Interfacial Tension of Complex Fluids with Micro uidics and Machine Learning
Lead Research Organisation:
Imperial College London
Department Name: Chemical Engineering
Abstract
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.
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.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S513635/1 | 01/10/2018 | 30/09/2023 | |||
| 2292574 | Studentship | EP/S513635/1 | 01/10/2018 | 31/03/2022 | Dale Seddon |
| Description | I have (with other members of my research group) studied the interaction between two surfactants sodium dodecyl sulphate (SDS) and dodecyldimethylamine oxide (DDAO) at several different ratios. These surfactant solutions are categorised as complex fluids due to the meso-structures that they create, such as micelles. The interaction was found to be in a synergistic manner, with the resultant surfactant mixtures having a lower Surface tension (SFT) (a special case of interfacial tension (IFT) where one of the fluids is air) than either of the pure surfactant solutions. The most interactive ratio was found to be 50:50. I have been improving and implementing a simple microfluidic design to obtain the IFT of complex fluids using droplet deformation. The degree of deformation relative to the speed of the droplet and the viscosity of the fluids can be used to determine the IFT. This is called the Taylor analysis. This analysis has already been implemented in microfluidics. This will enable to rapid access of IFTs of complex fluids. For example, the interaction between two surfactants can be examined when surrounded by oil instead of air. I have conducted a small angle neutron scattering experiment (SANS) to determine the micellar phase map of SDS and NaCl salt. This will be used to determine the micelle shapes (ranging from ellipses to worm-like micelles). The information could also be potentially used to train a machine learning algorithm (CNN) to predict what phase the micellar solution is in based from the 2D scattering image. |
| Exploitation Route | The interaction of two commonly used surfactants in industry and academia could be important when determining what ratio should be used to generate products (although commercial products have a lot more than 2 surfactants). Research on microfluidic IFT techniques could be expanded and improved in academia and industry. |
| Sectors | Chemicals |