Surfactant-laden drop behavior in pore space
Bibliographic record
Abstract
We numerically study the deformation and breakup of a surfactant-laden liquid drop immersed in another immiscible liquid flowing through a single pore with curvilinear boundaries using a conservative phase-field lattice Boltzmann method. Our results show that, compared to pure drops, surfactant-laden drops are prone to break, generating small satellite drops due to a non-uniform distribution of surfactant at the drop interface and a decrease in interfacial tension. As the surfactant concentration increases, it becomes increasingly challenging to maintain the stability of the drop, as higher surfactant concentrations result in a lower interfacial tension, thereby enhancing drop breakage. To provide a guideline on drop breakup conditions when it moves through a curved pore space, we present a map of the Weber number (We) vs the Reynolds number (Re), outlining the critical boundary beyond which drops break for surfactant-laden drops (at dimensionless bulk concentrations ψb=0.1 and 0.2) at Re ranging from 0.26 to 2.51. We theoretically explain this critical relationship for drop breakage by balancing the shear force and the surface tension force acting on the drop. We further investigate the combined effect of the viscosity ratio and channel confinement ratio (defined as the ratio between the channel depth and drop diameter) on drop breakup. We find that less viscous drops in a more confined channel are prone to breakage. The channel confinement ratio has a dominant effect on drop breakage since viscous drops with a high surfactant load do not break when the channel is not confined.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".