High efficient biofluid micromixing using ultra-fast AC electrothermal flow
Bibliographic record
Abstract
Electrokinetics have been widely used in lab-on-a-chip devices for fluid manipulation applications. The AC electrothermal (ACET) effect is a highly efficient technique for biofluids (σ>0.1 S/m) active micromixing, which can be used in chemical, biological, and medical analysis systems. In this paper, a novel idea of employing microelectrode arrays placed on sidewalls of a fluidic microchannel for increasing the mixing efficiency of biofluids is numerically investigated. It was reported that coplanar asymmetric microelectrode arrays are capable of creating ACET vortices in the bulk of a high conductive electrolyte solution. Two electrode arrays can be placed on the sidewalls of a microchannel, each of which has a different role, one pumps the biofluid while the other mixes it. Two different actuation patterns were applied to the electrodes. One pair of microelectrodes was simulated and the simulation procedure was then verified by conducting experiments for ACET flow measurement in a similar geometry. Microelectrode arrays were fabricated on 1mm thick glass substrates using photolithography. A 800 μm thick fluidic microchannel was fabricated by soft lithography of Polydimethylsiloxane (PDMS). The results showed that such a technique can dramatically increase the mixing of the solution while pumping is taking place. The mechanism was capable of efficiently mixing biofluid solutions (resultant concentration ratio of up to 80%) in a short time (<3 min) and short distance (<600 μm) for a 300×300 μm2 fluidic microchannel cross section area. Medical analysis such as heterogeneous immunoassays can be potential applications of such micromixing technique.
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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".