The most stable state of a droplet on anisotropic patterns: support for a missing link
Bibliographic record
Abstract
Surface tension and capillary forces were measured for water droplets in contact with anisotropic hydrophobic patterns made of microscopic ridges and grooves using a microbalance. Integrated with a charge-coupled device camera, the instrument allowed capturing of the synchronous images of a droplet during its spreading, compression, stretching and detachment. These images were used to analyze the evolution of the droplet shape and quantify its base diameter and contact angle in both the longitudinal and traverse directions. The experiments confirmed that a water droplet spreads preferentially along the longitudinal direction, on top of the ridges, following the continuity of the solid and producing asymmetry in the drop shape. Switching the droplet wetting mode from advancing to receding causes the droplet to symmetrize its shape. It was found that the maximum adhesion between the droplet and hydrophobic pattern coincides with droplet base circularity and apparent contact angles of nearly identical values measured in the longitudinal and traverse directions. These findings confirm that the most stable configuration for a liquid droplet on a rough solid surface appears only when the droplet base is axisymmetric. It is also demonstrated that the Cassie–Baxter equation pertains only to the droplet in the most stable state, where the excess free energy is minimized.
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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".