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Auxier II, John D.

Publications and source records attributed to Auxier II, John D..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Radiochemistry and nuclear chemistry workforce in the United States

The disciplines of radiochemistry and nuclear chemistry have direct applications in the fields of national security, nuclear medicine, nuclear power production, and environmental management. Although, often, nuclear and radiochemistry are grouped together and many experts work in both areas, the definition for each field is slightly different. For example, radiochemistry may be defined as the application of the phenomena of radioactive decay and techniques common to nuclear physics so as to solve problems in the field of chemistry. In contrast, nuclear chemistry may be defined as the application of procedures and techniques common to chemistry to study the structure of the atomic nucleus. This chapter provides a brief update of the current state of, and critical U.S. needs for, nuclear chemistry and radiochemistry expertise as the Assuring a Future U.S.-Based Nuclear and Radiochemistry Expertise report was published by National Academy of Sciences (NAS) in 2012.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Analytical comparisons of handheld LIBS and XRF devices for rapid quantification of gallium in a plutonium surrogate matrix

This work compares a portable laser-induced breakdown spectroscopy (LIBS) analyzer to a portable X-ray fluorescence (XRF) device for quantification of gallium (Ga) in a plutonium surrogate matrix of cerium (Ce) for the first time. Calibration methods are developed with spectra of Ce–Ga samples from both devices. Here, metrics such as limit of detection (LoD) and mean average percent error (MAPE) are examined to evaluate calibration performance. While the portable LIBS device can yield a nearly instantaneous analytical measurement, its accuracy is hampered by self-absorption. By employing a self-absorption correction and increasing gating delay, LIBS calibrations with errors in the low single percents and LoDs of 0.1% Ga were constructed. The XRF device produces calibrations with superlative sensitivity, yielding LoDs for gallium in the low tens of parts-per-million (ppm), two orders of magnitude lower than the corrected LIBS models. However, a clear trade-off of measurement fidelity is established between the instantaneous analysis of the LIBS device and the minutes-long XRF measurement yielding superior detection limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

Here, we present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

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. Additionally, 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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