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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 1,153 records · Page 64
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NASA NTRS1992

Pulsed thrust propellant reorientation - Concept and modeling

The use of pulsed thrust to optimize the propellant reorientation process is proposed. The ECLIPSE code is used to study the performance of pulsed reorientation in small-scale and full-scale propellant tanks. A dimensional analysis of the process is performed and the resulting dimensionless groups are used to present and correlate the computational predictions of reorientation performance. Based on the results obtained from this study, it is concluded that pulsed thrust reorientation seems to be a feasible technique for optimizing the propellant reorientation process across a wide range of spacecraft, for a variety of missions, for the entire duration of a mission, and with a minimum of hardware design and qualification.

Hochstein, John I.↗
DOE OSTI2025

A New Family of Ternary Intermetallic Compounds with Dualistic Atomic Ordering – The ZIP Phases

A new family of nanostructured ternary intermetallic compounds − named the ZIP phases − is introduced in this work. The ZIP phases exhibit dualistic atomic ordering, i.e., they form two structural variants: one with the fcc diamond cubic structure (space group Fd$\bar{3}$m) and one with the hexagonal structure (space group P6 3 /mmc). They are also characterized by metallic behavior, ionic bonding, and atomic zigzagging. Powder metallurgical routes involving pressure-assisted densification are adopted to demonstrate ZIP phase synthesis in the Nb-Si-Ni, Nb-Si-Co, Ta-Si-Ni, V-Si-Ni, and Nb-Si-Fe ternary systems. Crucially, reactive hot pressing is capable of producing high-purity ZIP phase materials after the judicious, elemental system-specific optimization of the processing route. Synthesis of phase-pure materials – demonstrated in the Nb-Si-Ni ternary system by the synthesis of quasi phase-pure Nb 3 SiNi 2 and Ni 3 SiNb 2 ZIP phase-based materials – is a steppingstone to the prospective exploitation of the ZIP phases. Characterization of Nb 3 SiNi 2 and Ni 3 SiNb 2 involves crystal structure determination, spatially resolved chemical analysis, and determination of select thermal, electrical, magnetic, mechanical, and physical properties. Density functional theory is used to assess the stability of Nb 3 SiNi 2 & Ni 3 SiNb 2 and derivative binary compounds at different temperatures, also exploring the exfoliation of these two ZIP phases along specific surfaces to produce 2D derivatives.

Intermetallic compounds (IMCs)↗
DOE OSTI2026

Accelerating laser ray tracing in high fidelity physics simulations of laser melting using squeeze U-net

Laser melting is a core component of the ongoing industrial revolution, dubbed Industry 4.0, as lasers facilitate fast and precise melting and fusion in advanced manufacturing. There is a strong need to optimize the laser process using simulations. However, this has proven challenging as high fidelity simulations are needed for predictive modeling and this is currently prohibitively expensive even when run on hundreds of processors on high performance computers. The challenge is capturing complex physics of laser material interaction, fluid dynamics, thermal physics and material phase transformations at various length and time scales. To close this technological gap, we modified a squeeze U-net to accelerate the laser ray tracing component of such high fidelity models by ~4x–40x while preserving the core physics principle of conservation of energy with 97% accuracy. This approach enables the accurate modeling of global laser energy absorption as a function of local surface temperatures and complex surface topologies, which govern the reflection directions and energy losses of laser rays upon interacting with the material surface.

Computer science↗
DOE OSTI2025

A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion predictions. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional (3D) plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source.

3D temporal sequences↗
DOE OSTI2025

From bench to biofactory: high-throughput technologies and automated workflows to accelerate biomanufacturing

Microbial production of target molecules has advanced significantly in recent years driven by innovations in enzyme engineering, DNA synthesis, and genomic editing. However, to access the massive potential of microbial production, a vast parametric space remains to be investigated to optimize these biobased processes for a robust bioeconomy. Here, we review the current state of the art, some key challenges and possible solutions. We see a critical role of automation, high-throughput technologies, self-driving and cloud labs, and data management to enable Artificial Intelligence/Machine Learning and mechanistic models to overcome the design space challenges and accelerate the development of novel bio-based solutions. Accurate models will expedite the development and scale-up of engineered microbes for a range of final products from many starting materials.

