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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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79 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tunable Electrochemical Pathway for High-Purity REMs and CMs

This report details a comprehensive research initiative focused on developing a technical research plan for the efficient and sustainable production of rare earth elements (REEs) and critical materials (CMs) from coal-based resources, specifically lignite coal from the Williston Basin in North Dakota. The project’s methodology centers on the design and evaluation of a tunable electrochemical pathway (TEP), an innovative approach aimed at achieving high-purity rare earth oxides (REOs), rare earth salts (RES), and CMs. This research is crucial for addressing the growing demand for these materials in clean energy technologies, electric vehicles, and high-tech applications, while reducing the United States’ reliance on foreign supply chains. The project was structured into four key tasks, each designed to build upon the previous one and contribute to the overarching goal of developing a viable technical research plan.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Generation of Rare Earth Metals from Rare Earth Oxide Using Microwave Plasmas

The overarching goal of the proposed project is to develop a simple, energy-saving, high-selectivity technology for efficiently converting rare-earth oxides (REOs) to rare-earth metals (RE-metals) using microwave plasma under low temperature and inexpensive H 2 and N 2 . The project was managed and led by Dr. Maohong Fan at the University of Wyoming. The collaborating institutions include Ames Lab and Colorado School of Mines. The industrial collaboration entity is Eutectix, LLC. The use of hydrogen to reduce rare earth oxides has many advantages, as it is renewable and does not produce harmful emissions during metal production. Also, it avoids the use of carbon and the emission of CO 2 . Nd 2 O 3 treated with hydrogen plasma shows new peaks alongside those of Nd 2 O 3 . Some of these peaks are close to (28.4, 29.4, 30.5, 32.3, 36.6, 42.1, 48.9, 50.08, 55.1, 59.05, 59.64, 60.68, 62.5, 63.25), which may be related to elemental Nd.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interactions of Perrhenate (Re(VII)O 4 - ) with Fe(II)-Bearing Minerals

Rhenium (Re) is an extremely rare element, with a crustal abundance of approximately 0.4 parts per billion (ppb) and a sea water concentration of 8.3 parts per trillion (ppt). However, Re concentrations in anoxic marine sediments range from 2 to 184 ppb, which is attributed to reduction of the highly soluble perrhenate ion (Re(VII)O 4 - ) to insoluble Re(IV) species. Anoxic sediments typically contain Fe(II) and sulfide species, which could potentially reduce Re(VII) to Re(IV). In this study, we examined the interactions of KReO 4 with magnetite (Fe 3 O 4 ), siderite (FeCO 3 ), vivianite (Fe 3 (PO 4 ) 2 •8H 2 O), green rust (mixed Fe(II)/Fe(III) layered double hydroxide), mackinawite (FeS), and chemically reduced nontronite (NAu-1) using X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectroscopy to determine the valence state and speciation of Re. Uptake of Re by green rust was rapid, with ~50% associated with the solids within 2 days. In contrast, there was <10% uptake by the other Fe(II) phases over 48 days. Reduction of Re(VII) to Re(IV) was only observed in the presence of green rust, producing clusters of bidentate-coordinated Re(IV)O 6 octahedra.. These results suggest that except for green rust, the potential for other Fe(II)-bearing minerals to act as reductants for ReO 4 - in sedimentary environments requires further investigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

In-orbit performance of the soft X-ray imaging telescope Xtend aboard XRISM

Here, we present a summary of the in-orbit performance of the soft X-ray imaging telescope Xtend onboard the X-Ray Imaging and Spectroscopy Mission (XRISM), based on in-flight observation data, including first-light celestial objects, calibration sources, and results from the cross-calibration campaign with other currently operating X-ray observatories. XRISM/Xtend has a large field of view of ${38{^{\prime }_{.}}5}$ $\times$ ${38{^{\prime }_{.}}5}$, covering an energy range of 0.4–13 keV, as demonstrated by the first-light observation of the galaxy cluster Abell 2319. It also features an energy resolution of 170–180 eV at 6 keV, which meets the mission requirement and enables us to resolve He-like and H-like Fe K$\alpha$ lines. Throughout the observation during the performance verification phase, we confirm that two issues identified in the Soft X-ray Imager (SXI) onboard the previous Hitomi mission—light leakage and crosstalk events—are addressed and suppressed in the case of Xtend. A joint cross-calibration observation of the bright quasar 3C 273 results in an effective area measured to be $\sim$420 cm$^{2}$ at1.5 keV and $\sim$310 cm$^{2}$ at 6.0 keV, which matches values obtained in ground tests. We also continuously monitor the health of Xtend by analyzing overclocking data, calibration source spectra, and day-Earth observations; the readout noise is stable and low, and contamination is negligible even one year after launch. A low background level compared with other major X-ray instruments onboard satellites, combined with the largest grasp ($\Omega _{\rm eff}\sim 60$ cm$^2$ deg$^2$) of Xtend, will not only support Resolve analysis, but also enable significant scientific results on its own. This includes near-future follow-up observations and transient searches in the context of time-domain and multi-messenger astrophysics.

instrumentation: detectors: Xtend↗
DOE OSTI2022

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Exotanium DOE SBIR Phase I Results Summary

Exotanium demonstrated this technology with the Idaho National Laboratory’s MASTODON application, a Multiphysics environment designed to run typical high-performance computing (HPC) simulations for structural dynamics, seismic analysis, and risk assessment. The MASTODON application was packaged into a container using Docker, Deployed on Amazon ECS, and managed through a custom Scale-Out Compute on AWS (SOCA) implementation.

97 MATHEMATICS AND COMPUTING↗