Experimental Technique for Sensing Droplet Position in Digital Microfluidic Systems Using Capacitance Measurement
Bibliographic record
Abstract
Capacitance measurement has been identified as an effective technique for droplet position sensing in digital microfluidic systems mainly due to its non-intrusive nature. In essence, this technique relies on the correlation between the capacitance of two top-bottom electrodes with the amount of droplet overlap on the electrode. This paper describes an experimental setup used to gather capacitance data from a set of electrodes with varying droplet overlap to determine the droplet position. A prototype closed digital microfluidic (DMF) system consisting of an array of electrodes in the form of a 2 × 2 matrix was fabricated. A circular droplet was positioned on the DMF system, and capacitance measurements for each of the four electrodes were taken using a fast data acquisition device. A sufficiently accurate approximation of the droplet position was made using the four capacitance measurements. The paper presents the experimental results and also discusses the sources of error, viability of the experimental setup and manufacturing procedure for use in the development of capacitance measurement droplet position sensing techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".