Interfacial Tension of Complex Fluids with Micro uidics and Machine Learning

界面张力是一种对自然界和工业中的许多系统都极其重要的现象。界面张力的影响存在于日常生活中的食品、家居、化妆品和化学品等领域。界面有力学和热力学定义。世纪以来，各种技术已被用于测量不混溶流体相之间的界面张力。本项目所研究的技术包括悬滴法和泰勒图法。泰勒阴谋以美国大兵命名Taylor是一种微流体技术，用于测量两种不混溶流体的界面张力。该技术检查液滴在接近微流体收缩时的变形。深度学习是一组在监督学习下的机器学习算法，几乎总是具有神经网络（NN）的形式。第一个深度学习算法是在几十年前建立的，随着与算法相关的问题的克服，最近越来越受欢迎。卷积神经网络（CNN）用于各种复杂的计算机视觉应用。CNN的最大优点是它们不受空间限制。这种空间不变性是由于池化层只传递重要信息。这在两个方面对该项目很重要：第一个是图像中存在的液滴可以在沿着收缩的不同点被捕获。第二个可能更重要的原因是液滴的变形模式将是移动的。该研究项目的目的是将微流体学和机器学习联合收割机结合起来，创建一个快速、连续运行的系统，以精确测量软物质体系的界面张力。然后，该技术将用于检查软物质系统的静态和动态界面张力。

Interfacial tension is a phenomenon that is extremely important to many systems in both nature and industry. The effects of interfacial tension are present in everyday life with products in the food, household, cosmetic and chemical sectors, among many more. Interfacial has a mechanical and thermodynamic definition. For more than a century, a variety of techniques have been used to measure interfacial tensions between immiscible fluid phases. The technqiues examined for this project include the pendant drop method and the Taylor plot method. The Taylor plot, named after G.I. Taylor, is a microfluidic technique to measure the interfacial tension of two immiscible fluids. The technique examines the deformation of droplets as they approach a microfluidic constriction.Deep learning is a group of machine learning algorithms under supervised learning that almost always have the form of neural networks (NN's). The first deep learning algorithms were established decades ago, with a recent increase in popularity as problems associated with the algorithms are overcome.Convolutional neural networks (CNN's) are used in a wide variety of complex computer vision applications. The greatest advantage of CNN's is that they are not spatially constrained. This spatial invariance is due to the pooling layers passing only the important information on. This is important for this project in two ways: the first being the fact that the droplets present in images can be captured at different points along the constriction. The second, and potentially more important reason is that deformation patterns of the droplets will be mobile.The aim of this research project is to combine microfluidics and machine learning to create a rapid, continuously operated system to accurately measure the interfacial tension of soft matter systems. This technique will then be used to examine the static and dynamic interfacial tension of soft matter systems.