Predicting Microfluidic Droplet Diameters in Glass Capillary Devices Using Machine Learning
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I have successfully generated a graphic user interface that predicts microfluidic droplet diameters from a neural network. The neural network inputs are fluid properties and geometries of 3D glass capillary devices. For water-in-oil single emulsions, the mean-squared error at the end of 100 epochs for training and validation converged to 7.2% and 7.4%, respectively. The deep machine learning model provides an alternative method of predicting droplet size without the need for rigorous theory. Moreover, the model can be altered to predict other microfluidic parameters or properties and could be extended to other fluids as well.
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A thesis [actually a Plan B] submitted to the faculty of the Graduate School of the University of Minnesota by Serena Holte in partial fulfillment of the requirements for the degree of Master of Science, June 2023.
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Holte, Serena. (2023). Predicting Microfluidic Droplet Diameters in Glass Capillary Devices Using Machine Learning. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/254804.
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