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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Defect and satellite characteristics of additive manufacturing metal powders

Metal additive manufacturing (AM) requires high-quality metal powders to three-dimensionally (3D) print metallic components with complex and customizable geometries. The lack of quantification of AM metal powders creates quality control challenges for 3D printed components, increases the uncertainty of printing reliability and net cost of inspected and certified printed components, and reduces the recyclability of used powders. However, critical characteristics of AM metal powders that are decisive factors for the 3D printing process, such as internal porosity, contamination, and satellite feature, remain ambiguous. In this work, we developed a novel approach to 3D quantify key characteristics of AM metal powders down to individual particles by using high-resolution synchrotron x-ray computed tomography. Empowered by the penetrative capability of high-energy x-ray, internal porosity and contamination within as-atomized metal powders from high-entropy alloys to nickel-based superalloys were evaluated. Additionally, the newly-developed dispersion method enables the homogeneous separation of individual particles, and consequently, results in the implementation of 3D particle shape analysis. To resolve a major challenge of identification and quantification of satellite-feature particles in as-atomized AM metal powders, the satellite features were quantitated by modeling and analyzing the shape parameter of local thickness variance. Furthermore, the 3D analytical methods of particle assessment in this study can be applied to other materials systems like rock, food, and pharmaceutical particles, and provide insights for process optimization across powder metallurgy, concrete, food and pharmaceutical manufacturing, and AM industries.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Advantages of tandem versus simultaneous operation: The case of isomerization/hydrogenation of terpinolene epoxide to Terpinen-4-ol using a Ni/TiO 2 -SiO 2 bifunctional catalyst

The conversion of terpinolene epoxide to terpinen-4-ol is a desirable chemical transformation for the production of a valuable intermediate. Here this report describes a one-pot, two-step system for the reaction that uses a bifunctional Ni/TiO 2 -SiO 2 catalyst with isomerization and hydrogenation capabilities. The TiO 2 -SiO 2 serves to isomerize the starting epoxide. The Ni component works to hydrogenate the allylic double bond. The tandem system achieves a high 64% yield of terpinen-4-ol. X-ray diffraction analysis showed that TiO 2 -SiO 2 was a mixed binary oxide, and Fourier transform infrared spectroscopy of adsorbed pyridine suggested that acid sites were mainly Lewis acid sites. Electron microscopy showed that the Ni/TiO 2 -SiO 2 catalyst consisted of spherical particles comprising a titanosilicate phase with highly dispersed zero-valent Ni or anatase on the surface. Additionally, there was strong interaction between Ni and the TiO 2 -SiO 2 mixed oxides. Control kinetic experiments demonstrated that the tandem use of the catalysts was more effective than simultaneous operation.

42 ENGINEERING↗
DOE OSTI2020

Rapidly solidifying Pb-free Sn-Ag-Cu-Al or Sn-Cu-Al solder

A solder alloy includes Sn, optional Ag, Cu, and Al wherein the solder alloy composition together with the solder alloy superheat temperature and rapid cooling rate from the superheat temperature are controlled to provide a dispersion of fine hard Cu—Al intermetallic particles in an as-solidified solder alloy microstructure wherein the particles are retained even after multiple solder reflow cycles often used in modern electronic assembly procedures to provide a particle strengthening to the solder joint microstructure as well as exert a grain refining effect on the solder joint microstructure, providing a strong, impact- and thermal aging-resistant solder joint that has beneficial microstructural features and is substantially devoid of Ag3Sn blades.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dispersing nano- and micro-sized portlandite particulates via electrosteric exclusion at short screening lengths

In spite of their high surface charge (zeta potential ζ = +34 mV), aqueous suspensions of portlandite (calcium hydroxide: Ca(OH) 2 ) exhibit a strong tendency to aggregate, and thereby present unstable suspensions. While a variety of commercial dispersants seek to modify the suspension stability and rheology (e.g., yield stress, viscosity), it remains unclear how the performance of electrostatically and/or electrosterically based additives is affected in aqueous environments having either a high ionic strength and/or a pH close to the particle's isoelectric point (IEP). We show that the high native ionic strength (pH ≈ 12.6, IEP: pH ≈ 13) of saturated portlandite suspensions strongly screens electrostatic forces (Debye length: κ -1 = 1.2 nm). As a result, coulombic repulsion alone is insufficient to mitigate particle aggregation and affect rheology. However, a longer-range geometrical particle–particle exclusion that arises from electrosteric hindrance caused by the introduction of comb polyelectrolyte dispersants is very effective at altering the rheological properties and fractal structuring of suspensions. As a result, comb-like dispersants that stretch into the solvent reduce the suspension's yield stress by 5× at similar levels of adsorption as compared to linear dispersants, thus enhancing the critical solid loading (i.e., at which jamming occurs) by 1.4×. Significantly, the behavior of diverse dispersants is found to be inherently related to the thickness of the adsorbed polymer layer on particle surfaces. These outcomes inform the design of dispersants for concentrated suspensions that present strong charge screening behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effects of ZrN Coating and Heat Treatment on U-Mo Dispersion Fuel under Irradiation

