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Just as humans communicate with other humans, the cells in our bodies communicate with each other through various, often complex, mechanisms. Cell-to-cell transmission of small molecules, lipids, proteins, peptides, or nucleic acids can be mediated by extracellular lipid vesicles called exosomes. Exosomes have been found to play a role in the delivery of regulatory molecules from one cell to another, serving as a universal communication mechanism. Currently, there is an emerging focus on characterizing exosome communication dynamics. Understanding exosome mechanisms of cell-to-cell communication requires accurate measurements of the spatiotemporal and chemical dynamics of exosome secretion. No current analytical approach offers the appropriate combination of spatial, temporal, and chemical resolutions needed to understand the dynamics of exosome communication at the tissue or organ level. The research outlined in this dissertation aims to bridge the gap between bulk fluid analysis of exosome cargo and single cell visualization of individual exosomes at a single point in time. To achieve better understanding of the dynamics of exosome secretion, there is a critical need for new analytical technologies. Without meeting this need, the roles of exosome communication in human health cannot be thoroughly understood. The long-term goal of this research is to develop tools to characterize communication between cellular networks, and model mechanisms of disrupted pathways with significance to neurological disorders. The primary objective is to develop an assay for characterizing temporal and chemical dynamics of exosome secretion.

Particle characterization is important to the aerospace field because particle ingestion in propulsion engines can lead to catastrophic failures. It has been shown laser based methodologies can determine size and concentration of spherical particles by using light extinction. However, when one moves to increasingly complex shapes one must take into consideration not only light extinction but multi angle light scattering. Cylindrical particles scatter light in a way that can be quantified by electromagnetic wave theory. This scattering distribution is directly related to the cylinders diameter and material properties, as well as the wavelength of the incident light. This project designed and implemented a rig that measures the scattering distribution of single static cylindrical particles. It was shown that the scattering distribution for cylinders can be measured and compared to computational expected values, especially in the forward scattering region. Future work in measuring the scattering distribution of increasingly complex geometries and in flow conditions is proposed.

This thesis presents an inductive method of detecting, roughly sizing and differentiating debris particles in lubrication oil. In the proposed approach, a sensing coil, with associated electronics, detects the passage of metal debris particles. Metal debris particles cause perturbations in the inductance and series resistance values of the sensing coil, which in turn affect the output voltage of the associated electronics. The sensing coil is connected to a Maxwell-Wien bridge that produces a change in differential voltage based upon a change in the inductance of the sensing coil. The bridge is intentionally imbalanced, such that a ferrous particle increases the differential voltage and a non-ferrous particle decreases the differential voltage. The signal is filtered, amplified and rectified to produce a DC varying output signal. Debris particles passing through the sensing coil produce pulses in the output signal. The height of the pulse can be measured to calculate the diameter of the individual debris particle. The debris sensor is shown experimentally to detect the presence and the approximate diameter of irregularly shaped iron (ferrous) particles 75 µm and 150 µm in mean diameter and copper (non-ferrous) particles 120 µm and 150 µm in mean diameter.

The first aim of this work was to integrate, validate, and document, a digital particle image quantification (Flow-IQ) software package developed in conjunction with and supported by Aeroprobe Corporation. The system is tailored towards experimental fluid mechanics applications. The second aim of this work was to test the performance of DPIV algorithms in wall shear flows, and to test the performance of several particle sizing algorithms for use in spray sizing and average diameter calculation. Several particle sizing algorithms which assume a circular particle profile were tested with DPIV data on spray atomization, including three point Guassian, four point Gaussian, and least squares algorithms. A novel elliptical diameter estimation scheme was developed which does not limit the measurement to circular patterns. The elliptic estimator developed in this work is able to estimate the diameter of a particle with an elliptic shape, and assumes that the particle is axisymmetric about the x or y axis. Two elliptical schemes, the true and averaged elliptical estimators, were developed and compared to the traditional three point Gaussian diameter estimator using theoretical models. If elliptical particles are theoretically used, the elliptical sizing schemes perform drastically better than the traditional scheme, which is limited to diameter measurements in the x-direction. The error of the traditional method in determining the volume of an elliptical particle increases dramatically with the eccentricity. Monte Carlo Simulations were also used to characterize the error associated with wall shear measurements using DPIV. Couette flow artificial images were generated with various shear rates at the wall. DPIV analysis was performed on these images using PIV algorithms developed by other researchers, including the traditional multigrid method, a dynamically-adaptive DPIV scheme, and a control set with no discrete window offset. The error at the wall was calculated for each data set. The dynamically adaptive scheme was found to estimate the velocity near the wall with less error than the no discrete window offset and traditional multigrid algorithms. The shear rate was found to be the main factor in the error in the velocity measurement. In wall shear velocity measurement, the mean (bias) error was an order of magnitude greater than the RMS (random) error. A least squares scheme was used to correct for this bias error with favorable results. The major contribution of this effort stems from providing a novel elliptical particle sizing scheme for use in DPIV, and quantifies the error associated with wall shear measurements using several DPIV algorithms. A test bed and comprehensive user's manual for Flow-IQ v2.2 was also developed in this work.