Field‐programmable lab‐on‐a‐chip based on microelectrode dot array architecture
Bibliographic record
Abstract
The fundamentals of electrowetting-on-dielectric (EWOD) digital microfluidics are very strong: advantageous capability in the manipulation of fluids, small test volumes, precise dynamic control and detection, and microscale systems. These advantages are very important for future biochip developments, but the development of EWOD microfluidics has been hindered by the absence of: integrated detector technology, standard commercial components, on-chip sample preparation, standard manufacturing technology and end-to-end system integration. A field-programmable lab-on-a-chip (FPLOC) system based on microelectrode dot array (MEDA) architecture is presented in this research. The MEDA architecture proposes a standard EWOD microfluidic component called 'microelectrode cell', which can be dynamically configured into microfluidic components to perform microfluidic operations of the biochip. A proof-of-concept prototype FPLOC, containing a 30 × 30 MEDA, was developed by using generic integrated circuits computer aided design tools, and it was manufactured with standard low-voltage complementary metal-oxide-semiconductor technology, which allows smooth on-chip integration of microfluidics and microelectronics. By integrating 900 droplet detection circuits into microelectrode cells, the FPLOC has achieved large-scale integration of microfluidics and microelectronics. Compared to the full-custom and bottom-up design methods, the FPLOC provides hierarchical top-down design approach, field-programmability and dynamic manipulations of droplets for 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".