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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sample glue layer investigation and mitigation for laser induced prompt impulse experiments

Understanding longer timescale material reactions under dynamic stress loading is critical for applications in materials engineering, shock physics, and planetary science. Prompt impulse experiments generate lower pressures since the ablator—the material first removed by the laser—is thicker and farther from the diagnostic plane, capturing aggregate material responses from the initial shock wave, rarefaction waves, and later time effects. This complexity demands thorough material characterization and simulation support. Since traditional sample construction is specific to supported shock experiments, designing prompt impulse experiments requires reconsideration around target design and sample engineering. Here, we present sample preparation techniques, experimental investigations, and theoretical simulations to investigate glue layer impacts, aiming to standardize samples for consistent data at lower laser fluences. We find that glue layers <30 μm have a minimal impact on peak velocity and pulse shape. The peak velocity scales linearly with glue layer thickness until a glue layer of 75 μm. For glue layers >75 μm, the peak velocity no longer scales with thickness; however, the pulse shape continues to degrade as described by simulations.

Lasers↗
DOE OSTI2023

The Materials Provenance Store

Abstract We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing microelectronics through nanoscale science: A perspective on needs and opportunities from the nanoscale science research centers

Microelectronics are the cornerstone of the modern world, enhancing our daily lives by providing services such as communications and datacenters. These resources are accessible thanks to the continual pursuit of a deeper understanding of the chemical and physical phenomena underlying the materials synthesis approaches and fabrication processes used to create microelectronic components and subsequently the components' responses to electrical, optical, and other stimuli that are utilized within microelectronic systems. Today, further development of microelectronics requires multidisciplinary expertise across scientific disciplines and fields of study—synthesis, materials characterization, nanoscale fabrication, and performance characterization—with focus placed on comprehending the nanoscale forms and features of microelectronic components. The Nanoscale Science Research Centers (NSRCs) are Department of Energy, Office of Science user facilities that support the international scientific community in advancing nanoscale science and technology. As a key component of the U.S. Government's National Nanotechnology Initiative, the NSRCs enable transformative discoveries by providing world-class facilities, expertise, and collaborative opportunities. Here, in this perspective, we showcase a non-exhaustive cross-section of the capabilities housed at and developed by the NSRCs and their user communities to address fundamental synthesis, metrology, fabrication, and performance considerations toward advancing the development of new microelectronics. Finally, we provide a timely outlook on the next major areas of necessary development in nanoscale sciences to continue the innovation of microelectronics into the next generation.

2D materials↗
DOE OSTI2021

Magneto-Optical Sensing Beyond the Shot Noise Limit

Magneto-optical sensors including spin noise spectroscopies and magneto-optical Kerr effect microscopies are now ubiquitous tools for materials characterization that can provide new understanding of spin dynamics, hyperfine interactions, spin-orbit interactions, and charge-carrier g-factors. Both interferometric and intensity-difference measurements can provide photon-shot-noise-limited sensitivity, but further improvements in sensitivity with classical resources require either increased laser power that can induce unwanted heating and electronic perturbations or increased measurement times that can obscure out-of-equilibrium dynamics and slow experimental throughput. Proof-of-principle measurements have already demonstrated quantum enhanced spin noise measurements with a squeezed readout field that are likely to be critical to the nonperturbative characterization of spin excitations in quantum materials that emerge at low temperatures. Here, a truncated nonlinear interferometric readout for low-temperature magneto-optical Kerr effect and related magneto-optical microscopies that is accessible with today's quantum optical resources is proposed. 10 nrad/$\sqrt{Hz}$ sensitivity is achievable with optical power as small as 1 µW. As a result, measurements may be performed at temperatures as low as 83 mK in commercially available dilution refrigerators. This combination of high sensitivity and low operating temperature is impossible to achieve with any classical measurement. The quantum advantage for the proposed measurements persists even in the limit of large loss and small squeezing parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Unraveling the impact of initial choices and in-loop interventions on learning dynamics in autonomous scanning probe microscopy

The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in scanning probe microscopy. We explore the concept of the “seed effect,” where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy on PbTiO 3 thin films, we illustrate the impact of the “seed effect” and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Hygrothermal Aging and Thermomechanical Characterization of As-Manufactured Tidal Turbine Blade Composites