The stability of U-Mo fuel particles in Al matrix can be improved using ZrN coating and heat treatment. This study investigated irradiation behaviors of fuel plates containing U-Mo fuel particles fabricated with different conditions including heat treatment and the thickness of ZrN coating. Additionally, different fission densities were applied to each fuel plate to understand how varying fission densities affect its irradiation behaviors. This study showed that more recrystallization of grains and development of high burnup structure (HBS) occurred in irradiated fuel particles with higher fission densities. Heat treatment reduced the accumulation of fission gas bubbles in the fuel particles by coarsening their grains. The fuel particles with a ZrN coating thickness of 1.2-1.8 µm showed less significant development of U-Mo/Al interaction layers than the particles with a coating thickness of 0.5 µm after irradiation. This might indicate the existence of a critical thickness of ZrN coating to minimize the formation of the interaction layers. Fission gas bubbles were observed at grain boundaries of irradiated U-Mo fuel particles at low fission densities, and the formation of dendritic features with a darker contrast was identified in backscattered electron images. These features stemmed from grain boundaries with fission gas bubbles or HBS, and some of them contained the bubbles, indicating that they might represent an early stage of HBS propagation in the fuel particles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Observations of supermicron-sized aerosols originating from biomass burning in southern Central Africa

During the 3 years of the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign, the NASA Orion P-3 was equipped with a 2D stereo (2D-S) probe that imaged particles with maximum dimension (D) ranging from 10 < D < 1280 µm. The 2D-S recorded supermicron-sized aerosol particles (SAPs) outside of clouds within biomass burning plumes during flights over the southeastern Atlantic off Africa's coast. Numerous SAPs with 10 < D < 1520 µm were observed in 2017 and 2018 at altitudes between 1230 and 4000 m, 1000 km from the coastline, mostly between 7–11° S. No SAPs were observed in 2016 as flights were conducted further south and further from the coastline. Number concentrations of refractory black carbon (rBC) measured by a single particle soot photometer ranged from 200 to 1200 cm –3 when SAPs were observed. Transmission electron microscopy images of submicron particulates, collected on Holey carbon grid filters, revealed particles with potassium salts, black carbon (BC), and organics. Energy-dispersive X-ray spectroscopy spectra also detected potassium, a tracer for biomass burning. These measurements provided evidence that the submicron particles originated from biomass burning. NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) 3 d back trajectories show a source in northern Angola for times when large SAPs were observed. Fire Information for Resource Management System (FIRMS) Moderate Resolution Imaging Spectroradiometer (MODIS) 6 active fire maps showed extensive biomass burning at these locations. Given the back trajectories, the high number concentrations of rBC, and the presence of elemental tracers indicative of biomass burning, it is hypothesized that the SAPs imaged by the 2D-S are examples of BC aerosol, ash, or unburned plant material.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Energetic decomposition yields efficient bimetallic Cu MOF-derived catalysts

Metal–organic frameworks (MOFs) have recently emerged as efficient self-sacrificial templates to fabricate porous carbon-supported metal nanoparticles (NPs). Due to observed increased activity, catalysts containing bimetallic NPs represent an active frontier for heterogeneous catalyst development. A strategy to synthesize active catalysts with highly dispersed bimetallic metal/metal oxides within a porous carbon matrix via rapid MOF decomposition using dopants is presented in this paper. A 2,4,6-trinitrotoluene (TNT) additive enhances the rapid thermolysis of the metal-doped Cu-based MOFs to minimize particle aggregation. Catalyst characterization reveals that a secondary metal increases dispersion of both metals over the carbon composite support. The catalyst preparation method influences both the metal particle size and oxidation state. Catalytic performance shows increased rates for 4-nitrophenol reduction even with <1 wt% of added secondary metal. Among the synthesized catalysts, the Ni–CuO@C bimetallic catalyst exhibits outstanding activity. This synthetic strategy is useful for creating highly efficient, robust, non-noble metal catalysts for development of sustainable chemical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

EDXplorer: A Utility for APA Analysis

The automated particle analysis (APA) method of scanning electron microscopy (SEM) energy dispersive X-ray spectroscopy (EDS/EDX) is a useful tool for analyzing the elemental and morphological data of particulate samples. Often, such datasets have many thousands of particles, and it can be difficult to sift through the data to find meaningful trends. EDXplorer is a software utility for processing APA data. They enable the user to easily load data and determine the important components and aspects of the datasets using a powerful and versatile library of data plotting functions, mainly centered around scatter plots and histograms. These programs are intended to fill a void in the data processing of data from certain instruments, where often the user must rely on their own code or other software that is not user-friendly. EDXplorer is intended as a general plotting utility for browsing through data and discovering data trends.