Petzold, Christopher J↗
DOE OSTI2026

Quantifying market volume sensitivity to material property modifications in polyhydroxybutyrate: A parametric analysis approach

Polyhydroxybutyrate (PHB), a biodegradable biopolymer, represents a promising alternative to petroleum-based thermoplastics. However, despite consistent market growth, PHB faces persistent commercialization challenges that limit widespread adoption. Existing research has focused predominantly on optimizing PHB production processes, leaving a critical gap in understanding which material property modifications would most effectively enhance market competitiveness. This study addresses this gap by systematically analyzing the relationship between polymer material properties and market performance using U.S. market data from 2008 to 2021 for 21 thermoplastic polymers across 19 material properties. We employed principal component regression to identify property modifications that could maximize market volume while reducing CO 2 emissions. Our parametric analysis revealed that two specific material properties – Hardness Shore A and Sheet Extrusion Temperature – significantly influence PHB marketability across different price points. Market simulations demonstrated that a 10% increase in Hardness Shore A could increase PHB market volume by 431.5 million kg while reducing emissions by 188.7 kg CO 2 . A similar 10% increase to Sheet Extrusion Temperature could yield a 297.5 million kg volume increase and a 99.2 kg CO 2 reduction in emissions. Critically, this approach is agnostic to the specific methods required to achieve these property changes, instead providing material scientists with quantitative, data-driven targets for R&D prioritization. Here, this framework offers a novel methodology for evaluating biopolymer competitiveness and supporting strategic decisions to accelerate PHB market adoption and contribute to decarbonization of the plastics industry.

09 BIOMASS FUELS↗
DOE OSTI2026

Electrochemical behavior of SnCl 2 and influence of Cu and Ni ions in molten LiCl−KCl−CaCl 2 eutectic

Reliable transport and thermodynamic data for multivalent ions in complex molten salts are scarce, limiting model fidelity for electrorefining and impurity control. Here, we report a comprehensive electrochemical characterization of SnCl₂ in LiCl–KCl–CaCl₂ (50.5–44.2–5.3 mol%) at 685 K, including the effects of Ni 2+ and Cu + impurities. Using cyclic voltammetry (CV), chronoamperometry (CA), and chronopotentiometry (CP), we quantified Sn 2+ and Ni 2+ diffusion with exceptional agreement across methods: Sn 2+ averaged (1.03 ± 0.10) × 10 −5 cm 2 s −1 , and Ni 2+ averaged (0.75 ± 0.19) × 10 −5 cm 2 s −1 . The tight confidence-interval overlap across CV, CA, and CP strengthens confidence in these values and is uncommon in molten chloride studies. Open-circuit-potential measurements provided standard apparent reduction potentials that closely match LiCl–KCl literature, indicating minimal shift with CaCl₂ present. The Sn 2+ /Sn couple behaves as a reversible two-electron soluble–insoluble process at 685 K; the Sn 4+ /Sn 2+ couple transitions to soluble–soluble behavior near 788 K, which may correlate with the decomposition of surface bound chlorostannates, though direct characterization remains to be established. In mixed systems, Cu+/Cu overlaps Sn 2+ /Sn, limiting Cusingle bondSn electroseparation, whereas the larger potential gap between Ni 2+ /Ni and Sn 2+ /Sn supports selective Ni removal. These internally consistent transport and thermodynamic data establish a validated basis for process modeling and optimization of Sn electrorefining and impurity management in LiCl–KCl–CaCl₂.

Berzins-Delahay↗
DOE OSTI2026

Circadian immunometabolic states impart a temporal response to SARS-CoV-2 spike proteins in mammalian macrophages

Circadian rhythms, the 24-hour cycles that tune organismal physiology to the daily rhythms of light and dark, optimally organize cellular processes such as metabolism and mitochondrial function. In mammals, macrophage functions are regulated by these 24-hour circadian rhythms such that the immunometabolic response is coordinated across the day, consolidating macrophage physiology into temporally distinct phases to time the cellular immune response. However, while it is known that there are time-of-day specific responses to stress in a macrophage, little has been done to determine if circadian regulation coordinates the response of a macrophage to real-world pathogens. Importantly, key proteins in the response to viral infection have been found to be under circadian control, and time of day of application is known to affect the efficacy of vaccinations, including in the case of the COVID-19 virus. Therefore, to investigate if the circadian regulation of macrophage physiology imparted a time-of-day response to viral exposure, we exposed primary mouse and human macrophages to the SARS-CoV-1 and CoV-2 spike proteins at different times over the circadian day. To establish a time-of-day effect, we performed a multi-omics analysis and in vitro tissue culture assays examining macrophage responses over circadian time. We found that, conserved across the species, the timing of spike protein exposure dictated two distinct temporal responses which were characterized by hallmarks of immunometabolic suppression and modest inflammatory activation. However, these responses were primarily influenced by central metabolic and mitochondrial changes and not by classical immune activation.

