Doctoral Dissertations

Abstract

”For decades, Neutron Depth Profiling has been used for the non-destructive analysis and quantification of boron in electronic materials and lithium in lithium ion batteries. NDP is one of the few non-destructive analytical techniques capable of measuring the depth profiles of light elements to depths of several microns with nanometer spatial resolution. The technique, however, is applicable only to a handful of light elements with large neutron absorption cross sections. This work discusses the possibility of coupling Particle Induced X-ray Emission spectroscopy with Neutron Depth Profiling to yield additional information about the depth profiles of other elements within a material. The technical feasibility of developing such a system at the Missouri University of Science and Technology Reactor (MSTR) beam port is discussed.

This work uses a combination of experimental neutron flux measurements with Monte Carlo radiation transport calculations to simulate a proposed NDP-PIXE apparatus at MSTR. In addition, the possibility of implementing an Artificial Neural Network to perform automated data analysis of NDP is presented. It was found that the performance of the Artificial Neural Network is at least as accurate as traditional processing approaches using stopping tables but with the added advantage that the Artificial Neural Network method requires fewer geometric approximations and accounts for all charged particle transport physics implicitly”--Abstract, page iii.

Advisor(s)

Graham, Joseph T.

Committee Member(s)

Usman, Shoaib
Alajo, Ayodeji Babatunde
Liu, Xin (Mining & Nuclear Engr)
Safwan, Jaradat

Department(s)

Nuclear Engineering and Radiation Science

Degree Name

Ph. D. in Nuclear Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2019

Pagination

ix, 92 pages

Note about bibliography

Includes bibliographic references (pages 82-91).

Rights

© 2019 Mubarak Mohammed Albarqi, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12071

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