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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Characterizing microscale signatures in uranium ore concentrates using electron probe microanalyzer

Impurities in uranium ore concentrates (UOCs) serve as forensic signatures of processing history and origin. Here, this study utilizes Electron Probe Microanalyzer (EPMA) to characterize microscale compositional and textural features in individual UOC particles from both commercial and bench-scale production. At the particle scale, multiple internal phases with distinct morphologies, chemical signatures, and stoichiometries are documented. Our data shows that chemical impurities are heterogeneously distributed within single particles and among particles within a sample. These microscale heterogeneities correlate with known processing histories, indicating that microscale signatures of early fuel cycle materials can provide valuable information for nuclear forensic material analysis.

organic↗
DOE OSTI2024

Hot-Roll Fabrication of Anisotropic Nanograin Nd-Fe-B Magnet

Nd-Fe-B based magnets have the highest energy product among all permanent magnets, which is required for numerous clean energy technologies. For higher temperature applications (T > 150°C), additions of heavy rare earth elements (HREEs) such as Dy are required to maintain sufficient coercivity during operation. Additions of Dy are expensive. Thus, it is desirable to reduce the need for HREEs by reducing the grain size to the nanoscale, which increases the coercivity and decreases its temperature dependence. Here, we report a novel nanograin Nd-Fe-B magnet fabrication method that is continuous and inexpensive. The process uses mechanically milled Nd-Fe-B melt-spun flakes as feedstock powder that is packed into a metal vessel and then hot rolled to form a fully dense and highly textured strip magnet with tailored thicknesses, down to 800 µm. Finally, using this process, fully dense nanograin bulk magnets can be synthesized in minutes compared to the traditional multi-step processes that are typically low throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural Evolution of Steel During Magnetic Field-Assisted Processing

Advancing magnetic field-assisted processing, as an energy-efficient method for tailoring steel microstructures, requires a thorough understanding of how the high magnetic field impacts microstructural evolution, particularly its effect on prior austenite grain structures. The current investigation of a near-eutectoid composition, Fe-C alloy, uses electron backscatter diffraction to examine the morphology and orientation of martensite and pearlite microstructures, and to reconstruct the parent austenite microstructures present during equivalent heating under varied magnetic field strengths (0-T, 2-T, 5-T, and 9-T). It was observed that the magnetic field has a negligible effect on martensite lath/block width, slightly decreases prior austenite grain size, and increases the fraction of austenite grains with annealing twins. Additionally, the magnetic field increases the phase fraction of proeutectoid ferrite but has a negligible effect on pearlite block size and the distribution of boundary misorientation angles. No preferred texture was induced by the magnetic field, regardless of the applied field direction, in the proeutectoid ferrite phase or the martensite and prior austenite microstructures. Furthermore, the observed results contradict previous literature, and the differences are discussed.

Magnetic Materials↗
DOE OSTI2025

A Review of the Influence of Processing Parameters on ODS Steels Produced via Additive Manufacturing Techniques

Abstract This paper reviews current observations regarding processing conditions for oxide dispersion-strengthened steels consolidated through additive manufacturing techniques. Variations in ODS steels observed across process parameters include changes in grain size, grain texture, oxide size, density of oxides, porosity, melt pool characteristics, and mechanical properties. These properties were then compared across techniques to understand which techniques and processing conditions lead to the highest strength, ductility, and oxide density. Current literature suggests that a mix of grain types, in the form of either morphology or phase, can significantly increase the strength of printed ODS steels. Meanwhile, the most ductile samples, regardless of consolidation technique or matrix material, were made from feedstock with oxide additions located on the powder surface. Reported grain and oxide sizes were plotted against the ratio of laser power to scan speed, volumetric energy density, and normalized enthalpy. No strong correlation between these values and microstructural features was observed. The plots that were made suggest that a larger data set, more in-depth representative equations, and more defined material properties as a function of specific feedstock used are necessary to determine a value that can be correlated to the printed ODS steel microstructure.

deJong, Matthew↗
DOE OSTI2024

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

Abstract The Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench .

Levine, L. E. (ORCID:0000000334484229)↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2024

Nanotwinned alloys under high pressure

Nanotwinned alloys are of interest due to their high strength and ductility, but twin boundaries may not be stable under shear. Computational studies indicate that high hydrostatic pressure may suppress detwinning mechanisms. Here, in this study, we investigate the microstructural changes of nanotwinned-nanocrystalline copper-nickel and Inconel 725 alloys under quasi-hydrostatic pressures up to 50 gigapascals (GPa). The alloys are compressed in a diamond anvil cell. In-situ x-ray diffraction (XRD) and ex-situ transmission electron microscopy (TEM) were employed to monitor microstructural changes. Twin boundary deformation and grain growth occur at 11.4 GPa quasi-hydrostatic pressure in the copper-nickel alloy. Molecular dynamics (MD) simulations reveal that hydrostatic pressure causes elevated local shear stress at grain boundaries, which leads to atomic rearrangements. A superposition of hydrostatic and deviatoric pressures lead to partial dislocation mediated twin boundary migration. In contrast, the Inconel 725 alloy showed stable twin and grain boundaries up to a quasi-hydrostatic pressure of 12.7 GPa. Texture, high solid solution strengthening, and low stacking fault energy are hypothesized to the enhanced microstructural stability in Inconel 725.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy

The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.

