A scalable digital microfluidic platform for automation of onsite testing of dairy samples
Bibliographic record
Abstract
Antibiotic residues are regulated in commercially produced milk, with elevated concentrations being harmful. Detection of these antibiotic residues in milk pose a significant challenge for supply chain stakeholders due to the industry standard practice of low-interval off-site laboratory testing. This practice poses risk of non-compliant milk going undetected during on-site milk collection. On-site microfluidic technologies with integrated optical sensors are positioned to mitigate this challenge using increased screening intervals. Droplet-based (digital) microfluidic systems show promise to provide highthroughput screening in dairy applications with integrated fluorescence spectroscopy technologies. However, conventional digital microfluidic systems are subject to biofouling from the protein and fat content within milk. In this work, a biofouling-resistant digital microfluidic platform is introduced. The digital microfluidic platform leverages advancements in parafilm layers, and is demonstrated with actuation of milk and water microdroplets. Electrowetting-based microdroplet actuation is achieved via scalable grid arrays of uniplanar printed surface electrodes in open and closed system configurations. For this array technology, a reconfigurable firmware is developed for user control of automated microdroplet actuation at up to three hundred volts using a graphical computer interface. An exposition of the microdroplet actuation performance is demonstrated and assessed through an optical system for closed-open feedback and positioning of microdroplets. This optical closed-loop allows the actuation velocity of microdroplets to be characterized for polydimethylsiloxane and parafilm dielectric layers, for both water and milk as a function of frequency and voltage. Scalability and automation of the microfluidic platform is discussed, and future integration of fluorescence spectroscopy is investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".