A World-to-Chip Interface for Digital Microfluidics
Bibliographic record
Abstract
Digital microfluidics (DMF) is a fluid handling technique that enables manipulation of discrete droplets on an array of electrodes. There is considerable enthusiasm for this method because of the potential for array-based screening applications. A limitation for DMF is nonspecific adsorption of reagents to device surfaces. If a given device is used to actuate multiple reagents, this phenomenon can cause undesirable cross-contamination. A second limitation for DMF (and all other microfluidic systems) is the "world-to-chip" interface; it is notoriously difficult to deliver reagents and samples to such systems without compromising the oft-hyped advantages of rapid analyses and reduced reagent consumption. We introduce a new strategy for digital microfluidics, in which a removable plastic "skin" is used to (a) eliminate cross-contamination and (b) bridge the world-to-chip interface. We demonstrated the utility of this format by implementing on-chip protein digestion on immobilized enzyme depots. This new method has the potential to transform DMF from being a curiosity for aficionados into a technology that is useful for biochemical applications at large.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.036 |
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".