This study investigates the hygrothermal aging behavior and thermomechanical properties of as-manufactured glass fiber-reinforced epoxy and thermoplastic composite tidal turbine blades. The blades were previously deployed in a marine environment and subsequently analyzed through a comprehensive suite of material characterization techniques, including hygrothermal aging, dynamic mechanical analysis (DMA), tensile testing and X-ray computed tomography (XCT). Hygrothermal aging experiments revealed that while thermoplastic composites exhibited lower overall water absorption (0.78% vs. 0.47%), they had significantly higher diffusion coefficients than epoxy (2.1 vs. 12.1 × 10 −13 m 2 s −1 ), suggesting faster saturation in operational environments. DMA results demonstrated that water ingress caused plasticization in epoxy matrices, reducing the glass transition temperature and increasing damping (112 °C to 104 °C), while thermoplastic composites showed more stable thermal behavior (87 °C glass transition temperature). Tensile testing revealed substantial reductions in ultimate strength (>40%) for both materials after prolonged water exposure, with minimal change in elastic modulus, highlighting the role of matrix degradation over fiber reinforcement. XCT image analysis showed that both composites were manufactured with high quality: no large voids or cracks were present, and the degree of misalignment was low. These findings inform future marine renewable energy composite designs by emphasizing the critical influence of moisture on long-term structural integrity and the need for optimized material systems in harsh marine environments. This work provides a rare real-world comparison of epoxy and recyclable thermoplastic tidal turbine blades, showing how laboratory aging tests and advanced imaging reveal the influence of material and manufacturing choices on long-term marine durability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Ultrasonic characterization of Ti-5Al-5Mo-5V-3Cr

Metastable β titanium alloys such as Ti-5Al-5Mo-5V-3Cr are of increasing interest due to their excellent corrosion resistance, high strength, and in particular, the ability to manipulate microstructure to a high degree to control the mechanical properties of the material. It is therefore important to consider how this manipulation of mechanical properties will affect the inspection of this material with ultrasonic testing both for the purposes of flaw detection and material characterization. The connection between material properties and ultrasonic characterization techniques will be discussed and then dissected in the context of Ti-5553. This metastable alloy was shown to exhibit remarkable variation in microstructure as well as ultrasonic velocity and attenuation demonstrating these techniques as potential candidates for characterizing Ti-5553 and Ti-5553 as a good candidate for aiding in future studies aimed at learning more about the role of secondary phases in ultrasonic wave propagation.

Sunderman, Ruth↗
DOE OSTI2024

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Aging mechanisms of filled cross-linked polyethylene (XLPE) cable insulation material exposed to simultaneous thermal and gamma radiation

Thermal and simultaneous thermal and gamma radiation aging experiments were conducted on an industry grade cross-linked polyethylene (XLPE) cable insulation material, which is a composite material with XLPE as polymer matrix and various additives and fillers. Reverse engineering was conducted to identify and quantify the material composition given that this information was not revealed by the cable manufacture. Samples were then aged at temperatures of 60, 90, and 115 °C, exposed to gamma radiation for total doses of 0 to 324 kGy, and at dose rates of 0 to 540 Gy/h. Aging mechanisms were studied using various materials characterization techniques including pyrolysis gas chromatography mass spectrometry (Py-GCMS), differential scanning calorimetry (DSC), nuclear magnetic resonance spectroscopy (NMR), and gel-fraction tests. Results show that the flame-retardant components were decomposed into smaller molecules when the material was exposed to gamma radiation, while no changes were observed when the samples were aged thermally without gamma radiation. With exposure to gamma radiation, the crystalline phase of the XLPE were damaged by gamma radiation introducing defects in the crystals, resulting in smaller and less perfect crystals. The dominance of chain scission or chain reformation process largely depends on the chain mobility, which is decided by the aging temperature. Chain scission was seen to dominate when the material was exposed to gamma radiation at 60 °C. Chain cross-linking slightly dominated when the material was exposed to gamma radiation at 90 °C and became more dominant with exposure to gamma radiation at 115 °C. No significant change to the XLPE polymer matrix was observed when the samples were exposed to thermal aging without gamma radiation. Finally, antioxidant is more effective in protecting the XLPE polymer matrix under thermal aging, but less effective with sample exposure to gamma radiation.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

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

Novel Materials R&D for Next-Generation Accelerator Target Facilities

High-Entropy Alloys and Electrospun Nanofiber materials are two classes of novel materials that can offer improved resistance to beam-induced radiation damage and thermal shock. Research to develop these new materials specifically for multi-megawatt accelerator target applications, such as beam windows and particle-production targets, are ongoing at Fermilab within the scope of a DOE Early Career Research Program. The research program combines in-beam experiments with complementary simulations to tailor the microstructures of these novel materials for use in next-generation accelerator target facilities. Iterative simulations to optimize the material composition, physics performance, and beam-induced thermomechanical response will guide the material design and fabrication processes based on established figures of merit. This will be followed by material irradiation experiments using low-energy ions and prototypic high-energy protons with extensive post-irradiation material characterization to assess and qualify the selected novel materials. This talk will describe the alloy design and synthesis, microstructural pre-characterization of the alloys, and plans for the eventual down selection following low-energy ion irradiation studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Imaging and Analysis of Insoluble Electrorefiner Material