Moseley, Duncan [ORNL] (ORCID:0000000343518347)↗
DOE OSTI2022

Solution combustion synthesis of iron-based alumina nanocomposites for clean production of hydrogen from fossil fuels

Iron-based alumina nanocomposites are promising materials for microwave-assisted thermocatalytic dehydrogenation of fossil fuels, which has been proposed for the production of hydrogen with no CO2 emissions. Solution combustion synthesis (SCS) is an attractive technique for the fabrication of these materials because it has been used for synthesis of many nanoscale oxides. However, SCS can be carried out using different organic fuels and different heating equipment, and it is important to determine which fuel and heating mode generate materials with the best properties for dehydrogenation, such as maximized specific surface area. In the present work, the iron-based alumina nanocomposites were obtained by SCS using two different fuels (citric acid and glycine) and two different heating modes (a hotplate and a muffle furnace). The precursors/oxidizers were iron nitrate and aluminum nitrate. They were mixed with the fuel according to stoichiometry and dissolved in water. Heating the solution resulted in combustion synthesis. The products were characterized by X-ray diffraction analysis, Brunauer-Emmett-Teller surface area analysis, laser diffraction particle size analysis, scanning electron microscopy, and energy dispersive X-ray spectroscopy. The experiments have shown that the specific surface area of the iron-based alumina nanocomposites obtained by SCS can be dramatically increased by using citric acid instead of glycine, and additionally by using a muffle furnace instead of a hotplate.

Solution combustion synthesis, Nanoscale metal oxi↗
DOE OSTI2026

Long-Range Dispersion Governs the Behavior of Near-Critical Fluids: Universal Scaling and Implications for Accurate Molecular Simulation

Computer simulations of near-critical and supercritical fluids often deviate from experimental results, a discrepancy commonly attributed to force-field resolutions and inaccuracies. We demonstrate that these errors primarily stem from using finite cutoff schemes for dispersion interactions, which become effectively long-range as the correlation length grows near the critical point. By employing the smooth particle-mesh Ewald method to account for full long-range dispersion, we show that coarse-grained models can also achieve high quantitative accuracy for n-undecane and propylbenzene. This approach enables the precise determination of density fluctuations and correlation lengths, which exhibit universal critical scaling and depend on the molecular size and shape. Our findings resolve a longstanding debate in molecular simulations of near-critical fluids, establishing that capturing long-wavelength fluctuations is essential for bridging the gap between the microscopic behavior and macroscopic critical phenomena.

Chen, Guang↗
DOE OSTI2026

Experimental Investigation of Uranium and Iron Condensation from High-Temperature Plasma Conditions

We used a plasma flow reactor (PFR) to generate synthetic fallout nanoparticles from vapor-phase condensation of two different input concentrations of uranium and iron analytes (U/Fe = 1:1 and 1:2). Synthetic fallout from complex chemical matrices (e.g., mixtures of U and Fe) has not been generated in this setup before and allows for the observation of relative condensation and fractionation of nuclear debris. Experiments were conducted under two different temperature histories with variations in particle flow patterns along the PFR. Transmission electron microscopy (TEM) observation and analysis of the nanoparticles revealed variations in speciation of uranium oxides (UO 2 and α-UO 3 ) depending on the competition between flow mixing and oxygen sequestration by iron. A ternary metal oxide, UFeO 4 , was observed in addition to iron oxides (e.g., FeO and Fe 3 O 4 ), which suggests that fallout models should account for chemical speciation of ternary metal oxides (i.e., UFeO 4 ) and their relative condensation behaviors in addition to those of singular metal oxides (e.g., FeO and UO 2 ). X-ray Energy Dispersive Spectroscopy (EDS) elemental maps showed that some particles had Fe-rich cores surrounded by U-rich regions. This suggests that either condensed U oxides coagulate onto molten Fe oxides or that the increase in iron analyte concentration might drive the system toward a higher degree of supersaturation, leading to earlier formation of iron oxide particles and providing an energetically favored pathway for nucleation of uranium oxides around iron oxide particles.

and nuclear chemistry↗
DOE OSTI2022

Friction extrusion of ODS copper rod made from powder

Meters-long oxide dispersion strengthened copper rods were manufactured directly from powder by a single-step process, called friction extrusion. The average grain size was 880 nm, which was refined 40 times from powder precursor due to extensive shear deformation. Meanwhile, the average size of alumina particles was 60 nm which retains a decent oxide dispersion strengthening. Here, mechanical properties, including hardness, tensile strength, and ductility, were tested and found to have better or similar values than conventional sintered-then-extruded oxide dispersion strengthened copper materials. The presented technique promises a convenient avenue for the fabrication of oxide dispersion strengthened copper from powder.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Electric-field-assisted-sintering of rare-earth oxide dispersion strengthened Fe-Cr-Mo alloys

