Superhydrophobic Polymer Topography Design Assisted by Machine Learning Algorithms
| Autor: | Jarren Teo, Jarrett J. Dumond, Hong Yee Low, Qiang Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Optimal design
Materials science Artificial neural network business.industry Design tool 02 engineering and technology Surface finish 010402 general chemistry 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Finite element method 0104 chemical sciences Micrometre Design objective General Materials Science Laplace pressure Artificial intelligence 0210 nano-technology business Algorithm computer |
| Zdroj: | ACS Applied Materials & Interfaces. 13:30155-30164 |
| ISSN: | 1944-8252 1944-8244 |
| DOI: | 10.1021/acsami.1c04473 |
| Popis: | Superhydrophobic surfaces have been largely achieved through various surface topographies. Both empirical and numerical simulations have been reported to help understand and design superhydrophobic surfaces. Many such successful surfaces have also been achieved using bioinspired and biomimetic designs. Despite this, identifying the right surface texture to meet the requirements of specific applications is not a straightforward task. Here, we report a hybrid approach that includes experimental methods, numerical simulations, and machine learning (ML) algorithms to create design maps for superhydrophobic polymer topographies. Two design objectives to investigate superhydrophobic properties were the maximum water contact angle (WCA) and Laplace pressure. The design parameters were the geometries of an isotropic pillar structure in micrometer and sub-micrometer length scales. The finite element method (FEM) was validated by the experimental data and employed to generate a labeled dataset for ML training. Artificial neural network (ANN) models were then trained on the labeled database for the topographic parameters (width W, height H, and pitch P) with the corresponding WCA and Laplace pressure. The ANN models yielded a series of nonlinear relationships between the topographic design parameters and the WCA and Laplace pressure and substantial differences between the micrometer and sub-micrometer length scales. Design maps that span the topography design parameters provide optimal design or tradeoff parameters. This research demonstrates the potential of ANN as a rapid design tool for surface topography exploration. |
| Databáze: | OpenAIRE |
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