Effects of surface roughness on liquid bridge capillarity and droplet wetting
Bibliographic record
Abstract
This study examines the influence of surface roughness on the capillarity of liquid bridges between two solid surfaces and the wettability of droplets on solid surfaces. For this purpose, different surface roughnesses were prepared by gluing waterproof sandpaper onto flat glass surfaces. The effects of surface roughness on liquid bridge capillarity were investigated by using a method that is based on an exact analytical solution of the Young-Laplace equation for a liquid bridge between two parallel planes coupled with capillary force measurement. The capillary forces between two parallel planes were measured by a micro-balance, while the geometries of the capillary bridge were recorded using a high-resolution camera. Using the images of capillary bridges, the meridional profiles of capillary bridges were determined by a high-resolution image processing technique and correlated to the measured capillary forces. The effects of surface roughness on droplet wetting were measured by employing the same high-resolution image processing technique. The measured results show that as the roughness increases, the wetting angle increases, whereas the capillary forces of the liquid bridge decrease.
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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".