Size dependent droplet actuation in digital microfluidic systems
Bibliographic record
Abstract
Digital microfluidic systems (DMFS) manipulate liquid droplets with volumes in submicroliter range in two dimensional arrays of cells. Among possible droplet actuation mechanisms, Electrowetting-on-dielectric (EWOD) actuation has been found to be most feasible and advantageous because of low power consumption, ease of signal generation and basic device fabrication. In EWOD based DMFS, droplets are actuated by applying an electric field and thus increasing the wettability on one side of the droplet. In this paper, we show that the EWOD actuation of a droplet can be modeled as a closed loop system having unity feedback of position. Electrode, dielectric and droplet are modeled as a capacitor with variable area as the droplet, considered as a conductor, moves over the dielectric layer. The EWOD force depends on the rate of change of droplet area over the actuated electrode, which in turn depends on the direction of motion and the position of the droplet between the actuated and previous electrode. Thus, EWOD actuation intrinsically utilizes the droplet position to generate sufficient force to accelerate the droplet. When the droplet approaches the final position, the magnitude of force reduces automatically so the droplet decelerates. In case the droplet has sufficient momentum to exceed the final position, the EWOD force, according to the model, will act on the opposite side of the droplet in order to bring it back to the desired position. The dynamic response has been characterized using the proposed model for different droplet sizes, actuation voltages, dielectric thicknesses and electrode sizes.
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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) | 0.001 | 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".