Theses and Dissertations

ORCID

https://orcid.org/0009-0008-0192-3671

Advisor

Knizley, Alta

Committee Member

Hwang, Joonsik

Committee Member

Berry, Gentry

Date of Degree

8-13-2024

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Mechanical Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Mechanical Engineering

Abstract

Through enhancing aerosol particle measurement accuracy by determining particle shape factors using Atomic Force Microscopy (AFM) combined with machine learning techniques, this study aims to provide a methodology that will improve the precision of aerosol measurements and contribute to the development of more effective filtration technologies. Accurate shape factor measurement is crucial for devices such as the Scanning Mobility Particle Sizer (SMPS), which often assume spherical particles of uniform density. By identifying and analyzing particles in AFM scans using machine learning techniques, this research provides a better understanding of shape factors, improving the quality of aerosol measurements. These advancements contribute to a deeper understanding of aerosol properties and their impact on filtration systems, aiding in the development of more effective filtration technologies and improving our capability to measure and control particulate matter in various environmental and industrial applications.

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