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Nuclear Data Evaluation of High-Energy Proton-Induced Reactions for Isotope Production


This dissertation details the first experiments of a newly formed Tri-laboratory Effort in Nuclear Data (TREND) between Lawrence Berkeley, Los Alamos, and Brookhaven National Laboratories. TREND was established to address lacking high-energy charged-particle data needs for isotope production by measuring proton-induced nuclear reaction cross sections from 35 to 200 MeV. The experimental methods and results for multiple stacked-target irradiations performed in support of this effort using arsenic, niobium, copper, and titanium targets are discussed. An extensive focus dedicated to the characterization of the 75As(p,x)72Se, 68Ge excitation functions is included on account of their sought-after promise as generator nuclei for PET imaging.

In addition to providing direct information for the production of medical radionuclides, the TREND results were used to develop a new data analysis methodology for high-energy (p,x) reactions. Moreover, this thesis uniquely merges experimental work and evaluation techniques with the introduction of a standardized original evaluation procedure that can be used to optimize the planning and execution of isotope production with high-energy, high-intensity proton accelerators. The presented methodology provides insight into pre-equilibrium reaction dynamics and a host of nuclear data properties relevant to the accurate modeling of high-energy proton-induced reactions. Notably, this evaluation approach also includes a new method for charged-particle data validation.

Finally, this dissertation discusses the fabrication and characterization of the thin arsenic targets used in the stacked-target irradiations at the heart of the TREND experiments. The work herein aims to bolster modern targetry knowledge and depicts the difficulties and successes in meeting uniformity and thickness requirements for arsenic targets. A new thin-target characterization technique, developed using traditional neutron activation tools, that is reliable, accessible, and non-destructive is presented.

There exists untenable uncertainty and unreliability for the use of nuclear reaction codes in the medical isotope production community, particularly at high-energies, where charged-particle modeling suffers or is naïvely determined because little guiding data and evaluation exist. Altogether, this dissertation is an essential improvement of the infrastructure critical to the future of charged-particle isotope production and foundational nuclear data.

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