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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Radiative transition probabilities of neutral and singly ionized Europium estimated by laser-induced breakdown spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) is a versatile technique for compositional analysis for solids, liquids, or gasses. LIBS is an asset for the quantitative or qualitative analysis of resource limited materials like actinides and rare earths because it is quasi-nondestructive. Two Eu 2 O 3 pellets were synthesized to be a test and validation set, respectively. Spectral lines identified from the National Institute of Standards and Technology database with fundamental data reported were used to form Saha-Boltzmann plots. The Saha-Boltzmann plots were used to determine the plasma temperatures and electron densities of the laser-induced plasmas for both samples. These Saha-Boltzmann plots were then used to calculate previously unreported transition probabilities associated with identified peaks. Additionally, the transition probabilities presented in this paper provide the capability for calibration free LIBS to be performed more readily on europium samples and the spectroscopic analysis of stellar bodies. Eight previously unreported transition probabilities are presented in this paper; five for Eu I and three for Eu II. The transition probabilities for Eu I ranged from 0.172 to 7.38 × 10 7 s -1 and those for Eu II ranged from 1.56 to 6.75 × 10 7 s -1 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Radiative transition probabilities of neutral and singly ionized rare earth elements (La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) estimated by laser-induced breakdown spectroscopy

Rare earth elements (REEs) are essential to society given their prevalence in many modern technologies. Quantitative elemental analysis of REEs is therefore a critical capability. Calibration free–laser-induced breakdown spectroscopy (CF-LIBS) is a rapidly maturing and promising approach to quantitative elemental analysis with many attractive qualities. The application of CF-LIBS to analyzing samples containing the REEs is hindered by a lack of fundamental data, specifically transition probabilities (TPs). As such, this study seeks to help address this knowledge deficiency by reporting 967 previously unreported TPs for 13 REEs (lanthanum, cerium, praseodymium, neodymium, samarium, gadolinium, terbium, dysprosium, holmium, erbium, thulium, ytterbium, and lutetium). The method developed in this study to estimate the TPs requires small amounts of material (a few nanograms) compared with other approaches (~ milligrams), uses non-specialized equipment, and does not involve complicated sample preparations.

(LIBS)↗
DOE OSTI2024

A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network

Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.

36 MATERIALS SCIENCE↗