莲花效应
表面粗糙度
莲花
材料科学
表面光洁度
曲面(拓扑)
固体表面
纳米技术
粘附
比例(比率)
计算机科学
人工智能
生物系统
数学
复合材料
几何学
化学
物理
化学物理
有机化学
原材料
生物
量子力学
植物
作者
Xiao He,Kaihua Zhang,Xianghui Xiong,Yuepeng Li,Xizi Wan,Zijia Chen,Yixuan Wang,Xuetao Xu,Mingqian Liu,Ying Jiang,Shutao Wang
出处
期刊:Small
[Wiley]
日期:2022-09-07
卷期号:18 (41)
被引量:3
标识
DOI:10.1002/smll.202203264
摘要
Abstract Superhydrophobic surfaces with the “lotus effect” have wide applications in daily life and industry, such as self‐cleaning, anti‐freezing, and anti‐corrosion. However, it is difficult to reliably predict whether a designed superhydrophobic surface has the “lotus effect” by traditional theoretical models due to complex surface topographies. Here, a reliable machine learning (ML) model to accurately predict the “lotus effect” of solid surfaces by designing a set of descriptors about nano‐scale roughness and micro‐scale topographies in addition to the surface hydrophobic modification is demonstrated. Geometrical and mathematical descriptors combined with gray level cooccurrence matrices (GLCM) offer a feasible solution to the puzzle of accurate descriptions of complex topographies. Furthermore, the “black box” is opened by feature importance and Shapley‐additive‐explanations (SHAP) analysis to extract waterdrop adhesion trends on superhydrophobic surfaces. The accurate prediction on as‐fabricated superhydrophobic surfaces strongly affirms the extensionality of the ML model. This approach can be easily generalized to screen solid surfaces with other properties.
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