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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Comparative study on the formation of Cr and Ti ohmic contacts to (001) β -Ga 2 O 3

Here, a comparative study of Cr/Au and Ti/Au ohmic contacts on (001) β-Ga 2 O 3 was conducted. The electrical behavior from current-voltage measurements and the interfacial composition and microstructure as determined from high-resolution transmission electron microscopy (TEM) with energy dispersive x-ray analysis were compared for the different contacts at selected points in an annealing series (300–700 °C, 1 min. anneals in N 2 ). Cr/Au contacts became ohmic at temperatures (300–350 °C) approximately 50–100 °C lower than Ti/Au contacts (400–450 °C). Cr/Au and Ti/Au contacts demonstrated optimal ohmic behavior (lowest resistance) when annealed to 450–500 and 500–600 °C, respectively, with Ti/Au contacts yielding a lower total resistance than Cr/Au. Cross-sectional TEM images of Cr/Au contacts annealed at 450 °C revealed the presence of Au nanoclusters at the Ga 2 O 3 interface and CrO x layers at both the top of the contact and the Ga 2 O 3 interface. Whereas TiO x also formed at the top and bottom interfaces in 450 °C-annealed Ti/Au contacts, the TiO x surface layer appeared to be variable in thickness and/or discontinuous, unlike the CrO x surface layer. Au nanoclusters were not detected at the interface in the Ti/Au contacts. The interdiffusion and oxidation observed in both contact metallizations point to the need for diffusion barriers that may allow these contacts to be used in future Ga 2 O 3 -based devices that operate at elevated temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Stirred-Reactor Coupon Analysis: An International Round Robin Study

The objective of this task was to determine the precision of the SRCA technique when used to determine the dilute condition corrosion rate. To this end, an interlaboratory round robin study was conducted per the instructions in ASTM Practice E691 to measure the precision with which the SRCA test method can be conducted. Twelve independent labs from eleven different institutions each evaluated four glass compositions in three different conditions. The ASTM procedures recommend at least 6 labs participate in a round robin testing the same 3 materials in the same conditions to determine precision. In this case, 12 labs each performed 12 independent tests. This was only possible thanks to the multi-glass testing capability of the SRCA test. A total of 108 duplicate pairs were used to calculate the repeatability of the tests, with the same glass tested in the same vessel, producing as identical conditions as possible for the replicates. These test results were quite tightly clustered, with a median difference from the average value of the pair of only 2.9%. Based on the calculations outlined in ASTM E177-20 and a measured standard deviation of 4.74%, the intralaboratory repeatability limit (r) was calculated to be within 13.3% of the expected value with 95% confidence level. The reproducibility limit (R) of the test was examined using all 277 data points from the round robin. Because of the differences in dissolution rates due to pH variability and intrinsically for the 12 conditions tested, the reproducibility limits for each condition and overall were calculated from the percent relative residual value for each test. The SRCA test is expected to be reproducible within 64% of the expected value with a 95% confidence level.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2022

Preparation of: I. intercalative metal oxide/conductive polymer composites as electrode materials for rechargeable batteries; II. sodium rich manganese oxide hydrate with capacity for aqueous Na-ion electrochemical energy storage

The present invention is directed at intercalative metal oxide/conductive polymer composites suitable for use as electrode materials for rechargeable batteries. The composites can be prepared by agitation of the metal oxide and the conductive polymer in aqueous media. The present invention is also directed at a sodium rich layered manganese oxide hydrate prepared by annealing manganese (II, III) oxide and sodium hydroxide. The sodium rich manganese (III, IV) oxide so formed indicates an enhanced capacity for Na-ion storage suitable for the use of electrode materials for aqueous energy storage.

Xiaowei, Teng↗
DOE OSTI2024

Cellulosic gels, films and composites including the gels, and methods of forming same

Disclosed are cellulose-based 5 flexible aerogels and xerogels containing bacterial cellulose nanorods, ribbons, fibers, and the like, wherein the gels have tunable optical, heat transfer, and stiffness properties. Further disclosed are highly transparent and flexible cellulose nanofiber-polysiloxane composite aerogels featuring enhanced mechanical robustness, tunable optical anisotropy, and low thermal conductivity.