Throughout pyroprocessing efforts at the Hot Fuel Examination Facility (HFEF), insoluble material has accumulated in the Electrorefiner (ER) vessel. The objective of this effort was to analyze the accumulated material to determine its origin. Material was removed from the ER salt bath and distilled to remove excess salt prior to performing analysis. Three samples were sent for chemical and isotopic analysis and underwent an ethyl acetate-bromine dissolution to segregate the oxide fraction from the metal fraction. Actinide concentrations were determined using a quadrupole–inductively coupled plasma–mass spectrometer (Q-ICP-MS). Three additional samples were sent for morphologic and elemental analysis by scanning electron microscopy (SEM) with Energy Dispersive X-ray (EDX) analysis at the Irradiated Materials Characterization Laboratory (IMCL). Analyses suggest that the material is primarily composed of UO2. with a small fraction of metal. This study draws no single conclusion as to the origin of the insoluble material in the ER. The particle sizes and morphologies observed in SEM micrographs indicate that the larger particles observed may be a result of introducing material to the ER that has not been completely reduced in Oxide Reduction (OR) operations, which precede electrorefining. Smaller particles may be due to reactions with oxygen and moisture present in HFEF. This research suggests that microscopic analysis of fuel particles before and after OR operations (including after distillation) as well as the uranium product collected on the cathode in the ER would increase understanding about particle morphology within the pyrochemical process.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

TiNi-Based Bi-Metallic Shape-Memory Alloy by Laser-Directed Energy Deposition

In this study, laser-directed energy deposition was applied to build a Ti-rich ternary Ti–Ni–Cu shape-memory alloy onto a TiNi shape-memory alloy substrate to realize the joining of the multifunctional bi-metallic shape-memory alloy structure. The cost-effective Ti, Ni, and Cu elemental powder blend was used for raw materials. Various material characterization approaches were applied to reveal different material properties in two sections. The as-fabricated Ti–Ni–Cu alloy microstructure has the TiNi phase as the matrix with Ti2Ni secondary precipitates. The hardness shows no high values indicating that the major phase is not hard intermetallics. A bonding strength of 569.1 MPa was obtained by tensile testing, and digital image correlation reveals the different tensile responses of the two sections. Differential scanning calorimetry was used to measure the phase-transformation temperatures. The austenite finishing temperature of higher than 80 °C was measured for the Ti–Ni–Cu alloy section. For the TiNi substrate, the austenite finishing temperature was tested to be near 47 °C at the bottom and around 22 °C at the upper substrate region, which is due to the repeated laser scanning that acts as annealing on the substrate. Finally, the multiple shape-memory effect of two shape-memory alloy sides was tested and identified.

36 MATERIALS SCIENCE↗
DOE OSTI2023

SEEMS: A Single Event Effects and Muon Spectroscopy facility at the Spallation Neutron Source

This study outlines a concept that would leverage the existing proton accelerator at the Spallation Neutron Source (SNS) of Oak Ridge National Laboratory to enable transformative science via one world-class facility serving two missions: Single Event Effects (SEE) and Muon Spectroscopy (μSR). The μSR portion would deliver the world’s highest flux and highest resolution pulsed muon beams for material characterization purposes, with precision and capabilities well beyond comparable facilities. The SEE capabilities deliver neutron, proton, and muon beams for aerospace industries that are facing an impending challenge to certify equipment for safe and reliable behavior under bombardment from atmospheric radiation originating from cosmic and solar rays. With negligible impact on the primary neutron scattering mission of the SNS, the proposed facility will have enormous benefits for both science and industry. Herein, we have designated this facility “SEEMS.”

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Effect of processing parameters and build orientation on microstructure and performance of AISI stainless steel 304L made with selective laser melting under different strain rates

Selective laser melting (SLM) process brings diverse potentials on geometry flexibility; therefore, it is more and more widespread to be employed in fabrication metal alloys served for industries. Nonetheless, a material characterization study is desired to carry on for better understanding the correlation among process, structure, microstructure, and performance. In the current study, the SLM fabricated AISI stainless steel 304L was fabricated with different process parameters and built orientations (horizontal, inclined, and vertical. The tensile behavior was evaluated under different strain rates (0.0001 /s, 0.001 /s, 0.01 /s, and 0.1 /s) and compared to the commercial cold-rolled and annealed counterpart. Grain structures, tensile strength, elongation-to-failure, strain rate sensitivity, work hardening, and fractographic analysis were evaluated in terms of the effect of energy density, build orientation, and strain rate. The output indicates the tensile strength increases with increasing strain rates. On the contrary, the elongation-to-failure shows a decreasing trend with strain rates. Tensile properties of specimens built in the horizontal and inclined orientations are more sensitive to strain rates due to the smaller dimension of grain structures. Tensile anisotropy depends on the energy input, where a high energy density could yield a strong build orientation-dependent anisotropy. Hall-Petch relationship is validated to explain the mechanical anisotropy in different built orientations for SLM alloys. The strain hardening exponent and work hardening rate are demonstrated to be positively correlated, and they increase with smaller grain sizes. The fine dimple features indicate the ductile fracture mode regardless of strain rates. The size of the ductile dimples seems to depend on the strain rates and build orientations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