Digital microfluidic operations on micro-electrode dot array architecture
Bibliographic record
Abstract
As digital microfluidics-based biochips find more applications, their complexity is expected to increase significantly owing to the trend of multiple and concurrent assays on the chip. There is a pressing need to deliver a top-down design methodology that the biochip designer can leverage the same level of computer-aided design support as the semi-conductor industry now does. Moreover, as microelectronics fabrication technology is scaling up and integrated device performance is improving, it is expected that these microfluidic biochips will be integrated with microelectronic components in next-generation system-on-chip designs. This study presents the analysis and experiments of digital microfluidic operations on a novel electrowetting-on-dielectric-based 'micro-electrode dot array architecture' that fosters a development path for hierarchical top-down design approach for digital microfluidics. The proposed architecture allows dynamic configurations and activations of identical basic microfluidic unit called 'micro-electrode cells' to design microfluidic components, layouts, routing, microfluidic operations and applications of the biochip hierarchically. Fundamental microfluidic operations have been successfully performed by the architecture. In addition, this novel architecture demonstrates a number of advantages and flexibilities over the conventional digital microfluidics in performing advanced microfluidic operations.
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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 | 0.000 |
| 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".