Smalyukh, Ivan I.↗
DOE OSTI2023

Material and component developments for the DEMO divertor using fibre reinforcement and additive manufacturing

Within the research along the European Fusion Roadmap, water-cooled divertor PFCs are foreseen in the design of a first fusion demonstration power plant (DEMO) in order to provide reliable heat removal capability. In the frame of this concerted attempt, the Max Planck Institute for Plasma Physics is concentrating on the development and testing of composite materials based on tungsten (W, preferred armour material) and copper (Cu, preferred heat sink material). W fibres (W f ) as monofilaments and yarns as reinforcement play a central role in these investigations due to their extraordinary properties concerning ductility already at room temperature and high tensile strength. Recent investigations on the impact of radiation damage suggest that the fibres retain their ductility upon irradiation. W reinforced with W fibres (W f /W) allows to overcome the intrinsic brittleness of W. Quantitative mechanical fracture tests of W f /W confirm the basic mechanisms of fibre reinforcement and the increased resistance to mechanical fatigue. The good wettability of W with liquid Cu and the absence of any metallurgical solubility make up an ideal material pairing for composite production. W fibre-reinforced Cu (W f /Cu) cooling tubes provide a rather high thermal conductivity (> 250 W mK -1 ) and at least twice the strength of CuCrZr in hoop direction in the temperature range up to at least 500 °C. Very recent neutron irradiation experiments confirm the sustainment of ductility of the W f /Cu composite. Numerical simulations suggest that thermal stresses in W-Cu PFCs could be strongly reduced by tailoring the local W and Cu volume fraction. This 'freely' distributed material composition can be achieved by means of additively manufactured W skeletons consecutively infiltrated by Cu. Investigations with W preforms produced by Laser Beam Powder Bed Fusion and infiltrated by Cu demonstrate the feasibility of this approach while testing of specifically prepared specimen is ongoing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Processing Temperature Effect on Failure Behavior of Unidirectional Thermoplastic Polymer-fiber-reinforced Polymers (PFRPs)

This study aimed to examine the influence of processing temperature on the mechanical and morphological characteristics of unidirectional (UD) thermoplastic polymer-fiber-reinforced polymers (PFRPs), utilizing UHMWPE-polymer-fiber-reinforced HDPE composites as an example. To achieve this objective, UD thermoplastic PFRPs were produced through filament winding and hot pressing techniques, employing five distinct processing temperatures within the range spanning from the melting onset temperature of the polymer matrix to the melting peak temperature of the reinforcing polymer fiber. By conducting the uni-axial tensile tests on the UD UHMWPE-polymer-fiber-reinforced HDPE composites fabricated at different processing temperatures, it was shown that the optimal longitudinal tensile properties of the composites, comparable with glass-fiber-reinforced thermoset or thermoplastic polymers (GFRPs or GFRTPs) can be achieved when the processing temperature does not significantly exceeds the melting onset temperature of the reinforcing polymer fiber. However, when the processing temperature exceeds this threshold, the mechanical properties of the composites are significantly reduced, as evidenced by the transition in the failure morphology from the presence of significant splitting cracks to the occurrence of plastic necking due to the complete melting of the reinforcing polymer fibers in a composite. Particularly, the ductile behavior of the optimal thermoplastic PFRPs investigated in this study is superior than that of carbon-fiber-reinforced thermoset or thermoplastic polymers (CFRPs or CFRTPs) and even GFRPs and GFRTPs. This study not only provides valuable insights into the proper fabrication of high-performance thermoplastic PFRPs but also offers useful experimental data that can aid in the validation and development of computational models, particularly those related to processing modeling.

