Capacitance-based droplet position estimator for digital microfluidic devices
Bibliographic record
Abstract
Digital microfluidic (DMF) devices manipulate minuscule droplets through basic fluidic operations including droplet transport, mixing and splitting commonly known as the building blocks for complete laboratory analyses on a single device. A DMF device can house various chemical species and confine chemical reactions within the volume of a droplet much like a micro-reactor. The automation of fluidic protocols requires a feedback controller whose sensor is capable of locating droplets independent of liquid composition (or previous knowledge of liquid composition). In this research, we present an estimator that tracks the continuous displacement of a droplet between electrodes of a DMF device. The estimator uses a dimensionless ratio of two electrode capacitances to approximate the position of a droplet, even, in the domain between two adjacent electrodes. This droplet position estimator significantly enhances the control precision of liquid handling in DMF devices compared to that of the techniques reported in the literature. It captures the continuous displacement of a droplet; valuable information for a feedback controller to execute intricate fluidic protocols including droplet positioning between electrodes, droplet velocity and acceleration control. We propose a state estimator for tracking the continuous droplet displacement between two adjacent electrodes. The dimensionless nature of this estimator means that any droplet composition can be sensed. Thus, no calibration for each chemical species within a single DMF device is required. We present theoretical and experimental results that demonstrate the efficacy of the position estimator in approximating the position of the droplet in the interval between two electrodes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".