Circadian Biology↗
DOE OSTI2026

Influence of the as-built microstructure on the recrystallization of an additively manufactured Inconel939 Ni-based superalloy

This study investigates the influence of the as-built microstructure on the recrystallization (RX) behavior and mechanical properties of the Ni-based superalloy Inconel 939 produced by laser powder bed fusion (PBF-LB/M). Two distinct as-built microstructures were obtained by varying the hatch distance (h d ): a columnar, strongly textured condition (h d =50, termed h d 50) and an equiaxed, weakly textured condition (h d =70, termed h d 70)). Both were subjected to nine solution treatments combining three temperatures (1100, 1150, and 1200 °C) and three holding times (1, 4, and 8 h). Comprehensive microstructural characterization was conducted to assess grain morphology, texture, grain boundary character, dislocation density, and precipitate distribution. Recrystallization was found to be significantly slower than in cast counterparts, requiring higher temperatures and longer times for completion. The initial microstructure plays a decisive role: full RX was achieved only in hd70 specimens after treatment at 1200 °C for 8 h, whereas hd50 samples exhibited delayed and incomplete RX under identical conditions. This behavior is attributed to the finer grain size and higher fraction of high-angle grain boundaries in hd70, which promote recrystallization. Mechanical testing revealed that hd70 samples subjected to a 1200 °C/8 h treatment followed by standard double ageing show higher yield and tensile strengths across the investigated temperature range than both printed and cast Inconel939 processed under conventional conditions, albeit with slightly reduced ductility. The enhanced mechanical performance is attributed to the larger grain size, which limits grain boundary sliding. These results demonstrate the critical importance of controlling the as-built microstructure and tailoring post-processing strategies to optimize high-temperature performance of PBF-LB/M Inconel939.

Inconel939↗
DOE OSTI2025

Processing-dependent chemical ordering in Cu 3 Au characterized via non-destructive Bragg coherent diffraction imaging

Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. Here, in this study, Bragg coherent diffraction imaging is used to reconstruct morphology and lattice displacement in model Cu 3 Au nanocrystals that have undergone different heat treatments to produce variation in chemical ordering. The magnitudes and distributions of the scattering amplitudes (proportional to electron density) and lattice strains within these crystals are then analyzed to correlate them to the expected amount of chemical ordering present. Nanocrystals with increased amounts of ordering are found to generally have less extreme strains present and reduced strain distribution widths. In addition, statistical correlations are found between the spatial arrangement of scattering amplitude and lattice strains.

Warren, Nathaniel [Pennsylvania State Univ., Unive↗
DOE OSTI2024

Solvent-Dependent Dynamics of Cellulose Nanocrystals in Process-Relevant Flow Fields

Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mechanistic Insights for Plasma-Catalytic CO 2 Reduction over TiO 2 in a Dielectric Barrier Discharge Reactor

Reaction kinetics experiments coupled with phenomenological kinetic modeling and parameter estimation are used to elicit insights into the mechanism and active sites for the plasma-catalytic dissociation of CO 2 on TiO 2 . Experimental and model insights showed that gas-phase reactions contribute at least two-thirds of the overall product formation at explored conditions; weak temperature dependence, strong sensitivity to specific energy input (SEI), apparent first order in CO 2 , and positive influence of cofed argon (Ar) and oxygen (O 2 ) for the gas-phase contributions all suggest that expected plasma reaction steps such as electron-impact and high-energy collisions are the dominant modes for CO 2 dissociation. The Arrhenius-like expression for gas contributions resulted in a preexponential of 4.40 × 10 –3 s –1 , an E SEI,g of 7.90 × 10 –4 mol/kJ, and an E a,g of 1.00 × 10 –3 J/mol. For surface contributions, the small apparent barrier of 16.3 kJ/mol, relatively weaker dependence on SEI, first-order dependence on CO 2 , and insensitivity to cofed Ar and O 2 all point to CO 2 dissociation on TiO 2 surface facets without vacancies and aided by plasma (leading to vibrationally excited CO 2 and/or a reactive surface with significant surface charge accumulation). The Arrhenius-like expression resulted in a preexponential of 7.81 × 10 –2 s –1 , an E SEI,s of 1.90 × 10 –3 mol/kJ, and an E a,s of 1.63 × 10 4 J/mol. The derived kinetic model further enabled a systematic evaluation of the effect of inputs (plasma power, flow rate, CO 2 inlet concentration, and temperature) to identify process trends and optimal operating conditions.

catalyst↗
DOE OSTI2025

DeSelenator: A Se-Removal Process for Environmental Decontamination of Wastewaters from Coal-Burning Power Plants