Qiao, Yao↗
DOE OSTI2022

Optimizing Piezoelectric Nanocomposites by High‐Throughput Phase‐Field Simulation and Machine Learning

Abstract Piezoelectric nanocomposites with oxide fillers in a polymer matrix combine the merit of high piezoelectric response of the oxides and flexibility as well as biocompatibility of the polymers. Understanding the role of the choice of materials and the filler‐matrix architecture is critical to achieving desired functionality of a composite towards applications in flexible electronics and energy harvest devices. Herein, a high‐throughput phase‐field simulation is conducted to systematically reveal the influence of morphology and spatial orientation of an oxide filler on the piezoelectric, mechanical, and dielectric properties of the piezoelectric nanocomposites. It is discovered that with a constant filler volume fraction, a composite composed of vertical pillars exhibits superior piezoelectric response and electromechanical coupling coefficient as compared to the other geometric configurations. An analytical regression is established from a linear regression‐based machine learning model, which can be employed to predict the performance of nanocomposites filled with oxides with a given set of piezoelectric coefficient, dielectric permittivity, and stiffness. This work not only sheds light on the fundamental mechanism of piezoelectric nanocomposites, but also offers a promising material design strategy for developing high‐performance polymer/inorganic oxide composite‐based wearable electronics.

42 ENGINEERING↗
DOE OSTI2024

Tribological behavior of hybrid Aluminum-TiB 2 metal matrix composites for brake rotor applications

Here, this research investigates the feasibility of hybrid aluminum metal matrix composites (MMC) incorporating TiB 2 particles for brake rotor applications. The composites were produced by incorporating both in-situ, submicron-sized TiB 2 particles and regular micron-sized TiB 2 powders via stir and squeeze casting techniques into A206 aluminum alloy matrix. Systematic adjustments in the fractions of in-situ and ex-situ TiB 2 particles were conducted to evaluate their impact on wear behavior and mechanisms. Combination of both particle types allowed composites with up to 10 vol% of reinforcements. Composites with higher proportions of ex-situ particles demonstrated increased wear resistance compared to those solely composed of in-situ particles, control specimens without TiB 2 , and conventional cast iron counterparts. The lowest wear rate for the hybrid composites sliding against phenolic brake pads was 1.1 × 10 -5 mm 3 /Nm, signifying a 3-fold reduction relative to cast iron sliding against the same pads. Wear analysis elucidated distinctive mechanisms within the hybrid composites, characterized by mild fragmented abrasive wear, adhesive wear, and plastic deformation, accompanied by the formation of an intermixed tribo-oxide layer.

42 ENGINEERING↗
DOE OSTI2024

Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation

The United States Department of Energy is responsible for managing the legacy nuclear waste stored in underground tanks at the Hanford Site. The waste will be separately vitrified as low-activity waste and high-level waste fractions. Waste glass formulation algorithms have been traditionally developed using partial quadratic mixture property-composition models. Recently, machine learning (ML) techniques have been used to predict glass properties and discover new glass materials for nuclear waste vitrification, and these advancements can be utilized to improve waste glass composition design. In this proof-of-principle study, ML algorithms such as Gaussian process regression (GPR) were used to interpolate glass properties (e.g., viscosity, electrical conductivity, chemical durability). After selecting appropriate sets of GPR hyper-parameters for each property, an optimization program was developed to formulate glass compositions to maximize waste loading while simultaneously satisfying property within constraints. The results of the ML-based waste loadings and glass compositions were compared to those obtained using the traditional methods. Comparing to the previous glass design framework, the ML-based optimization methods offer improved glass designs and a streamlined approach to generation of optimally designed data and near real-time updates.

glass formulation, machine learning, constraints, ↗
DOE OSTI2020

In Situ Structural and Electrical Conductivity Characterization of Sr2MMoO6-δ Double Perovskite Solid Oxide Fuel Cell Anode Materials

In this study, a series of Sr 2 MMoO 6-δ double perovskite solid oxide fuel cell (SOFC) anode materials (where 0 <= δ <= 0.041 and M = Fe, Co, or Ni) were synthesized, and the changes in their morphologies under relevant SOFC operating conditions were explored. Ultra-small-angle X-ray scattering (USAXS), small-angle X-ray scattering (SAXS), and wide-angle X-ray scattering (WAXS) were used to determine changes in the microstructures and phase compositions of the anode materials at high temperatures and under a reducing atmosphere. The stability of the double perovskite structure was found to be highly dependent on the identity of the cation M, such that when M = Fe, the material remained stable over the course of the experiments. However, when M = Co or Ni, significant changes in the microstructure and phase composition were observed. An in situ study of the M = Co sample, in which electrical conductivity and USAXS/SAXS/WAXS measurements were conducted simultaneously, revealed the structural degradation mechanisms and electrical conductivity changes over a range of temperatures. To conduct these measurements, a cell was developed that allowed for a sample mounted on Pt wires to be placed in the X-ray beam and heated under gas flow comprising 4% mass H 2 and 96% mass N 2 . The resulting measurements allowed for the direct comparison of the electrical and morphological changes occurring in the material under operating conditions, such that increases in conductivity could be attributed to the growth of new phases.

25 ENERGY STORAGE↗
DOE OSTI2022

Ultra‐Thin Single‐Particle‐Layer Sodium Beta‐Alumina‐Based Composite Polymer Electrolyte Membrane for Sodium‐Metal Batteries

Inorganic/organic composite polymer electrolytes (CPEs) with good flexibility and electrode contact have been pursued for solid-state sodium-metal batteries. However, the application of CPEs for high energy density solid-state sodium-metal batteries is still limited by the low Na + conductivity, large thickness, and low ion transference number. Herein, an ultra-thin single-particle-layer (UTSPL) composite polymer electrolyte membrane with a thickness of ≈20 µm straddled by a sodium beta-alumina ceramic electrolyte (SBACE) is presented. A ceramic Na + -ion electrolyte that bridges or percolates across an ultra-thin and flexible polymer membrane provides: 1) the strength and flexibility from the polymer membrane, 2) excellent electrolyte/electrode interfacial contact, and 3) a percolation path for Na + -ion transfer. Owing to this novel design, the obtained UTSPL-35SBACE membrane exhibits a high Na+-ion conductivity of 0.19 mS cm -1 and a transference number of 0.91 at room temperature, contributing to long-term cycling stability of symmetric sodium cells with a small overpotential. The assembled quasi-solid-state cell with the as-prepared UTSPL-35SBACE membrane displays superior cycling performance with a discharge capacity of 105 mAh g -1 at 0.5 °C rate after 100 cycles and excellent rate performance (82 mAh g -1 at 5 °C rate) at room temperature with the potassium manganese hexacyanoferrate (KMHCF)@CNTs/CNFs cathode, where KMHCF refers to potassium manganese hexacyanoferrate.

25 ENERGY STORAGE↗
DOE OSTI2022

Failure evaluation of neutron-irradiated SiC/SiC composites by underwater acoustic emission

Here, understanding the statistical properties of mechanical properties of non-irradiated and neutron-irradiated SiC/SiC composites is essential for component design. This study aims to evaluate the detailed damage accumulation behavior of composites focusing on two fracture parameters: proportional limit stress (PLS) and acoustic emission (AE) onset stress. The developmental underwater AE technique, which is benefit in non-contact in-situ failure monitoring method during mechanical testing and in handle of the irradiated material, was first applied to evaluate damage accumulation behavior. Two types of chemical vapor infiltration SiC/SiC composites were used: one reinforced with Hi-Nicalon Type-S SiC fiber and one reinforced with Tyranno-SA3 SiC fiber in the form of satin-woven 2D fabrics with pyrolytic carbon interface. Neutron irradiation in the High Flux Isotope Reactor at Oak Ridge National Laboratory reached a fluence of 30 dpa at a temperature of 620–670°C. Four-point flexural tests were conducted to evaluate post-irradiation strength. Weibull statistics did not suggest marked degradation of composite strength. Detailed failure behavior evaluated by AE demonstrated no irradiation-induced change of the AE onset stress (i.e., crack initiation equivalent stress). Failure probability analysis suggests that increasing the reliability of composites (i.e., the Weibull modulus rather than strength itself) is essential to expanding the design margin and benefiting from a probabilistic design approach.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Unusual electrical conductivity driven by localized stoichiometry modification at vertical epitaxial interfaces

Precise control of lattice mismatch accommodation and interfacial cation diffusion is critical to modulate correlated functionalities in epitaxial heterostructures, particularly when the interface composition is positioned near a compositional phase transition boundary. Here we select La 1-x Sr x MnO 3 (LSMO) as a prototype phase transition oxide and establish vertical epitaxial interfaces with NiO for exploring the strong interplay between strain accommodation, stoichiometry modification, and localized electron transport across the interface. It is found that localized stoichiometry modification overcomes the dead layer problem plaguing LSMO and leads to strongly directional conductivity, as manifested by a more than three orders of magnitude difference between out-of-plane and in-plane conductivity. Comprehensive atomic scale structural characterization and transport measurements reveal that this emerging behavior is created by a compositional change produced by preferential cation diffusion that pushes the LSMO phase transition from insulating into metallic within an ultrathin interface region. This study explores the nature of unusual electrical conductivity at vertical epitaxial interfaces and highlights the beneficial role of controllable cation diffusion that enables emerging functionalities for a broad range of potential applications such as memristors, spintronic devices, and novel nanoelectronic devices using strongly correlated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Additive manufacturing of LiCoO 2 electrodes via vat photopolymerization for lithium ion batteries

Additive manufacturing has the potential to revolutionize the fabrication of lithium-ion batteries for a diversity of applications including in portable, biomedical, aerospace, and the transportation fields. Standard commercial batteries consist of stacked layers of various components (current collectors, cathode, anode, separator and electrolyte) in a two-dimensional manner. By leveraging the latest advances in additive manufacturing and computer-aided design, an improved geometric and electrochemical configuration of these batteries can maximize energy efficiency while allowing design optimization to reduce dead space for a given application. In this work, a composite UV photosensitive resin was prepared and used as feedstock in a vat photopolymerization system. The resin was loaded with LiCoO 2 acting as electrochemically active material for the cathode of a lithium-ion battery, and was further improved with the addition of conductivity-enhancing carbonaceous additives. Challenges to additive manufacturing arise from the opacity and high viscosity of the composite nature of these electrochemically-active resins, which cause light refraction during selective UV curing. Subsequently, items were printed and subjected to a thermal post-processing step to obtain an adequate compromise between electrochemical performance and mechanical integrity. Both sintered and green state 3D printed cathodes were assembled into half-cell lithium-ion batteries using lithium metal as a reference and counter electrode. Electrochemical cycling of these batteries yielded satisfactory results approaching commercial LiCoO 2 cathodes’ performance, with the potential advantages of additive manufacturing – high surface area anode–cathode configurations for power performance as well as shape conformability.

25 ENERGY STORAGE↗
DOE OSTI2023

Tectonic and geological setting influence hot spring microbiology

Hydrothermal systems form at divergent and convergent boundaries of lithospheric plates and within plates due to weakened crust and mantle plumes, playing host to diverse microbial ecosystems. Little is known of how differences in tectonic setting influence the geochemical and microbial compositions of these hydrothermal ecosystems. Here, for this work, coordinated geochemical and microbial community analyses were conducted on 87 high-temperature (>65°C) water and sediment samples from hot springs in Yellowstone National Park, Wyoming, USA ( n = 41; mantle plume setting), Iceland ( n = 41, divergent boundary), and Japan ( n = 5; convergent boundary). Region-specific variation in geochemistry and sediment-associated 16S rRNA gene amplicon sequence variant (ASV) composition was observed, with 16S rRNA gene assemblages being nearly completely distinguished by region and pH being the most explanatory parameter within regions. Several low abundance ASVs exhibited cosmopolitan distributions across regions, while most high-abundance ASVs were only identified in specific regions. The presence of some cosmopolitan ASVs across regions argues against dispersal limitation primarily shaping the distribution of taxa among regions. Rather, the results point to local tectonic and geologic characteristics shaping the geochemistry of continental hydrothermal systems that then select for distinct microbial assemblages. These results provide new insights into the co-evolution of hydrothermal systems and their microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