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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1021/acs.langmuir.3c02688
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
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|a Choi, Seyong
|e verfasserin
|4 aut
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| 245 |
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|a Machine-Learning Approach in Prediction of the Wettability of a Surface Textured with Microscale Pillars
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 05.12.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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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|a Journal Article
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|a Kim, Kiduk
|e verfasserin
|4 aut
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| 700 |
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|a Byun, Kisang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jang, Joonkyung
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Langmuir : the ACS journal of surfaces and colloids
|d 1992
|g 39(2023), 48 vom: 05. Dez., Seite 17471-17479
|w (DE-627)NLM098181009
|x 1520-5827
|7 nnns
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| 773 |
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|g volume:39
|g year:2023
|g number:48
|g day:05
|g month:12
|g pages:17471-17479
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|u http://dx.doi.org/10.1021/acs.langmuir.3c02688
|3 Volltext
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