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Statistical Heuristic Wettability Analysis of Randomly Textured Surfaces
Md Arifur Rahman Khandoker1, Kevin Golovin1
1Okanagan Polymer Engineering Research & Applications Laboratory, School of Engineering, University of British Columbia, Kelowna, BC V1V 1V7, Canada.
Langmuir : the ACS Journal of Surfaces and Colloids
|November 18, 2020
Summary
Researchers explored liquid repellency on randomly rough surfaces, finding that statistical methods better predict water contact angles than non-statistical ones. However, current theories struggle with mixed wetting states and stability predictions on these complex surfaces.
Area of Science:
- Surface science
- Materials science
- Fluid dynamics
Background:
- Liquid repellency relies on air trapped in surface roughness.
- Superhydrophobicity studies predominantly use idealized, regular textures.
- Randomly textured surfaces, common in nature and applications, are less understood.
Purpose of the Study:
- To assess the applicability of existing wettability theories for regular surfaces to randomly rough ones.
- To compare statistical and non-statistical approaches for characterizing random surface textures.
- To evaluate the predictive power of these methods for apparent contact angles and wetting state stability.
Main Methods:
- Hydrophobized sandpapers of varying grit sizes modeled random rough surfaces.
- Non-statistical approach: Texture size/spacing derived from direct surface imaging (particle analysis, Delaunay triangulation).
- Statistical approach: Texture size/spacing determined using sample autocorrelation length and mean periodicity.
Main Results:
- The statistical approach generally predicted water contact angles more accurately than the non-statistical method.
- Both methods showed limitations in predicting apparent contact angles for mixed Cassie wetting states.
- Estimates of nonwetted state pressure stability were underpredicted using statistical parameters.
Conclusions:
- Current wettability theories are not directly applicable to randomly rough surfaces with varied texture distributions via simple statistical parameter mapping.
- Further research is needed to adapt theories for complex, real-world surface topographies.
- Understanding and predicting wetting behavior on random textures remains a significant challenge.
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