Droplet lift-off from hydrophobic surfaces from impact with soft-hydrogel spheres
Bibliographic record
Abstract
Abstract Droplet impacts on superhydrophobic surfaces may result in complete bouncing, with the absence of contact hysteresis and viscous dissipation leading the droplet to fully rebound off the surface. This rebound usually happens in the retraction phase, when the droplet retracts back after reaching a maximum spread diameter. Here, we present experimental evidence of a bouncing phenomenon where a sessile droplet on a hydrophobic surface bounces off the surface in its spreading phase when a soft deformable hydrogel sphere axisymmetrically impacts the droplet. We term this as ‘Lift-Off’ and propose a simple force balance based on the deformation characteristics of the hydrogel sphere to explain the out-of-plane jump of the droplet during spreading. We observe three different impact regimes, and propose their dependency on a modified elastic ‘Mach’ number ( M a * ) with M a * ≈ 0.1 corresponding to the onset of lift-off. We also report on the unique acoustic signatures of lift-off cases, associated with the capture of air-bubbles through the air-borne retracting droplet rim. These results may have potential applications for drainage and surface cleaning, non-stick surface coating, industrial mixing and plant disease spreading.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".