Oxide dispersion-strengthened (ODS) alloys are widely recognized for their exceptional high-temperature strength, creep resistance, and radiation tolerance, making them indispensable for advanced nuclear reactors, aerospace, and energy systems. Achieving a fine and stable dispersion of oxide nanoparticles is critical, as these particles act as strong barriers to dislocation motion and effective sinks for irradiation-induced defects, ensuring structural integrity under extreme conditions. Here, in this study, Fe–Cr–Mo-based ODS alloys were fabricated via mechanical alloying and consolidated using electric-field-assisted sintering (EFAS) with additions of Y 2 O 3 , La 2 O 3 , and CeO 2 . EFAS processing produced ultrafine-grained microstructures (average grain size <1 μm) with uniformly distributed oxide clusters (2–4 nm). Atom probe tomography revealed that La 2 O 3 -containing alloys exhibited the highest nanoparticle number density, resulting in superior tensile strength compared to yttria- and ceria-bearing counterparts. The combined effect of grain refinement and rare-earth oxide dispersion significantly enhanced mechanical performance, demonstrating the potential of EFAS for developing high-strength ferritic alloys for demanding environments such as nuclear systems.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

A new method for operating a continuous flow diffusion chamber to investigate immersion freezing: assessment and performance study

Glaciation in mixed-phase clouds predominately occurs through the immersion freezing mode where ice nucleating particles (INPs) immersed within supercooled droplets induce nucleation of ice. Currently, model representations of this process are a large source of uncertainty in simulating cloud radiative properties, and to constrain these estimates, continuous flow diffusion chamber (CFDC)-style INP devices are commonly used to assess the immersion freezing efficiencies of INPs. In this study, a new approach was explored to operating such an ice chamber that provides maximum activation of particles without droplet breakthrough and correction factor ambiguity to obtain high-quality INP measurements in a manner that has not been demonstrated as possible previously. The conditioning section of the chamber was maintained at ~ –20 °C and water relative humidity (RH w ) ~ 113 % conditions to maximize the droplet activation, and the droplets were supercooled with an independently temperature-controlled nucleation section at a steady cooling rate (0.5 °C min –1 ) to induce the freezing of droplets and evaporation of unfrozen droplets. The performance of the modified ice chamber was evaluated using four INP species: K-feldspar, illite-NX, Argentinian soil dust, and airborne arable dust that had shown ice nucleation over a wide span of supercooled temperatures. Dry dispersed and size-selected K-feldspar particles were generated in the laboratory. Illite-NX and soil dust particles were sampled during the second phase of the Fifth International Ice Nucleation Workshop (FIN-02) campaign, and airborne arable dust particles were sampled from the aerosol inlet located on the rooftop of the laboratory. The measured ice nucleation efficiencies of model aerosols with a surface active site density (n s ) metric were higher, but mostly agreed within one order of magnitude compared to literature results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A new method for operating a continuous-flow diffusion chamber to investigate immersion freezing: assessment and performance study

Abstract. Glaciation in mixed-phase clouds predominantly occurs through the immersion-freezing mode where ice-nucleating particles (INPs) immersed within supercooled droplets induce the nucleation of ice. Model representations of this process currently are a large source of uncertainty in simulating cloud radiative properties, so to constrain these estimates, continuous-flow diffusion chamber (CFDC)-style INP devices are commonly used to assess the immersion-freezing efficiencies of INPs. This study explored a new approach to operating such an ice chamber that provides maximum activation of particles without droplet breakthrough and correction factor ambiguity to obtain high-quality INP measurements in a manner that previously had not been demonstrated to be possible. The conditioning section of the chamber was maintained at −20 ∘C and water relative humidity (RHw) conditions of 113 % to maximize the droplet activation, and the droplets were supercooled with an independently temperature-controlled nucleation section at a steady cooling rate (0.5 ∘C min−1) to induce the freezing of droplets and evaporation of unfrozen droplets. The performance of the modified compact ice chamber (MCIC) was evaluated using four INP species: K-feldspar, illite-NX, Argentinian soil dust, and airborne soil dusts from an arable region that had shown ice nucleation over a wide span of supercooled temperatures. Dry-dispersed and size-selected K-feldspar particles were generated in the laboratory. Illite-NX and soil dust particles were sampled during the second phase of the Fifth International Ice Nucleation Workshop (FIN-02) campaign, and airborne soil dust particles were sampled from an ambient aerosol inlet. The measured ice nucleation efficiencies of model aerosols that had a surface active site density (ns) metric were higher but mostly agreed within 1 order of magnitude compared to results reported in the literature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