Multiple droplets on a conical fiber: formation, motion, and droplet mergers
Bibliographic record
Abstract
Small droplets on slender conical fibers spontaneously move along the fiber due to capillary action. The droplet motion depends on the geometry of the cone, the surface wettability, the surface tension, the viscosity, and the droplet size. Here we study with experiments and numerical simulations, the formation, spontaneous motion, and the eventual merger, of multiple droplets on slender conical fibers as they interact with each other. The droplet size and their spacing on the fibre is controlled by the Plateau-Rayleigh instability after dip-coating the conical fiber. Once these droplets are formed on the fiber, they spontaneously start to move. Since droplets of different size move with different speeds, they effectively coarsen the droplet patterning by merging on the fiber. The droplet merging process affects locally the droplet speed and alters the spatiotemporal film deposition on the fiber.
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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 | 0.000 | 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".