Additive manufacturing↗
DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗
DOE OSTI2025

Effect of laser melt schedule on the microstructure of additively manufactured IN718 Superalloy

Laser powder bed fusion (L-PBF) has enabled the fabrication of geometrically complex metallic structures and components that are challenging to producing using conventional manufacturing approaches. The site-specific and far from equilibrium thermal conditions of L-PBF offer the potential to facilitate multi-length scale design of structure and properties across the atomic-through macro-levels. However, L-PBF systems face scalability challenges due to throughput constraints. Laser rotary powder bed fusion (L-RPBF) systems are being investigated as a solution to enhance the deposition rates compared to conventional L-PBF. Rotary systems also offer additional flexibility for controlling the time structure of melting through laser interleaving on alternating layers. Here, in this study, IN718 test samples were printed using single-laser or interleaved dual-laser configuration in a L-RPBF system to investigates the effect of process settings and melt-interleaving on as-fabricated microstructure. The microstructural evolution, such as grain size and crystallographic texture, was assessed by determining variations in the melt-pool shapes. Laser interleaving leads to a reduction in average grain size compared to single laser by ∼ 40 % at high power (400 W) and by ∼36 % at medium power (370 W). Results presented here identify key challenge for obtaining uniform microstructures and barriers for the broader adoption of high-deposition rate L-RPBF.

Dual-laser↗
DOE OSTI2024

Relating flow resistance to equivalent roughness

Describing flow resistance using the physical properties of an underlying surface is a recalcitrant problem in overland flow models. If discharge measurements are available, an equivalent roughness (e.g., Manning’s n) can be calibrated to represent the effects of surface properties within the domain with a single numerical value. Alternatively, the flow resistance can be estimated from discharge and velocity measured at a point, typically a runoff plot outlet. However, such experimental estimates are often inconsistent with the equivalent roughness determined from calibration to discharge, even if both derive from the same dataset. For example, if Manning’s equation is used to parameterize flow resistance, the Manning’s n obtained by calibrating a model to discharge differs from the value of n calculated from measured flow and velocity at the hillslope outlet. Here, this discrepancy is resolved by deriving a correction factor relating experimentally-determined flow resistance to the equivalent roughness. The derived correction factor is tested for four commonly-used resistance formulations using 129 rainfall simulator experiments. The correction factor is necessary to reproduce measured velocities, and yields minor improvements in discharge prediction. Plain Language Summary: Accurate runoff prediction is needed for land and water management in dryland regions, where sporadic and limited rainfall necessitate efficient water use and drought mitigation strategies. The skill of runoff models is known to be hindered by out ability to estimate flow resistance, which is the quantity that describes how energy is lost from flowing water to the underlying surface. Typically, models represent flow resistance with an equivalent roughness, e.g., Manning’s n, that is adjusted until the model can reproduce available discharge observations at watershed scale. However, the flow resistance measured in plot-scale experiments (1–10 m) often exceeds equivalent roughness coefficients by a factor of 10. This means that the direct use of plot-scale experimental data to parameterize runoff models could cause errors in discharge and runoff velocity predictions. Here, we resolve these differences by deriving an analytic correction factor that relates flow resistance to the equivalent roughness required for models to reproduce experimental velocity and discharge data. This correction factor is tested using rainfall simulator data from 129 experiments performed in the US Southwest covering a wide range of precipitation intensities, soil textures and vegetation types. Use of the correction factor substantially improves model prediction of flow velocity, which is needed for reproducing the timing of flood events and the estimation of erosion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impacts of feeding three strains of microalgae alone or in combination on growth performance, protein metabolism, and meat quality of broiler chickens

Variations in nutrient compositions, especially amino acid (AA) profiles, among microalgal species may enable a superior feeding outcome from a combined than singular supplementation in poultry diets. Therefore, a feeding trial was conducted to compare the effects of three strains of microalgal biomass supplemented alone or in combination to replace 5 % (starter) and 10 % (grower) soybean meal (on weight-to-weight basis) on growth performance, protein metabolism, and meat quality of broiler chickens. Day-old Cornish Cross male chicks (total = 180) were divided into 5 groups (6 cages/treatment, 6 birds/cage) and fed a corn-soybean meal basal diet (BD), BD + H117 (Chlorella sp., H117), BD + C985 (Tetraselmis sp., C985), BD + Nannochloropsis oceanica (NO), and BD + H117 + C985 + NO (Combination). Feeding any of the microalgae diets did not alter growth performance nor meat quality including texture, pH, color, and water holding capacity of breast and thigh meats. However, the breast weight percentages were decreased (P < 0.05) by feeding the C985, NO, and Combination diets. Compared with the BD, the 4 microalgal diets led to higher (P < 0.05) plasma uric acid and protein concentrations at weeks 3 and (or) 6. The mRNA levels of MAFbx, MURF1, FOXO1, and calpastatin in the breast and thigh muscles were altered by the microalgal diets but not those of genes associated with other quality traits. In conclusion, replacing 5 % or 10 % soybean meal with three sources of microalgae in broiler diets decreased breast weights percentage but not absolute weight. Furthermore, feeding chickens with the combination of three microalgae did not restore the breast loss and induced different expressions of genes related to muscle hypertrophy or atrophy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Porous mesh manifold for enhanced boiling performance

