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相关概念视频

Contact Angle01:13

Contact Angle

12.3K
When a solid is dipped inside a liquid, the liquid surface becomes curved near the contact. For some solid–liquid interfaces, the liquid is pulled up along the solid, while for others, the liquid surface is convex or depressed near the solid surface. This phenomenon can be explained using the concept of cohesive and adhesive forces.
The adhesive force is the molecular force between molecules of different materials, that is, between the molecules of the solid and the liquid. The cohesive...
12.3K

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相关实验视频

Updated: Jun 18, 2025

Measuring the Interaction Force Between a Droplet and a Super-hydrophobic Substrate by the Optical Lever Method
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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
PubMed
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.

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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.