Universal wetting transition of an evaporating water droplet on hydrophobic micro- and nano-structures
Bibliographic record
Abstract
Water-repellent, rough surfaces have a remarkable and beneficial wetting property: when a water droplet comes in contact with a small fraction of the solid, both liquid-solid adhesion and hydrodynamic drag are reduced. As a prominent example from nature, the lotus leaf-comprised of a wax-like material with micro- and nano-scaled roughness-has recently inspired numerous syntheses of superhydrophobic substrates. Due to the diverse applications of superhydrophobicity, much research has been devoted to the fabrication and investigations of hydrophobic micro-structures using established micro-fabrication techniques. However, wetting transitions remain relatively little explored. During evaporation, a water droplet undergoes a wetting transition from a (low-frictional) partial to (adhesive) complete contact with the solid, destroying the superhydrophobicity and the self-cleaning properties of the slippery surface. Here, we experimentally examine the wetting transition of a drying droplet on hydrophobic nano-structures, a previously unexplored regime. In addition, using a theoretical analysis we found a universal criterion of this wetting transition that is characterized by a critical contact angle. Different from previous results showing different critical droplet sizes, our results show a universal, geometrically-dependent, critical contact angle, which agrees well with various data for both hydrophobic micro- and nano-structures.
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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 | 0.001 |
| 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".