Serial siphon valving for centrifugal microfluidic platforms
Bibliographic record
Abstract
Today, the focus in microfluidic platforms for diagnostics is on the integration of several analysis steps toward sample-to-answer systems. One of the main challenges to integration is the requirement for serial valving to allow the sequential release of fluids in a temporally and spatially controlled manner. The advantages offered by centrifugal microfluidic platforms make them excellent candidates for integration of biological analysis steps, yet they are limited by the lack of robust serial valving technologies. This is especially true for the majority of centrifugal microfluidic devices that rely on hydrophilic surfaces, where few passive serial valving techniques function reliably. Building on the useful functionality of centrifugal microfluidic siphoning previously shown, a novel serial siphon valve is introduced that relies on multiple, inline siphons to provide for a better controlled, sequential release of fluids. The introduction of this novel concept is followed by an analytical analysis of the device. Proof-of-concept is also demonstrated, and examples are provided to illustrate the range of functionality of the serial siphon valve. The serial siphon is shown to be robust and reproducible, with variability caused by the dependence on contact angle, rotation velocity, and fluidic properties (viz., surface tension) significantly reduced compared to current microfluidic, centrifugal serial valving technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".