Growth of nanodroplets on a still microfiber under flow conditions
Bibliographic record
Abstract
Surface droplets in the microscale are of great interest for their relevance in broad droplet-based technologies. Derived from the Ouzo effect, the solvent exchange process is a simple bottom-up approach to produce surface nano-/micro-droplets by the nucleation and growth mechanism. The oil oversaturation pulse is created as a good solvent (ethanol) for the oil displaced by a poor solvent (water) in the flow cell. In this work, we investigated the formation of surface droplets on a one-dimensional substrate (a single hydrophobic fiber with a diameter of 10 μm) in a flow. The droplet growth on the microfiber is enhanced as the fiber is perpendicular to the external flow direction, due to the coupled effects between the droplet formation and the local flow. On the other hand, the droplet growth exhibits different growth dynamics when the fiber is placed parallel to the external flow direction. The general trend that surface droplets grow faster on a fiber at higher flow rates is consistent with the situation on planar substrates. The coupled interactions between the growing droplets and the local flow conditions during the solvent exchange process were further revealed in the simulations. The findings from this work will be valuable for the design and utilization of the solvent exchange process to produce surface nanodroplets on a microfiber under flow conditions and thus broaden the droplet-based application fields.
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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) | 0.001 | 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".