A novel alternating current multiple array electrothermal micropump for lab-on-a-chip applications
Bibliographic record
Abstract
The AC electrothermal technique is very promising for biofluid micropumping, due to its ability to pump high conductivity fluids. However, compared to electroosmotic micropumps, a lack of high fluid flow is a disadvantage. In this paper, a novel AC multiple array electrothermal (MAET) micropump, utilizing multiple microelectrode arrays placed on the side-walls of the fluidic channel of the micropump, is introduced. Asymmetric coplanar microelectrodes are placed on all sides of the microfluidic channel, and are actuated in different phases: one, two opposing, two adjacent, three, or all sides at the same time. Micropumps with different combinations of side electrodes and cross sections are numerically investigated in this paper. The effect of the governing parameters with respect to thermal, fluidic, and electrical properties are studied and discussed. To verify the simulations, the AC MAET concept was then fabricated and experimentally tested. The resulted fluid flow achieved by the experiments showed good agreement with the corresponding simulations. The number of side electrode arrays and the actuation patterns were also found to greatly influence the micropump performance. This study shows that the new multiple array electrothermal micropump design can be used in a wide range of applications such as drug delivery and lab-on-a-chip, where high flow rate and high precision micropumping devices for high conductivity fluids are needed.
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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 | 0.001 |
| Open science | 0.001 | 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".