Rapid and accurate elemental separation is crucial for analyzing national technical nuclear forensics (ntnf) debris samples

This $300,000 Cooperative Agreement awarded by the Department of Homeland Security's Countering Weapons of Mass Destruction Office under the National Nuclear Forensics Expertise Development Program (CFDA 97.130) will support the University of Florida in developing advanced capabilities for the rapid and accurate separation and analysis of nuclear forensics debris samples. The key products and services to be delivered include:

1. Developing new chemical separation methods, theoretical modeling, and coding tools to accelerate the processing and reporting of data from nuclear forensics samples. This includes creating AI-driven predictive capabilities for separation schemes.

2. Training two Ph.D. and two undergraduate students in nuclear forensics techniques, including radiochemistry, instrumentation, data processing, and theoretical calculations.

3. Outreach activities to engage other students and academic communities in the DHS scientific work on nuclear forensics.

The project aims to substantially impact the speed and accuracy of nuclear forensics analysis, advance the underpinning separation science, and develop the next generation of experts in this critical national security domain.

Pnnl has developed state-of-the-art flowsheets and artificial intelligence (AI) tools for separating and quantifying actinides, fission, and activation products and several instrument data-translation tools to improve reporting timelines. To optimize pnnl separation systems, we will develop chemical and theoretical methods and coding tools to accelerate ntnf sample analyses. This project is unique because IT is multidisciplinary IT combines specialists in radiochemistry and theoretical calculations in association with ntnf experts at pnnl. We will deliver impactful methodologies for separating relevant ntnf elements using experimental radioanalytical techniques, tools that convert raw instrument data into report-ready formats using codes such as python, and thermochemical data that will aid with the development of AI tools to predict separation schemesthese thermodynamic data will be quantified using theoretical calculations (density-functional theory, DFT). We will also train two PH.d. And two undergraduate students, who will graduate with a sophisticated knowledge of chemical separations, radioanalytical techniques and instrumentation, coding, and theoretical calculations for problems associated with nuclear forensics. Furthermore, we will reach out to other potential students, sparking their interest in nuclear forensics and DHS scientific activities. This application responds to the nofo TMA 3 that asks explicitly for innovations that rapidly identify and quantify elemental and isotopic constituents (major, minor, and trace) in fresh radioactive fallout debris and data handling methodologies. This work will substantially impact 1) the acceleration of nuclear forensics data processing and reporting, 2) separation science and associated predictive capabilities, 3) the training of the next generation in NU-clear forensics, and 4) the visibility of the DHS scientific activities to the US academic COM-munities, therefore supporting the DHS countering weapons of mass destruction office (cwmd) mission objective to detect nuclear materials.