Peclet Number Dependence of Mass Transfer in Microscale Segmented Gas–Liquid Flow
Bibliographic record
Abstract
A detailed understanding of the scaling behavior associated with the fluid flow and the transport of gas molecules from a train of elongated gas plugs into neighboring liquid segments is of great importance for a broad range of microscale applications. The indirect dependence of the parameters affecting the Capillary and Peclet numbers and thereby scaling behavior (i.e., the velocity and length of the gas plugs, and the length of the liquid segments) on the directly adjustable experimental inputs (i.e., flow rate or pressure of each phase) has hindered the systematic investigation of scaling behavior in microscale gas–liquid flows. Here, we take advantage of an image-based feedback strategy that allows us to directly impose Capillary and Peclet numbers. We custom fabricated a long, straight microchannel (width 300 μm, length-to-width ratio 700) in a gas impermeable silicon–glass substrate. We automatically determined the length reduction of initially uniformly sized gas plugs at different positions along the microchannel and elucidated the gas concentration within adjacent liquid segments. In accordance with penetration theory, we analytically estimated the gas–liquid mass transfer time to scale with the Peclet number, Pe, to the power of −0.5. The experimentally measured scaling exponent −0.55 ± 0.5 for carbon dioxide dissolution in methanol and ethanol at Pe = 2060–16500 compared favorably with the analytical prediction and provides a guideline for predicting physical transport for a wide range microscale gas–liquid flow processes.
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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 | 0.000 | 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) | 0.001 | 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".