Interfacial Tension of Complex Fluids with Micro uidics and Machine Learning

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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.