Cruise control for segmented flow
Bibliographic record
Abstract
Capitalizing on the benefits of microscale segmented flows, e.g., enhanced mixing and reduced sample dispersion, so far requires specialist training and accommodating a few experimental inconveniences. For instance, microscale gas-liquid flows in many current setups take at least 10 min to stabilize and iterative manual adjustments are needed to achieve or maintain desired mixing or residence times. Here, we report a cruise control strategy that overcomes these limitations and allows microscale gas-liquid (bubble) and liquid-liquid (droplet) flow conditions to be rapidly "adjusted" and maintained. Using this strategy we consistently establish bubble and droplet flows with dispersed phase (plug) velocities of 5-300 mm s(-1), plug lengths of 0.6-5 mm and continuous phase (slug) lengths of 0.5-3 mm. The mixing times (1-5 s), mass transfer times (33-250 ms) and residence times (3-300 s) can therefore be directly imposed by dynamically controlling the supply of the dispersed and the continuous liquids either from external pumps or from local pressurized reservoirs. In the latter case, no chip-external pumps, liquid-perfused tubes or valves are necessary while unwanted dead volumes are significantly reduced.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".