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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".