A scalable fibre optic sensing architecture for lab-on-a-chip devices
Bibliographic record
Abstract
Advancements in continuous and digital microfluidics (DMF) for integrated optics technologies are improving the feasibility of biophotonic sensors within lab-on-a-chip devices. Lab-on-a-chip diagnostic devices are achieving unprecedented high levels of throughput. Digital microfluidics, with its reconfigurable nature, is often utilized over continuous microfluidic systems due to reagent economy, precision, potential for scalability, and independent fluid actuation. However, scalability within DMF systems is currently inhibited by the DMF sensing architectures that are presently used, being capacitance and resistance sensing. These electrical-based sensing architectures probe each microdroplet location and this is difficult to scale. In this work, a fibre-optic sensing architecture is developed to improve scalability and achieve independent sensing of microdroplets. The sensing architecture utilizes an m × n (column and row) perpendicular overlap grid structure of embedded fibre-optic cables that yields m × n sensing positions with m + n measurement points. To evaluate both localized and practical scalability of the system, actuation contact time and differentiation of multiple microdroplets are assessed. The embedded fibre-optic cables will distribute light proportional to the number of microdroplets in contact along the column or row. Differentiation of multiple microdroplets is assessed with a theoretical model and through experimental measurements. The DMF sensing architecture is demonstrated for a three by three grid with multiple microdroplets present. The results show compatibility with high-speed DMF operation (due to fast contact times) and demonstrate scalable sensing of multiple microdroplets.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".