Bubbles no more: in-plane trapping and removal of bubbles in microfluidic devices
Bibliographic record
Abstract
Gas bubbles present a frequent challenge to the on-chip investigation and culture of biological cells and small organs. The presence of a single bubble can adversely impair biological function and often viability as it increases the wall shear stress in a liquid-perfused microchannel by at least one order of magnitude. We present a microfluidic strategy for in-plane trapping and removal of gas bubbles with volumes of 0.1-500 nL. The presented bubble trap is compatible with single-layer soft lithography and requires a footprint of less than ten square millimetres. Nitrogen bubbles were consistently removed at a rate of 0.14 μL min(-1). Experiments were complemented with analytical and numerical models to comprehensively characterize bubble removal for liquids with different wetting behaviour. Consistent long-term operation of the bubble trap was demonstrated by removing approximately 4000 bubbles during one day. In a case study, we successfully applied the bubble trap to the on-chip investigation of intact small blood vessels. Scalability of the design was demonstrated by realizing eight parallel traps at a total removal rate of 0.9 μL min(-1) (measured for nitrogen).
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".