Machine-Learning Approach in Prediction of the Wettability of a Surface Textured with Microscale Pillars
Tuning the wettability of a flat surface by introducing an array of microscale pillars finds wide applications, especially in engineering a superhydrophobic surface. The wettability of such a pillared surface is quantified by the contact angle (CA) of a water droplet. It is desired to know the CA pr...
| Veröffentlicht in: | Langmuir : the ACS journal of surfaces and colloids. - 1992. - 39(2023), 48 vom: 05. Dez., Seite 17471-17479 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | Langmuir : the ACS journal of surfaces and colloids |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Tuning the wettability of a flat surface by introducing an array of microscale pillars finds wide applications, especially in engineering a superhydrophobic surface. The wettability of such a pillared surface is quantified by the contact angle (CA) of a water droplet. It is desired to know the CA prior to construction of pillars, in order to obviate the trial-and-errors in experimenting with many different topographies. Given an accurate theoretical prediction of CA has been elusive, we propose a convolutional neural network (CNN) model of CA for a surface patterned with rectangular or cylindrical pillars. By employing a three-dimensional descriptor of the surface topography, the present CNN model can predict experimental CAs within errors comparable to the uncertainties in measuring CAs |
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| Beschreibung: | Date Revised 05.12.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1520-5827 |
| DOI: | 10.1021/acs.langmuir.3c02688 |