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Superhydrophobic Polymer Topography Design Assisted by Machine Learning Algorithms
Qiang Wang1, Jarrett J Dumond2, Jarren Teo3
1Digital Manufacturing and Design Centre (DManD), Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore.
ACS Applied Materials & Interfaces
|June 15, 2021
Summary
Researchers developed a hybrid approach using machine learning to design superhydrophobic surfaces. This method creates design maps for optimal surface textures, enhancing water contact angle and Laplace pressure for specific applications.
Area of Science:
- Materials Science
- Surface Engineering
- Computational Modeling
Background:
- Superhydrophobic surfaces are typically created using varied surface topographies.
- Bioinspired and biomimetic designs have advanced superhydrophobic surface development.
- Optimizing surface texture for specific applications remains challenging.
Purpose of the Study:
- To develop design maps for superhydrophobic polymer topographies using a hybrid approach.
- To investigate the relationship between topographic parameters and superhydrophobic properties.
- To utilize machine learning for rapid exploration of surface design parameters.
Main Methods:
- Experimental validation combined with numerical simulations (Finite Element Method).
- Generation of a labeled dataset for machine learning (ML) training.
- Development of Artificial Neural Network (ANN) models to predict water contact angle (WCA) and Laplace pressure based on topographic parameters (width, height, pitch).
Main Results:
- ANN models revealed nonlinear relationships between topographic parameters and superhydrophobic properties (WCA, Laplace pressure).
- Significant differences in performance were observed between micrometer and sub-micrometer length scales.
- Generated design maps provide optimal or tradeoff parameters for superhydrophobic surface design.
Conclusions:
- The hybrid approach integrating experimental, simulation, and ML methods is effective for designing superhydrophobic surfaces.
- Artificial Neural Networks show significant potential as rapid design tools for exploring surface topography.
- This research facilitates the tailored design of superhydrophobic surfaces for diverse applications.
