A comparison of bioinspired slippery and superhydrophobic surfaces: Micro-droplet impact
Bibliographic record
Abstract
Slippery lubricant impregnated surfaces (SLIPSs/LISs) exhibit remarkable features of repellency and droplet mobility to a broad range of complex fluids. Their performance in micro-droplet repellency has received less attention. In this study, the anti-wetting performance of SLIPSs in comparison to superhydrophobic surfaces (SHSs) is investigated for the micro-droplet impact on different textured surfaces. Different series of square-pillar arrays are modeled to consider the effect of surface morphology on droplet hydrodynamics. A multiphase numerical model in conjunction with an accurate contact angle method has been implemented to analyze details of three immiscible phases during the droplet impact on the SLIPS. Our findings revealed that on the SLIPS with a low-density micro-textured surface where the effect of lubricant is more significant, droplet repellency and mobility are improved compared to SHSs. It was illustrated that on the SLIPS, droplet pinning decreased significantly and in low Weber number cases where the effect of lubricant is more noticeable, partial bouncing occurred. It was also observed that slippery surfaces with a low-density of micro-pillars exhibit bouncing behavior, which indicated the repellency effect of lubricant in droplet hydrodynamics. Although micro-droplets failed to recoil at a higher Weber number (We≃160) on both the SHS and the SLIPS, droplet penetration within the micro-structured surface was considerably smaller on the SLIPS.
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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".