High-performance electronics are continuously demanding cooling of higher heat fluxes. Phase-change cooling, including pool boiling, is a useful approach to address this challenge; however, competition between liquid and vapor flows generally limit the heat fluxes that can be dissipated. A range of strategies to control these flows have been investigated previously, including capillary guides. Here a manifold structure formed from a metallic mesh is investigated to control the disposition of liquid and vapor phases above a pool fed boiling surface enhanced with porous structures. Copper mesh forms defined liquid flow paths, using capillary action to guide and distribute liquid evenly over the heated surface, along with open channels to facilitate vapor escape. The mesh provides a novel structure for liquid guidance that imposes low resistance to liquid flow while occluding a minimal area of heated surface underneath. The manifold performance is characterized in boiling fed by a pool of water above a laser-textured aluminum nitride heat dissipation surface with pin–fin structures having heights of 110 µm and spacing of 30 µm with a heated area of 5 mm x 5 mm. A maximum heat flux of 490 W/cm 2 is reached with the manifold in the pool fed configuration, representing an increase of more than 65% over the porous pin fin surface alone. The maximum stable superheat observed for the manifold of 36K is 14K higher than that for the porous surface without the manifold. The factors limiting performance of the manifold are analyzed. High superheat is attributed to partial flooding of the boiling surface as suggested by the reduction in superheat using external suction. Similar systems and structures for enhanced two-phase cooling are compared.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Mesophase pitch-based high performance carbon fiber production using coal extracts from mild direct coal liquefaction

Mild direct coal liquefaction (autogenous pressure, no catalyst, no H2 gas) of Springfield coal in fluid catalytic cracking decant oil is shown to effectively produce coal extract precursors to spinnable mesophase pitch. Here this work demonstrates that the coal extract can be thermally treated to obtain mesophase pitch in a facile one-step process, bypassing the production of an intermediate isotropic pitch. Furthermore, the presence of 25 wt.% coal in the initial slurry can increase the yield to mesophase pitch nearly twofold and yield to carbon fiber by approximately 70%. The coal extract-derived mesophase pitch was melt-spun and heat treated to produce carbon fiber with graphitic texture, high modulus (>400 GPa) and tensile strength up to 943 MPa. Overall, this work demonstrates that coal can be effectively utilized to markedly amplify the mesophase pitch and carbon fiber yield from fluid catalytic cracking decant oil by relatively simple processing, while conserving utility as a precursor to high performance carbon fiber and potentially other high value graphitic products.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interaction between dissolution and precipitation during olivine carbonation: Implications for CO 2 mineralization

Large-scale carbonation of olivine is considered a promising approach for in situ mineral carbonation, offering a permanent and stable method for CO 2 storage. A critical aspect of this process is understanding how dissolution and precipitation interact, as this could drive fracturing and enhance further reactions. In this study, we conducted carbonation experiments on olivine using CO 2 -saturated aqueous solutions of NaHCO 3 and NaCl. Two experimental setups were used: one representing an open geochemical system and the other a closed system, corresponding to reaction-limited and flow-limited scenarios, respectively. Further, post-reaction textural analysis using scanning electron microscopy (SEM) revealed surface coatings of reaction products in the closed system, while etch pits and etch channels were prevalent in the open system. Although no direct evidence of reaction-driven fracturing was observed, etch pits and etch channels may serve as initiation points for subcritical crack formation and growth, potentially maintaining permeability and exposing new unreacted surfaces. Using linear elastic fracture mechanics (LEFM) model, we estimate that microcracks could propagate under a pressure of 0.1 GPa if reaction products accumulate within the etch pits. Our findings offer new insights into the mechanisms governing olivine carbonation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response

Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural configuration and thereby overall performance, constitutive models of materials with microstructure are needed. In this work we provide neural network architectures that provide effective homogenization models of materials with anisotropic components. These models satisfy equivariance and material symmetry principles inherently through a combination of equivariant and tensor basis operations. We demonstrate them on datasets of stochastic volume elements with different textures and phases where the material undergoes elastic and plastic deformation, and show that the these network architectures provide significant performance improvements.

anisotropy↗