A feedback control system for high-fidelity digital microfluidics
Bibliographic record
Abstract
Digital microfluidics (DMF) is a technique in which discrete droplets are manipulated by applying electrical fields to an array of electrodes. In an ideal DMF system, each application of driving potential would cause a targeted droplet to move onto an energized electrode (i.e., perfect fidelity between driving voltage and actuation); however, in real systems, droplets are sometimes observed to resist movement onto particular electrodes. Here, we implement a sensing and feedback control system in which all droplet movements are monitored, such that when a movement failure is observed, additional driving voltages can be applied until the droplet completes the desired operation. The new system was evaluated for a series of liquids including water, methanol, and cell culture medium containing fetal bovine serum, and feedback control was observed to result in dramatic improvements in droplet actuation fidelity and velocity. The utility of the new system was validated by implementing an enzyme kinetics assay with continuous mixing. The new platform for digital microfluidics is simple and inexpensive and thus should be useful for scientists and engineers who are developing automated analysis platforms.
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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 | 0.001 | 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) | 0.004 | 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".