An Electrokinetic Micro Mixer for Lab-on-Chip Applications: Modeling, Validation, and Optimization
Bibliographic record
Abstract
Mixing of liquids in micro mixers at low Reynolds numbers is a challenging task since the flow regime is laminar and it is difficult to engage instabilities of the flow. In many microfluidic systems, mixing can be improved by means of electrokinetic effects. A favorable micro mixer design consists of a Y-junction, where the different liquid streams merge, and a subsequent meandering microchannel. A pressure gradient pumps the liquids to be mixed through the microchannel. An oscillating electrical field is superimposed onto the pressure-driven base flow which generates an additional electrokinetic (electro osmotic) flow. These oscillating secondary flows in conjunction with the meandering geometry are responsible for stretching and folding of the contact area of the liquids to be mixed which enhances the mass transfer rates considerably. In this contribution, we present a mathematical model which allows for the numerical simulation of flow, electrical potential, and species concentration. The model is validated by experiments relying on Micro Particle Image Velocimetry (μPIV). Consequently, this model can be used to numerically optimize the electrical field in order to achieve fast and high mixing even at low Reynolds numbers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".