University of Utah Awarded $5M in Nuclear Forensics Research
Since September 2015, the University of Utah has been awarded three federal grants (almost $5M in funding) to improve nuclear forensics capabilities in the U.S. Two of those grants are led by CVEEN/UNEP Assistant Professor, Luther McDonald; while the third is led by Tolga Tasdizen of the Scientific and Computing Institute (SCI) with McDonald as a collaborator. In total, this funding is supporting 10 new Ph.D. students in areas relevant to nuclear forensics including: nuclear engineering, environmental engineering, and computer science.
Currently, if unknown nuclear material is caught during illicit trafficking or if a nuclear weapon were to be detonated, then it can take weeks or even months to do the full forensic analysis to determine the origin of the material, history of the material, and type of material. Obviously, this timeline is not reasonable. Whenever nuclear material is discovered, it is of prime importance to complete the forensic analysis ASAP!
Research in the McDonald lab is addressing this timeline and working to discover novel signatures that can be detected much faster. The first project awarded to McDonald by the Domestic Nuclear Detection Office – Academic Research Initiative (DNDO-ARI), investigates the morphological and microstructural properties of uranium ores and uranium oxides; while the second project awarded by the Defense Threat Reduction Agency (DTRA), expands upon this work to investigate the 18O/16O isotope ratio. Ultimately, the team hopes to quantify changes in the microstructure and 18O/16O isotope ratio based on preparation methods, storage location, and ageing in controlled temperature and humidity environments. Quantifying these features would yield a signature for the identification of provenance, process history, and a geographical timeline of uranium materials used in the production of nuclear weapons. This would revolutionize the current nuclear forensics protocols reducing the timeline from weeks/months to hours/days.
One of the biggest challenges of this research is the sheer size and complexity of the experimental data. Hence, this past Spring, McDonald teamed up with Tasdizen to establish a collaboration utilizing machine learning to aid in the data analysis. Their collaborative proposal, submitted to DNDO-ARI, was funded this past August.