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Updated: Jun 18, 2025

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使用机器学习进行准确和强大的静态疏水接触角度测量

Daniel G Shaw¹, Ran Liang², Tian Zheng³

¹Department of Chemical Engineering, University of Melbourne, Parkville, 3010 Victoria, Australia.

Langmuir : the ACS journal of surfaces and colloids

|July 30, 2024

概括

一个新的机器学习 (ML) 模型,Conan-ML,准确地测量静态接触角度 (>110°) 以最小的误差. 这种人工智能方法可显著加速高通量应用的接触角度分析.

科学领域:

材料科学材料科学材料科学
表面科学是一门学科.
计算科学计算科学

背景情况:

静态接触角度测量对于表征表面特性至关重要.
目前的方法,如-拉普拉斯合适是准确的,但耗时.
现有的技术可能容易出现人为错误,并且在处理复杂的表面条件时存在局限性.

研究的目的:

开发一种快速准确的机器学习模型,用于静态接触角度测量.
为了克服传统测法方法的局限性.
为高通量表面分析提供一个开源工具.

主要方法:

在超过720万个半滴轮上训练了一种机器学习模型,该轮来自于 - 拉普拉斯方程的解决方案.
纳入诸如表面粗度,重力,掉落大小和反射等因素到训练数据中.
开发了一个自动化图像和轮处理管道.

主要成果:

ML模型,Conan-ML,在接触角度>110°时,估计误差为1°.
康南-ML比扬-拉普拉斯适配速度快两倍.
与实验数据集上的现有方法相比,证明了更高的准确性.

结论:

康南-ML方法为高通量接触角分析提供了一种强大且可重复的方法.
这种开源工具有助于促进测和表面表征的进步.
这种ML模型的速度和准确性为新的研究和工业应用铺平了道路.

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