Experimental Study of Gas Microbubbles on Oil‐Infused Wrinkled Surfaces
Bibliographic record
Abstract
Abstract Lubricant‐infused surfaces (LIS) have been shown to reduce hydrodynamic drag to a greater extent than theoretically expected, making them attractive candidates for microfluidic applications. The presence of nano‐ and micro‐bubbles has been found to explain this property, but this observation is not widely acknowledged. This work investigated how the volume and distribution of lubricant in wrinkled Teflon LIS affects bubble durability. The lubricant is depleted from LIS by repeated immersion through an air–water interface, as well as by shearing, gravity drainage and spreading. The bubbles are imaged using confocal fluorescence microscopy at different levels of infused lubricant. The lubricant encasing the bubbles on LIS prevented bubbles from shrinking over several hours, compared to uninfused superhydrophobic Teflon wrinkles, in which bubbles more rapidly shrunk in height, typically within 30 min. The size of bubbles is independent of lubricant volume, likely due to lubricant redistribution underwater. These findings point toward the possibility of a short‐term stabilization of bubbles on structured surfaces for drag reduction applications through the use of lubricant.
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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".