Optimization of device geometry in single-plate digital microfluidics
Bibliographic record
Abstract
Digital microfluidics is a popular tool for lab-on-a-chip applications and is typically implemented in one of two formats: single-plate (“open”) devices or two-plate (“closed”) devices. Single-plate devices have some advantages relative to the more common two-plate format such as faster mixing, the capacity to move larger volumes on a given footprint, and easier access to droplets for handling or optical detection. In contrast with the two-plate format, in which ground potential is generally supplied via a top electrode, in the single-plate format, many different geometries of ground wires/electrodes have been used. Until the present study, there has been no metric to determine which of these geometries is best suited for droplet actuation. Here, we present a combination of numerical simulations and experimental tests to compare six different single-plate designs. We applied finite element analysis, using the commercially available COMSOL software package to calculate the electrodynamic actuation forces in each of the different designs and used the results to optimize device design. Forces predicted by the electrodynamic model were in agreement with forces predicted using electromechanical models. More importantly, results were verified experimentally using a unique technique that permits indirect estimation of actuation forces on digital microfluidic devices. This work illustrates the promise of using numerical modeling to enhance the design and performance of digital microfluidic devices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".