Selenium may become a toxic contaminant of freshwater systems when released into the environment through industrial wastewaters from mining, coal-burning power plants, or oil refining. Efficient and cost-effective Se-removal technologies are therefore necessary to reduce Se concentrations in these wastewaters to below the regulatory discharge limits. In this study, we have demonstrated an effective process that removes Se, mostly as selenate anions, from wastewaters generated by coal-burning power plants. This process, dubbed DeSelenator, leverages the high concentration of sulfate relative to selenate in the wastewater and the propensity of these oxyanions to cocrystallize with benzene-bis-iminoguanidinium (BBIG) cations into extremely insoluble salts (on par with BaSO 4 ). The SO 4 2− /SeO 4 2− cocrystallization with BBIG removes over 90% of S and Se from the wastewater. Following removal of the precipitate by filtration, the filtrate is passed over an anion-exchange resin that further reduces selenium concentration to 5 ppb, the EPA’s regulatory limit for freshwater systems. Finally, the effluent is passed over an activated carbon column, which removes 99.8% of the residual BBIG ligand remaining after crystallization, allowing for the safe discharge of the treated water into the environment. The Se-removal process was first optimized in the lab at the bench scale and then tested in the field at the Tennessee Valley Authority’s Bull Run coal-burning power plant. A technoeconomic assessment found the cost of water treatment with DeSelenator is on par with that of the active biological method, which is currently considered a state-of-the-art Se-removal technology.

anions↗
DOE OSTI2026

Probing multi-dimensional composition spaces in search of strong metallic alloys

Refractory complex concentrated alloys (RCCA) offer exceptionally high-temperature strength compared to pure metals and dilute alloys, but predictive theory for RCCA design is lacking. We present large-scale molecular Dynamics (MD) simulations of crystal plasticity to explore alloy compositions for maximum mechanical strength, focusing on Fe-Ta-W and Nb-Ta-Mo-W alloy families modeled with Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP). To efficiently guide the search for strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many simulated RCCA compositions exhibit pronounced cocktail strengthening, with strengths surpassing their strongest constituent metal, tungsten. Contrary to expectations, the highest strength is found on binary edges of the RCCA composition space. Detailed analyses of atomistic simulations reveal that, similar to pure BCC metals, plastic response in RCCA is primarily governed by screw dislocations. However, at large strains, dislocation multiplication and interactions (Taylor hardening) become the dominant mechanisms contributing to RCCA strength.

Materials science↗
DOE OSTI2024

Discovering tungsten-based composites as plasma facing materials for future high-duty cycle nuclear fusion reactors

Abstract Despite of excellent thermal properties and high sputtering resistance, pure tungsten cannot fully satisfy the requirements for plasma facing materials in future high-duty cycle nuclear fusion reactions due to the coupled extreme environments, including the high thermal loads, plasma exposure, and radiation damage. Here, we demonstrated that tungsten-based composite materials fabricated using spark-plasma sintering (SPS) present promising solutions to these challenges. Through the examination of two model systems, i.e., tungsten-zirconium composite for producing porous tungsten near the surface and dispersoid-strengthened tungsten, we discussed both the strengths and limitations of the SPS-fabricated materials. Our findings point towards the need for future studies aimed at optimizing the SPS process to achieve desired microstructures and effective control of oxygen impurities in the tungsten-based composite materials.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Upgrading carbon monoxide to bioplastics via integrated electrochemical reduction and biosynthesis

It is challenging to obtain high-value hydrocarbons that are longer than C 3 via electrochemical CO 2 /CO reduction. Integrating electrochemical CO 2 /CO electrolysers with a downstream bioreactor is one solution for obtaining high-value long-chain products, but the electrolytes in these two systems are mismatched, preventing smooth integration. Furthermore we demonstrate a porous solid electrolyte reactor that produces highly selective and electrolyte-free acetate and couple it with a biosynthesis system for generating C 4+ polyhydroxybutyrate bioplastic. A finely tuned electrolyte containing biocompatible salt medium with acetate can be directly injected into the downstream bioreactor without any separation or salt-mixing processes. In the optimized coupled platform, Ralstonia eutropha bacteria can grow with acetate generated from the CO electrocatalytic reduction reactor, and produce bioplastic as the final value-added product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine Learning for Optimized Polarization at Jefferson Lab

Polarized cryo-targets and polarized photon beams are widely used in experiments at Jefferson Lab. Traditional methods for maintaining the optimal polarization involve manual adjustments throughout data taking by human shift takers. This may introduce some level of inconsistency simply due to the wide variety of experience and expertise of the shift takers themselves. Implementing machine learning-based control systems can improve the stability of the polarization without relying on human intervention. The cryo-target polarization is influenced by temperature, microwave energy, the distribution of paramagnetic radicals, as well as operational conditions including the radiation dose. Diamond radiators are used to generate linearly polarized photons from a primary electron beam. The energy spectrum of these photons can drift over time due to changes in the primary electron beam conditions and diamond degradation. As a first step towards automating the continuous optimization and control processes, uncertainty aware surrogate models have been developed to predict the polarization based on historical data. This talk will provide an overview of the use cases and models developed, highlighting the collaboration between data scientists and physicists at Jefferson Lab.

Jeske, Torri [Thomas Jefferson National Accelerato↗
DOE OSTI2026

Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning

Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