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Enhancing AFM Particle Analysis and ...

McKelvey, William David,

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Enhancing AFM Particle Analysis and Shape Factor Identification With Machine Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing AFM Particle Analysis and Shape Factor Identification With Machine Learning // William David McKelvey.
Author:	McKelvey, William David,
Description:	1 electronic resource (76 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-02.
Contained By:	Masters Abstracts International86-02.
Subject:	Mechanical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31485391
ISBN:	9798383702680

Enhancing AFM Particle Analysis and Shape Factor Identification With Machine Learning /
McKelvey, William David,

Enhancing AFM Particle Analysis and Shape Factor Identification With Machine Learning /William David McKelvey. - 1 electronic resource (76 pages)

Source: Masters Abstracts International, Volume: 86-02.

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.

English

ISBN: 9798383702680Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Aerosols

Enhancing AFM Particle Analysis and Shape Factor Identification With Machine Learning /
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020 $a 9798383702680
035 $a (MiAaPQD)AAI31485391
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040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a McKelvey, William David, $e author. $0 (orcid)0009-0008-0192-3671 $3 3830268
245 1 0 $a Enhancing AFM Particle Analysis and Shape Factor Identification With Machine Learning / $c William David McKelvey.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (76 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 86-02.
500 $a Advisors: Knizley, Alta Committee members: Berry, Gentry; Hwang, Joonsik.
502 $b M.S. $c Mississippi State University $d 2024.
520 $a 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.
546 $a English
590 $a School code: 0132
650 4 $a Mechanical engineering. $3 649730
650 4 $a Aeronomy. $3 2102064
650 4 $a Analytical chemistry. $3 3168300
653 $a Aerosols
653 $a Atomic force microscopy
653 $a Machine learning
653 $a Microscopy
690 $a 0548
690 $a 0800
690 $a 0367
690 $a 0486
710 2 $a Mississippi State University. $b Department of Mechanical Engineering. $e degree granting institution. $3 3830269
720 1 $a Knizley, Alta $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-02.
790 $a 0132
791 $a M.S.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31485391