Water-oil core-shell droplets for electrowetting-based digital microfluidic devices
Bibliographic record
Abstract
Digital microfluidics based on electrowetting-on-dielectric (EWOD) has recently emerged as one of the most promising technologies to realize integrated and highly flexible lab-on-a-chip systems. In such EWOD-based digital microfluidic devices, the aqueous droplets have traditionally been manipulated either directly in air or in an immiscible fluid such as silicone oil. However, both transporting mediums have important limitations and neither offers the flexibility required to fulfil the needs of several applications. In this paper, we report on an alternative mode of operation for EWOD-based devices in which droplets enclosed in a thin layer of oil are manipulated in air. We demonstrate the possibility to perform on-chip the fundamental fluidic operations by using such water-oil core-shell droplets and compare systematically the results with the traditional approach where the aqueous droplets are manipulated directly in air or oil. We show that the core-shell configuration combines several advantages of both the air and oil mediums. In particular, this configuration not only reduces the operation voltage of EWOD-based devices but also leads to higher transport velocities when compared with the manipulation of droplets directly in air or oil.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".