Image-based feedback and analysis system for digital microfluidics
Bibliographic record
Abstract
Digital microfluidics (DMF) is a technology that provides a means of manipulating nL-μL volumes of liquids on an array of electrodes. By applying an electric potential to an electrode, these discrete droplets can be controlled in parallel which can be transported, mixed, reacted, and analyzed. Typically, an automation system is interfaced with a DMF device that uses a standard set of basic instructions written by the user to execute droplet operations. Here, we present the first feedback method for DMF that relies on imaging techniques that will allow online detection of droplets without the need to reactivate all destination electrodes. Our system consists of integrating open-source electronics with a CMOS camera and a zoom lens for acquisition of the images that will be used to detect droplets on the device. We also created an algorithm that uses a Hough transform to detect a variety of droplet sizes and to detect singular droplet dispensing and movement failures on the device. As a first test, we applied this feedback system to test droplet movement for a variety of liquids used in cell-based assays and to optimize different feedback actuation schemes to improve droplet movement fidelity. We also applied our system to a colorimetric enzymatic assay to show that our system is capable of biological analysis. Overall, we believe that using our approach of integrating imaging and feedback for DMF can provide a platform for automating biological assays with analysis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".