Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Mechanical and Aerospace Engineering

Committee Chair

James E Smith

Committee Co-Chair

Patrick Browning

Committee Member

Andrew Lowery

Committee Member

Timothy Nurkiewicz

Committee Member

Edward Sabolsky


Nanoparticles have become of great interest within the scientific community for their use in diverse applications. With this rapidly evolving field, new nano-sized compounds are being developed and used for a variety of reasons. Nanoparticle aerosol technology, aerosolization and dispersion of nano-compounds, has many novel applications and can be used to support current research efforts. This may include anything from drug delivery techniques to industrial processes. To aid these processes, new methods of aerosol generation and dispersion are needed to meet these future needs. This research and development is being conducted to expand upon a novel nanoparticle aerosol generator as a research device, as well as, future applications such as biomedical, pharmaceutical and industrial manufacturing.;This investigation involves the analysis of a fully developed nanoparticle aerosol generation system. It was hypothesized that the particle size and concentration (output) of the device can be varied by precise manipulation of the input parameters. Moreover, the output of a sample compound can be set to a desired value based on a predictive mathematical model constructed experimentally. This hypothesis was tested through the completion of this work. While a fundamental analysis of the aerosol generator represented an important first step, the resulting work demonstrates the valuable use of a controllable nano particle aerosol generation system and a predictive tool to allow the device to operate with a broad range of output characteristics and compounds. This allows the device to be used for multiple and diverse research efforts in the future.;The resulting outcome of this dissertation is a controllable system capable of varying both concentration and mean particle size by precise manipulation of the input parameters. The results demonstrate a single peak distribution with a geometric mean particle size < 200 nm, standard deviation under 2.5 and the ability to hold consistent long term concentrations. Additionally, the predictive model allows the user to predict output values for a given range of input settings with an average prediction accuracy of greater than 92%. The developed model also provides valuable information which include factor effects, optimal settings, factor influence and interactions. This information along with the controllable system provides valuable insights for further development of the technology.