Field‐programmable lab‐on‐a‐chip based on microelectrode dot array architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it