Exploring Rapid Radiochemical Separations at the University of Tennessee Radiochemistry Center of Excellence

The University of Tennessee formed its Radiochemistry Center of Excellence (RCoE) in 2013 with support from the U.S. National Nuclear Security Administration. One of the major thrusts of the RCoE is to develop deeper understanding of rapid methods for radiochemical separations that are relevant to both general radiochemical analyses as well as post-detonation nuclear forensics. Early work has included the development and demonstration of rapid separations of lanthanide elements in the gas phase, development of a gas-phase separation front-end for ICP-TOF-MS analysis, and the development of realistic analytical surrogates for post-detonation debris to support methods development

Modern Advancements in Post-Detonation Nuclear Forensic Analysis

Deterring nuclear terrorism is a critical national asset to support the preclusion of non-state actors from initiating a nuclear attack on the United States. Successful attribution of a detonated nuclear weapon allows for timely responsive measures that prove essential in the period following a nuclear event. In conjunction with intelligence and law enforcement evidence, the technical nuclear forensics (TNF) post-detonation community supports this mission through the development and advancement of expertise to characterize weapon debris through a rapid, accurate, and detailed approach. Though the TNF field is young, numerous strides have been made in recent years toward a more robust characterization capability. This work presents modern advancements in post-detonation expertise over the last ten years and demonstrates the need for continued extensive research in this field

Establishing Cost-Effective Computational Models for the Prediction of Lanthanoid Binding in [Ln(NO3)]2+ (with Ln = La to Lu)

This article is a study evaluating the predictive capabilities of various ab initio methods in the calculation of Gibbs free energies of reaction for [Ln(NO₃)]²⁺ compounds (with Ln = La to Lu), as nitrates are critical in traditional separation processes utilizing nitric acid. The composite methodologies evaluated predict Gibbs free energies of reaction for [Ln(NO₃)]²⁺ compounds within 5 kcal mol⁻¹ in most cases from the target method [CCSD(T)-FSII/cc-pwCV∞Z-DK3+SO] at a fraction of the computational cost

Applications of Portable LIBS for Actinide Analysis

A portable LIBS device was used for rapid elemental impurity analysis of plutonium alloys. This device demonstrates the potential for fast, accurate in-situ chemical analysis and could significantly reduce the fabrication time of plutonium alloys

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA),partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. A boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys