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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Metal hydrides: a historical perspective

Metal hydrides are known for their outstanding performance as materials for hydrogen storage and processing. These materials find applications for short- and long-term energy storage, compression and supply of hydrogen gas, thermal energy storage, as electrodes and electrolytes in rechargeable batteries, for the microstructural optimisation of functional materials, in thin film technologies, as catalysts, getters and in many other uses. After the discovery of the first binary metal hydrides back in the 19th century, their studies covered all possible binary M-H systems and expanded rapidly into the field of ternary hydrides following the recognition of the excellent hydrogen storage performance of LaNi 5 - and TiFe-based materials, which operate efficiently at room temperature and at near-ambient H 2 pressures. This review aims to provide an overview of the early works, as well as selected recent results on various classes of metal hydrides. It also covers the recent activities from the major contributing countries and continents, including USA, Europe, Japan, China and Australia. These studies relate to achieving the hydrogen storage systems goals set by the Department of Energy in the United States which inspired the research activities at the national and international level, through execution of the tasks on hydrogen-based energy storage managed by the International Energy Agency. The review is prepared by international experts in the field and covers the most important past developments and also presents the recent achievements in the field.

08 HYDROGEN↗
DOE OSTI2026

Investigation of irradiation damage and heat deposition: a comparative analysis for HEU-to-LEU conversion in HFIR

The planned conversion of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory from highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel requires detailed evaluation of experiment-relevant parameters to ensure continued performance for materials testing and isotope production. Here, this study presents the first comprehensive assessment of displacements per atom (dpa) and heat deposition rates in target materials within the HFIR flux trap with both HEU and candidate LEU core configurations. Seven analyses were conducted to evaluate key performance metrics, including fast neutron flux distribution, cross section response functions, cross section data, and local dpa and heat deposition rates using mesh- and cell-based tallies. Simulations employed Shift, Monte Carlo N-Particle (MCNP), and the HIFR Controller (HFIRCON) tool suite for high-fidelity transport and depletion modeling. The LEU designs—using U 3 Si 2 -Al dispersion fuel and operating at 95 MW—were compared to the current 85 MW HEU configuration. Results show that while the candidate LEU cores exhibit higher dpa rates due to a harder spectrum and extended cycle lengths, they also demonstrate reduced heat deposition rates in irradiation experiments, primarily due to increased gamma self-shielding from higher 238 U content in the core. These findings confirm that LEU conversion can maintain HFIR’s materials irradiation capabilities but may require redesigning existing experimental hardware.

HEU↗
DOE OSTI2023

Improving Cooperative Interactions Between Halogenated Aromatic Additives and Aromatic Side Chain Acceptors for Realizing 19.22% Efficiency Polymer Solar Cells

Processing additive plays an important role in the standard operation procedures for fabricating top performing polymer solar cells (PSCs) through efficient interactions with key photovoltaic materials. However, improving interaction study of acceptor materials to high performance halogenated aromatic additives such as diiodobenzene (DIB) is a widely neglected route for molecular engineering toward more efficient device performances. Here, in this work, two novel Y-type acceptor molecules of BTP-TT and BTP-TTS with different aromatic side chains on the outer positions are designed and synthesized. The resulting aromatic side chains significantly enhanced the interactions between the acceptor molecules and DIB through an arene/halogenated arene interaction, which improved the crystallinity of the acceptor molecules and induced a polymorph with better photovoltaic performances. Thus, high power conversion efficiencies (PCEs) of 18.04% and 19.22% are achieved in binary and ternary blend devices using BTP-TTS as acceptor and DIB as additive. Aromatic side chain engineering for improving additive interactions is proved to be an effective strategy for achieving much higher performance photovoltaic materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2022

Thermoelectric performance in disordered Cu 2 ZnSnSe 4 nanostructures driven by ultra-low thermal conductivity

Here, high-energy reactive mechanical alloying (ball milling) was used to synthesize tetragonal and cubic polymorphs of Cu 2 ZnSnSe 4 . The ordered tetragonal (I-4) polymorph undergoes a phase transition above 400 K into a Cu-Zn disordered tetragonal (I-42 m) polymorph, while the cubic (F-43 m) polymorph with full cation disorder is stabilized at room temperature. Both polymorphs show ultra-low thermal conductivities, 0.42 W m -1 K -1 at 722 K and 0.21 W m -1 K -1 at 523 K for the disordered tetragonal and cubic phases respectively. The cubic polymorph has a higher zT in the low-temperature range, peaking at 0.26 (523 K), while the disordered tetragonal has a maximum zT of 0.46 at 712 K. The latter is the highest reported zT for stoichiometric Cu 2 ZnSnSe 4 , comparable to the best-performing doped materials in the literature. A combination of experimental results and ab-initio calculations point to a coupling between structural disorder and microstructure as the mechanism behind the reported performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Understanding Microstructure Variability in Vapor-Deposited Energetic Materials by Using Phase-Field Methods

Critical components, such as detonators, in Sandia's stockpile contain heterogeneous materials whose performance and reliability depend on accurate, predictive models of coupled, complex phenomena to predict their synthesis, processing, and operation. Ongoing research in energetic materials has shown that microstructural properties, such as density, pore-size, morphology, and specific surface area are correlated to their initiation threshold and detonation behavior. However, experiments to study these specific characteristics of energetic materials are challenging and time consuming. Therefore, in this work, we turn to mesoscale modeling methods that may be capable of reproducing some observed phenomena to refine and predict outcomes beforehand. Even so, we have no physics-based modeling capability to predict how the microstructure of an energetic material will evolve over near- and long-term time scales. Thus, the goal of this work is to (i) identify any knowledge gaps in how the underlying microstructure forms and evolves during the synthesis process, and (ii) develop and test a mesoscale phase-field model for vapor deposition to capture critical mechanisms of microstructure formation, evolution, and variability in vapor-deposited energetic materials, such as processing conditions, material properties, and substrate interactions. Based on this work, the phase-field method is shown to be a valuable tool for developing the necessary models containing coupled, complex phenomena to investigate and understand the synthesis and processing of energetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Toward a machine-guided approach to energetic material discovery

In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.

42 ENGINEERING↗
DOE OSTI2020

Cellulose nanofibrils and nanocrystals in confined flow: Single-particle dynamics to collective alignment revealed through scanning small-angle x-ray scattering and numerical simulations

Nanostructured materials made through flow-assisted assembly of proteinaceous or polymeric nanosized fibrillar building blocks are promising contenders for a family of high-performance biocompatible materials in a wide variety of applications. Optimization of these processes relies on improving our knowledge of the physical mechanisms from nano- to macroscale and especially understanding the alignment of elongated nanoparticles in flows. Here, we study the full projected orientation distributions of cellulose nanocrystals (CNCs) and nanofibrils (CNFs) in confined flow using scanning microbeam SAXS. For CNCs, we further compare with a simulated system of dilute Brownian ellipsoids, which agrees well at dilute concentrations. However, increasing CNC concentration to a semidilute regime results in locally arranged domains called tactoids, which aid in aligning the CNC at low shear rates, but limit alignment at higher rates. Similarly, shear alignment of CNF at semidilute conditions is also limited owing to probable bundle or flock formation of the highly entangled nanofibrils. Finally, this work provides a quantitative comparison of full projected orientation distributions of elongated nanoparticles in confined flow and provides an important stepping stone towards predicting and controlling processes to create nanostructured materials on an industrial scale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Microstructural Characterization of the Second High Fluence Baffle-Former Bolt Retrieved from a Westinghouse Two-loop Downflow Type PWR

As one of the pressurized water reactor (PWR) internal components, baffle-former bolts (BFBs) are subjected to significant mechanical stress and neutron irradiation from the reactor core during the plant operation. Over the long operation period, these conditions lead to potential degradation and reduced load-carrying capacity of the bolts. In support of evaluating long-term operational performance of materials used in core internal components, the Oak Ridge National Laboratory (ORNL), through the Department of Energy (DOE), Light Water Reactor Sustainability (LWRS) Program, Materials Research Pathway (MRP) has harvested two high fluence BFBs from a commercial Westinghouse two-loop downflow type PWR. The two bolts of interest, i.e. bolts # 4412 and 4416, were withdrawn from service in 2011 as part of a preventative replacement plan. No identification of cracking or potential damage was found for these bolts during their removal in 2011. However, the bolts required a lower torque for removal from the baffle structure than the original torque specified during installation. Irradiation displacement damage levels in the bolts range from 15 to 41 displacements per atom. The goal of this project is to perform detailed microstructural and mechanical property characterization of BFBs following in-service exposures. The information from these bolts will be integral to the LWRS program initiatives in evaluating end of life microstructure and properties. Furthermore, valuable data will be obtained that can be incorporated into model predictions of long-term irradiation behavior and compared to results obtained in high flux experimental reactor conditions. In this report, we present our latest study in FY22 on microstructural characterizations of the second high fluence baffle-former bolt, i.e., bolt # 4412. Analytical electron microscopy and atom probe tomography characterization were performed. The radiation-induced defects in the material add to the large wealth of knowledge for neutron-induced defects in 304/316 grades of stainless steels, specifically for radiation-induced precipitation after high fluence commercial PWR irradiation. The main findings are summarized as follows: 1) The cavity size was considerably larger in the bolt thread section than in the bolt head, with the bolt thread section having a bimodal distribution of cavities greater than ~6 nm in diameter and less than ~3 nm in diameter. The bolt head only had the small-sized cavities. In addition, there was a denuded zone of large cavities near grain boundaries in the thread section of the bolt. 2) Radiation-induced precipitation in the BFB #4412 was highly complex, with the volume fraction, size, and number density of Ni/Si and Cu-rich precipitates depending strongly on the radiation temperature/dose. In many cases, co-precipitates of adjoined clusters were found with Ni/Si-rich precipitates sandwiched between Cu-rich clusters and Mo/Cr/P-rich clusters. 3) Solute segregation out of solution was highest for most solutes in the thread section of the bolt #4412 with the exception of Cu, which experienced more separation out of solution into Curich clusters in the bolt head section. This highlights the difference in the mechanisms for precipitation of Ni/Si clusters, which have the Ni 3 Si phase composition, and precipitation of Cu-rich clusters. 4) There appear to be multiple simultaneous influences that affect the microstructural variation along the length of the bolt that overcomes the ~2X difference in irradiation dose between the bolt head and the bolt thread. The irradiation temperature, thermal/fast neutron ratio variation, potential strain gradient, and exposure to PWR coolant water that each section of the bolt sees may have more influence on the microstructural evolution than the total irradiation dose. The bolt thread and shank, with higher temperature, higher relative fast neutron flux, higher strain, and exposure to coolant but lower dose, underwent more enhanced cavity formation, precipitation, and solute segregation than the bolt head section.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Natural fiber-derived gas diffusion layers for high performance, lower cost PEM fuel cells

The gas diffusion layer (GDL) is a critical component of proton exchange membrane fuel cells. GDL manufacture is dominated by the use of polyacrylonitrile (PAN) fibers, resulting in high production costs. Those costs contribute to the high cost of fuel cell stacks. Thus, reducing GDL manufacturing costs without sacrificing performance, should help reduce the overall cost of the fuel cell stack. Using inexpensive, natural fiber-based papers and fabric as substrates, here we examine the inherent performance these materials as GDLs, and the improvements enabled by the addition of a microporous layer, gas-phase hydrophobic treatment, and densification. The resulting GDLs achieve equivalent performance the commercial baseline GDL and demonstrate the potential of lower cost GDLs.

25 ENERGY STORAGE↗
DOE OSTI2025

Laser powder bed fusion of ODS Fe–Cr–Al (0.3Zr, 0.3Y 2 O 3 ): Unveiling processing-microstructure- mechanical property relationships

Here, this study investigates the fabrication of oxide dispersion strengthened (ODS) Fe-Cr-Al alloys via laser powder bed fusion (LPBF) with strategic additions of 0.3 wt% Zr and 0.3 wt% Y 2 O 3 for enhanced mechanical performance in nuclear applications. Systematic processing parameter optimization yielded three distinct conditions: one low-density product with significant defects and two near-full-density materials with improved consolidation. Comprehensive characterization confirmed single-phase α-ferrite matrix formation with successful incorporation of Y-, Zr-, O-, and C-rich precipitates characteristic of ODS alloys. However, precipitate density remained low (∼10 7 cm −3 ), resulting in sink strength values substantially below optimal levels for radiation resistance. Microhardness values (mid-200s HV) correlated inversely with grain size following the Hall-Petch relationship, indicating grain boundary strengthening as the dominant mechanism rather than precipitation strengthening. The optimized processing conditions achieved excellent mechanical properties with room temperature yield strength of approximately 500 MPa and 30 % elongation, demonstrating superior strength-ductility synergy compared to other additively manufactured ODS materials and performance consistent with literature values for LPBF-processed ODS-FeCrAl alloys. This investigation reveals both the potential and limitations of LPBF processing for ODS Fe-Cr-Al alloys. While successful defect-free fabrication was achieved, results highlight the critical need for systematic optimization of processing parameters and post-processing heat treatments to enhance precipitate density for effective dispersion strengthening and radiation resistance while maintaining additive manufacturing advantages.

Additive manufacturing↗
DOE OSTI2021

New Experiments for Detecting Emissions from Fission

Ensuring that nuclear science and technology is used solely for peaceful purposes is one of the most pressing challenges facing our society. The Consortium for Monitoring, Technology, and Verification (MTV) mission is to help meet this challenge by providing advanced technology and trained talent for careers in nuclear security and nonproliferation. The study of nuclear fission is one area of interest in the MTV. We are performing new experiments for the characterization of neutron and gamma ray emissions from fission fragments. These emissions are signatures for the detection and characterization of nuclear materials. To perform these experiments, we have developed Fission Sphere (FS-3), an array of forty organic stilbene detectors operated in time-coincidence. The FS-3 is used to measure the prompt emissions of neutrons and gamma rays from Cf-252 spontaneous fission. These new data will be used to validate physics-based prediction codes, including CGMF and FREYA, and will be useful in future ENDF and ENSDF evaluations. This paper will present results from the first experiments using FS-3 and a Cf-252 spontaneous fission source. It will compare the experimental results with predictions from theory and Monte Carlo simulation. Specifically, we will describe the correlations among energy, multiplicity, and angles of emitted particles. We will also discuss the application of these correlations in multiplicity counting techniques, which are widely used in nuclear safeguards.

Pozzi, S. A.↗
DOE OSTI2022

Evaluation of crystalline quality of traveling heater method (THM) grown Cd0.9Zn0.1Te0.98Se0.02 crystals

Because of its excellent opto-electronic properties, CdZnTe (CZT) has been the material of choice for x- and gamma-ray detectors operable at room temperature. CZT is the leading commercially available room-temperature radiation detector material today. Although much progress has been made over the past three decades, today's CZT crystals still face certain challenges, especially the presence of the performance-limiting materials defects and the associated relatively high production cost. In this regard, CdxZn1−xTeySe1−y (CZTS) is emerging as a next-generation compound semiconductor, which overcomes some of the limitations of CZT technology for the stated applications. Here, we conducted a study to evaluate the crystalline quality of the traveling heater method grown CZTS with an optimized alloy composition, i.e., Cd0.9Zn0.1Te0.98Se0.02. The as-grown samples were evaluated by low-temperature photoluminescence (PL) spectroscopy and high-resolution x-ray diffraction using the synchrotron light source at Brookhaven National Laboratory. The full width at half maximum of both the PL and x-ray rocking curves was observed to be broadened due to the lattice disorder of the quaternary compound, eventually degrading the crystalline quality. This was consistent with density functional theory calculations.

Roy, U. N. (ORCID:0000000282089680)↗
DOE OSTI2024

Multi-Material Topology Optimization for High Performance Heat Exchangers

This award allowed RTX Technology Research Center to demonstrate several key objectives related to the design and manufacture of additive heat exchangers. The focus of the project was on creating a multi-physics, multi-material topology optimization tool, then using it to create an ultra-compact, optimized, fully additive heat exchanger.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