Author ORCID Identifier


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Document Type


Degree Name

Master of Science


Chemical and Life Science Engineering

First Advisor

James K Ferri


Axisymmetric Drop Shape Analysis (ADSA) is a technique commonly used to determine surface or interfacial tension. Applications of traditional ASDA methods to process analytical technologies are limited by computational speed and image quality. Here, we address these limitations using a novel machine learning approach to analysis. With a convolutional neural network (CNN), we were able to achieve an experimental fit precision of (+/-) 0.122 mN/m in predicting the surface tension of drop images at a rate of 1.5 ms^-1 versus 7.7 s^-1, which is more than 5,000 times faster than the traditional method. The results are validated on real images of pendant drops. The same CNN model has an upper bound on the error of 0.690 mN/m in predicting the surface tension of experimental images with challenging features, including misalignment and poor focus. Additionally, other important drop properties are also accurately predicted (Volume, Surface Area) with high precision. In addition, a novel analytical solution to the Young-Laplace equation for an axisymmetric pendant drop using a perturbation approach is presented. The solution provides a geometric basis for understanding the shape of a pendant drop in terms of elliptic integrals. In addition, a correction is found that improves the solution accuracy.


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