simulation of electroosmotic flows. According to 2D studies on electrokinetic mixing inside heterogeneous channels, existence of vortices within the flow field always increases mixing performance. Hence, it may be expected that similar observations pertain to mixing in 3D flows as well. However, investigations on 3D micromixers identified situations in which existence of vortices had little or no significant benefit to the mixing performance. Findings of the present work indicated degree of flow asymmetry as a key parameter for the mixing performance. Since 3D flows are more capable of developing asymmetrical flow patterns, they are expected to have better mixing performance than their 2D counterparts. The results presented here for different 3D cases showed that mixing performance could be improved significantly depending on the alignment of vortex plane relative to the mixing interface of the fluids. These observations confirmed that 2D simulations of mixing could not fully explain behavior of passive micromixers.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".