Vertical Addressing of 1‐Plane Electrodes for Digital Microfluidics
Bibliographic record
Abstract
Abstract Digital microfluidics (DMF) has become a mainstay in the microfluidics and microelectromechanical communities. Many users rely on simple DMF devices featuring a small number of rows and columns of electrodes that can be rapidly manufactured using “one plane” lithographic or printing techniques. But as the popularity of DMF grows, there are increasing needs for larger devices that can facilitate multiplexed handling of many samples and reagents in parallel. One option for scaling DMF devices is to use “vertical addressing” techniques such as printed circuit boards (PCBs), but PCBs formed using standard techniques exhibit topography that is not ideal for smooth and reliable droplet movement. A new method to produce DMF devices using vertical addressing of 1‐plane electrodes (VAPE‐DMF) is introduced. This method, which separates devices into “covers” (bearing 1‐plane electrodes) and “sub‐substrates” (for vertical addressing), enables rapid and inexpensive manufacture of devices with arbitrarily large driving electrode arrays. This work describes how to manufacture VAPE‐DMF devices and demonstrates a proof‐of‐concept device with an array of 336 electrodes to handle 48 droplets to run 24 reactions in parallel. It is proposed that VAPE‐DMF represents a useful new development for the growing community of users and innovators of digital microfluidics and related methods.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".