Effect of Surfactant on Interfacial Gas Transfer Studied by Axisymmetric Drop Shape Analysis−Captive Bubble (ADSA-CB)
Bibliographic record
Abstract
A new method combining axisymmetric drop shape analysis (ADSA) and a captive bubble (CB) is proposed to study the effect of surfactant on interfacial gas transfer. In this method, gas transfer from a static CB to the surrounding quiescent liquid is continuously recorded for a short period (i.e., 5 min). By photographical analysis, ADSA-CB is capable of yielding detailed information pertinent to the surface tension and geometry of the CB, e.g., bubble area, volume, curvature at the apex, and the contact radius and height of the bubble. A steady-state mass transfer model is established to evaluate the mass transfer coefficient on the basis of the output of ADSA-CB. In this way, we are able to develop a working prototype capable of simultaneously measuring dynamic surface tension and interfacial gas transfer. Other advantages of this method are that it allows for the study of very low surface tensions (<5 mJ/m2) and does not require equilibrium of gas transfer. Consequently, reproducible experimental results can be obtained in a relatively short time. As a demonstration, this method was used to study the effect of lung surfactant on oxygen transfer. It was found that the adsorbed lung surfactant film shows a retardation effect on oxygen transfer, similar to the behavior of a pure DPPC film. However, this retardation effect at low surface tensions is less than that of a pure DPPC film.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".