Molecular Tagging in Microchannels With Microbubble Lensing-Induced Photobleaching (µ-BLIP)
Bibliographic record
Abstract
The curvature of gas-liquid interfaces and the step change in properties across these interfaces in microchannels are shown here to create a powerful lens/mirror effect. In a hydrophilic system, light incident on the bubble is focused into the surrounding liquid resulting in a locally increased total light exposure. The optical phenomena leading to this are discussed and the effect is demonstrated experimentally by imaging the increased photobleaching rate of fluorophores in the near-bubble region. Numerical simulations of the system are performed to investigate the electrical potential and flow fields resulting from the application of an axial electric field. Microbubble Lensing-Induced Photobleaching (μ-BLIP) is then applied as a method of molecular tagging for flow visualization in microchannels. Once formed, the electrokinetic transport of the marker is analyzed to determine the cross-channel velocity profile of the liquid phase, and the liquid velocity in the film. Experimental data is verified by comparison with numerical predictions and previous experimental studies. This contribution represents both a new application of microscale gas-liquid interfacial phenomena, and a new technique for microfluidic flow visualization, particularly applicable (though not limited) to the study of multiphase microchannel flows.
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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 | 0.000 |
| 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 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".