Hierarchical or Not? Effect of the Length Scale and\nHierarchy of the Surface Roughness on Omniphobicity of Lubricant-Infused\nSubstrates
Bibliographic record
Abstract
Lubricant-infused textured solid\nsubstrates are gaining remarkable\ninterest as a new class of omni-repellent nonfouling materials and\nsurface coatings. We investigated the effect of the length scale and\nhierarchy of the surface topography of the underlying substrates on\ntheir ability to retain the lubricant under high shear conditions,\nwhich is important for maintaining nonwetting properties under application-relevant\nconditions. By comparing the lubricant loss, contact angle hysteresis,\nand sliding angles for water and ethanol droplets on flat, microscale,\nnanoscale, and hierarchically textured surfaces subjected to various\nspinning rates (from 100 to 10 000 rpm), we show that lubricant-infused\ntextured surfaces with uniform nanofeatures provide the most shear-tolerant\nliquid-repellent behavior, unlike lotus leaf-inspired superhydrophobic\nsurfaces, which generally favor hierarchical structures for improved\npressure stability and low contact angle hysteresis. On the basis\nof these findings, we present generalized, low-cost, and scalable\nmethods to manufacture uniform or regionally patterned nanotextured\ncoatings on arbitrary materials and complex shapes. After functionalization\nand lubrication, these coatings show robust, shear-tolerant omniphobic\nbehavior, transparency, and nonfouling properties against highly contaminating\nmedia.
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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 | 0.003 |
| 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) | 0.008 | 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".