Fluid flow study of an AC electrothermal micropump consisting of multiple arrays of microelectrodes for biofluidic applications
Bibliographic record
Abstract
Electrokinetics has many applications in a wide range of areas, such as lab-on-a-chip and biomedical microdevices. The electrothermal effect has been used for biofluid delivery systems since it has high pumping efficiency for high conductive liquids (>0.1 S/m) compared to other electrokinetic techniques such as electroosmosis. AC electrothermal (ACET) micropumps are based on the temperature gradient caused by Joule heating or an external heat source, which generates permittivity and conductivity gradients in the bulk of the liquid. When the liquid is subjected to an electric field, the ACET force is created. Electrode geometry significantly affects the electric field distribution, which can yield stronger ACET forces. Previously electrode dimension optimization has been performed for a single-array coplanar asymmetric configuration in order to obtain maximum ACET velocities. In this paper we expand the study to other governing parameters in a multiple-row microelectrode array configuration consisting of microelectrodes placed on top, bottom, and/or side walls of a microchannel. The studied parameters are the substrate material and thickness, ambient temperature, fluid viscosity, and actuation frequency. Electrode dimensions remain constant during the study (120 μm wide and 20 μm thin electrodes, 20 μm gap). The study is performed using finite element analysis software for one pair of microelectrodes on each array with periodic boundary conditions. The simulation data is then compared with experimental data for a single combination of the aforementioned parameters. The results show that the effect of these parameters on ACET flow can be significant.
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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 | 0.001 | 0.000 |
| 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".