Date

2018

Department or Program

Physics

Primary Wellesley Thesis Advisor

Jerome Fung

Abstract

Dynamic light scattering (DLS) is a technique used to determine the size distribution of particles suspended in a fluid, that undergo Brownian motion through random collision with the molecules of the fluid. When light is incident on the suspension, each particle scatters the light, creating a complex interference pattern that randomly fluctuates in time as the particles move. Over long periods of time, we can use these flunctuations in the measured intensity of light to determine the composition of the sample. This thesis develops and tests a Bayesian inference approach to this problem. The Bayesian approach to statistics describes probability as a degree of belief, and incorporates prior information about the system into the inference. Recently, Bayesian inference has been incorporated into other fields with limited data sets, such as astronomy, and other techniques in soft matter, such as holographic microscopy; some preliminary efforts have been made to apply this to dynamic light scattering. This thesis expands on these efforts, and develops an open source algorithm in Python that is able to efficiently and accurately describe the size distribution of particles in a nearly monodisperse suspension with a mono-modal size distribution. This algorithm is tested on several sets of DLS; while these results are promising, further testing will need to be done before a conclusion can be drawn about the precision of this method compared to the traditional least squares fit. The results of this thesis do indicate that Bayesian inference provides comparable results to the traditional least squares fit, with additional information provided about the system in the probability density function for the inferred parameters.

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