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Kautz, Elizabeth

Publications and source records attributed to Kautz, Elizabeth.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Calibration-free analysis of Li isotope ratios using laser ablation and laser absorption spectroscopy

We introduce a rapid, calibration-free, all-optical method for high-precision lithium isotope ratio measurements in solid materials using laser ablation combined with tunable laser absorption spectroscopy. A new asynchronous acquisition method is used to acquire time-resolved, high-resolution spectra of the 6Li and 7Li D1 and D2 transitions near 671 nm. Isotope ratios and atomic column densities are extracted from measured spectra via a physics-based fitting model including hyperfine structure. Under 1 Torr air, spectra recorded = 0.75 ms after plasma onset exhibit narrow linewidths corresponding to Doppler temperatures = 400 K, enabling resolution of the isotope peaks with high signal-to-noise ratios. Analysis of LiAlO2 samples with varying 6Li:7Li ratios demonstrates isotopic precisions of 0.6–1.7% for spectra acquired in 30 s. Isotope ratios determined from the spectral fits show accuracy within –0.3% to –1.7% of reference ICP-MS measurements without requiring calibration to external standards. By eliminating sample preparation and enabling spatially resolved isotopic mapping, this method offers a rapid analysis approach to lithium isotope determination in solid materials relevant to nuclear energy, safeguards, and geochemistry.

Phillips, Mark C.↗
DOE OSTI2024

Mechanistic understanding of speciated oxide growth in high entropy alloys

Abstract Complex multi-element alloys are gaining prominence for structural applications, supplementing steels, and superalloys. Understanding the impact of each element on alloy surfaces due to oxidation is vital in maintaining material integrity. This study investigates oxidation mechanisms in these alloys using a model five-element equiatomic CoCrFeNiMn alloy, in a controlled oxygen environment. The oxidation-induced surface changes correlate with each element’s interactive tendencies with the environment, guided by thermodynamics. Initial oxidation stages follow atomic size and redox potential, with the latter becoming dominant over time, causing composition inversion. The study employs in-situ atom probe tomography, transmission electron microscopy, and X-ray absorption near-edge structure techniques to elucidate the oxidation process and surface oxide structure evolution. Our findings deconvolute the mechanism for compositional and structural changes in the oxide film and will pave the way for a predictive design of complex alloys with improved resistance to oxidation under extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correlating Nanoscale Secondary Ion Mass Spectrometry and Atom Probe Tomography Analysis of Uranium Enrichment in Metallic Nuclear Fuel

Distribution and enrichment of 235U within nuclear fuels are essential to material performance and reactor safety. With the development of metallic U fuels, understanding 235U homogeneity has become increasingly important, however, it is difficult to map with high spatial, and mass resolution to analyze both matrix and secondary phase precipitates. Here, we analyze 235U enrichment in matrix and carbide phases in LEU-Mo via two chemical imaging modalities: nanoscale secondary ion mass spectrometry (nanoSIMS) and atom probe tomography (APT). NanoSIMS and APT provided consistent results, with no statistically significant difference between measured and nominal enrichment (19.75 % 235U).

U-10Mo, atom probe tomography, nanoscale secondary↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