Method for Individual Control of Multiplexed Droplet Generation in Digital Microfluidic Devices
Bibliographic record
Abstract
This investigation presents two methods for multiplexed droplet generation in digital microfluidic devices. Analytical and experimental results show that the number of electrical output signals required for multiple reagent systems can be reduced by electrically connecting all but one electrode in each droplet generation path on the device. Both methods reduce the number of electrical signals required for generating droplets from multiple reservoirs by M(N−1), where M is the number of manipulation electrodes in the generation path and N is the number of reservoirs on the device. The first method uses individually controlled reservoirs to minimize the manipulation of the fluid in the unused reservoir, while the second method individually controls one of the electrodes in the generation pathway to allow for closed loop control of droplet generation. In both cases, droplets are kept at rest by simultaneously activating or deactivating all adjacent electrodes. These methods can be easily integrated into devices with multiple reservoirs without computational expense or prior knowledge of the electrode activation sequence. They can also be used in concert with droplet control algorithms for pin constrained systems to further reduce the number of output channels required in a digital microfluidic device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".