Simultaneous Pumping and Mixing of Biological Fluids in a Double-Array Electrothermal Microfluidic Device
Bibliographic record
Abstract
Transport and mixing of minute amounts of biological fluids are significantly important in lab-on-a-chip devices. It has been shown that the electrothermal technique is a suitable candidate for applications involving high-conductivity biofluids, such as blood, saliva, and urine. Here, we introduce a double-array AC electrothermal (ACET) device consisting of two opposing microelectrode arrays, which can be used for simultaneous mixing and pumping. First, in a 2D simulation, an optimum electrode-pair configuration capable of achieving fast transverse mixing at a microfluidic channel cross-section is identified by comparing different electrode geometries. The results show that by adjusting the applied voltage pattern and position of the asymmetrical microelectrodes in the two arrays, due to the resultant circular flow streamlines, the time it takes for the analytes to be convected across the channel cross-section is reduced by 95% compared to a diffusion-only-based transport regime, and by 80% compared to a conventional two-layer ACET device. Using a 3D simulation, the fluid transport (pumping and mixing) capabilities of such an electrode pair placed at different angles longitudinally relative to the channel was studied. It was found that an asymmetrical electrode configuration placed at an angle in the range of 30 ° ≤ θ ≤ 45 ° can significantly increase transversal mixing efficiency while generating strong longitudinal net flow. These findings are of interest for lab-on-a-chip applications, especially for biosensors and immunoassays, where mixing analyte solutions while simultaneously moving them through a microchannel can greatly enhance the sensing efficiency.
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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".