Doctoral Dissertations

Orcid ID

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Nuclear Engineering

Major Professor

Maik Lang

Committee Members

John D. Auxier, Lawerence Heilbronn, Ning Xu, Ania Szynkiewics


Pre- and post-detonation nuclear material require chemical analysis that is rapid, precise, and in some cases, portable. Special Nuclear Material (SNM) as well as nuclear debris is mostly radioactive, causing additional safety concerns and complexities in recent research. Literature research has provided a large number of conventional table top analysis techniques, focusing on the measurement of actinide ratios – most of which are destructive analysis. There is a need for the development of chemical characterization methods for pre- and post-detonation nuclear material that focuses on less destructive techniques for age and compositional analysis, as well as reduction in time of analysis (and thus, exposure to radiation). The research presented (i) investigates time- and temperature-dependent signatures of SNM (pre-detonation) through O isotope fractionation (ii) determines the effects of ion irradiation on SNM and how damage affects oxidation over time (iii) modifies data acquisition of HHLIBS in identification of nuclear debris through multivariate analysis (MVA) techniques. The application of MVA techniques to HHLIBS measurements produces quantitative compositional data from unknown samples and is expected to be a major contribution from this research, most notably for nuclear forensics. There is a large gap in the ability to use commercial HHLIBS for direct forensic analysis beyond qualitative relative abundances without any previous knowledge of sample composition. Due to the shared nature of nuclear weapon material to that of nuclear fuel, the results from these studies can be used to speculate the usefulness of MVA HHLIBS for routine non-destructive analysis inspections at nuclear energy facilities. The use of ammonium biflouride digestion (ABF) and ICP-OES for destructive methods will help to validate the MVA model and through iteration, create a successful method for infield analysis of unknown samples.

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