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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A quantitative comparison of the fingerprint of twinned microstructures through surface and three-dimensional techniques

Assessing the fingerprint of a material’s microstructure is key for supporting materials design. With the emergence of a wide range of 3D characterization techniques, it is critical to understand the main differences in fingerprints reconstructed from 2D and 3D datasets. To this end, we introduce a graph-based microstructure reconstruction framework that enables structural comparisons of twin domain networks in high purity Ti using 3D and 2D electron backscatter diffraction. Insights into the structure of the twin networks are facilitated by combining statistical analysis of twin crystallography with visual and graphical analysis of the novel graph abstractions of the twins. We demonstrate that compared to 3D reconstructions, conventional 2D views of twinning miss key aspects of the microstructure including the high interconnectivity of domains into networks that span the full reconstruction volume. The reduced cross-grain and in-grain twin connectivity typically observed in 2D has notable implications on our understanding of how twinning mediates the plastic response of microstructures and how twin networks evolve. It is thus clear that 3D characterization is critical for accurately inferring both twin network morphologies as well as the key unit processes facilitating network formation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗
DOE OSTI2020

Strongly adhesive dry transfer technique for van der Waals heterostructure

That one can stack van der Waals materials with atomically sharp interfaces has provided a new material platform of constructing heterostructures. The technical challenge of mechanical stacking is picking up the exfoliated atomically thin materials after mechanical exfoliation without chemical and mechanical degradation. Chemically inert hexagonal boron nitride (hBN) has been widely used for encapsulating and picking up vdW materials. However, due to the relatively weak adhesion of hBN, assembling vdW heterostructures based on hBN has been limited. Here, we report a new dry transfer technique. We used two vdW semiconductors (ZnPS 3 and CrPS 4 ) to pick up and encapsulate layers for vdW heterostructures, which otherwise are known to be hard to fabricate. By combining with optimized polycaprolactone (PCL) providing strong adhesion, we demonstrated various vertical heterostructure devices, including quasi-2D superconducting NbSe 2 Josephson junctions with atomically clean interface. The versatility of the PCL-based vdW stacking method provides a new route for assembling complex 2D vdW materials without interfacial degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of Chiral Two-Body Currents in 6 Li Magnetic Properties in Light of a New Precision Measurement with the Relative Self-Absorption Technique

A direct measurement of the decay width of the excited $0^+_1$ state of 6 Li using the relative self-absorption technique is reported. Our value of $Γ_{γ,0^{+}_{1}} → 1^{+}_{1}$ = 8.17 (14) stat. (11) syst. eV provides sufficiently low experimental uncertainties to test modern theories of nuclear forces. The corresponding transition rate is compared to the results of ab initio calculations based on chiral effective field theory that take into account contributions to the magnetic dipole operator beyond leading order. This allows for a precision test of the impact of two-body currents that enter at next-to-leading order.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Strain-tunable microwave-resonance technique for quantum materials

By integrating a dielectric microwave resonator with a piezoelectric-based strain device, we develop an in situ strain-tunable microwave spectroscopy technique that enables contactless measurements of material properties under strain. To demonstrate the capability of this device, we measure the strain-dependent microwave surface impedance of the representative iron-based superconductor Ba⁢Fe 2 (As 1−𝑥 ⁢P 𝑥 ) 2 at the slightly overdoped composition. We successfully control and observe the suppression of superconductivity under both compressive and tensile anisotropic lattice distortions along the tetragonal [110] T direction, as manifested by changes in the quality factor and resonance frequency shifts of microwave resonance. Furthermore, strain-induced changes in microwave surface resistivity, an extension of conventional DC-limit transport elastoresistivity to the microwave regime, provide information on electronic anisotropy equivalent to that of DC elastoresistivity, while offering a contactless alternative. Furthermore, our strain-tunable cavity therefore serves as a powerful, contactless probe of fundamental material properties under strain and may also potentially facilitate the design of hybrid quantum systems with strain-engineered quantum degrees of freedom.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Advanced Measurement and Visualization Techniques for High-Temperature Heat Pipe Experiments

This report conducts a preliminary investigation of the available literature and summarizes some potential advanced measurement and visualization techniques for operating high-temperature heat pipe (HP) that can be implemented to support the United States (U.S.) Department of Energy (DOE) Microreactor Program (MRP). The primary objective for the extensive literature research is to investigate the possibilities and feasibility for the design and construction of an advanced experimental test facility with the aim of producing high-fidelity high-resolution heat pipe data during its operation. Based on the existing HP test facilities at Idaho National Laboratory (INL)—including the Single Primary Heat Extraction and Removal Emulator (SPHERE) and the Microreactor Agile Non-Nuclear Experimental Test Bed (MAGNET)—further efforts will be made to examine the technical feasibility to HP measurements and support the research, development, and demonstration (RD&D) process for an HP-cooled microreactor. Continuing collaborations with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, other research institutes and universities are considered to be extremely important so that the experimental facility and resultant database can satisfy the validation needs of advanced heat pipe modeling codes being developed under DOE NEAMS program.

42 ENGINEERING↗
DOE OSTI2025

Recent Collaborations and Innovations to Demonstrate Next-Generation Techniques for Monitoring Subsurface Carbon Storage

World Carbon Capture, Utilization, and Storage (CCUS) Conference, Bergen, Norway, September 1–4, 2025. This talk provides a high-level overview of many novel and sustainable carbon storage-monitoring methods to accelerate the deployment of CCUS technologies at future CCUS sites across the United States. The Energy & Environmental Research Center’s work impacts the general CCUS industry by providing novel low-impact methods for tracking the injected plume’s migration and more autonomous data collection and processing techniques for performing assurance monitoring. Specifically, the results benefit 1) CCUS community members through knowledge sharing of lessons learned; 2) CCUS operators through commercialization of additional methods, including improvements to workflows and simplification of fieldwork; and 3) CCUS project stakeholders through implementation of low-impact and more autonomous monitoring solutions.

02 PETROLEUM↗
DOE OSTI2020

High-Pressure Sound Velocity Measurements of Liquids Using In Situ Ultrasonic Techniques in a Multianvil Apparatus

Sound velocity and equation of state of liquids provide important constraints on the generation, presence, and transport of silicate and metallic melts in the Earth’s interior. Unlike their solid counterparts, these properties of liquids pose great technical challenges to high-pressure measurements and are poorly constrained. Here we present the technical developments that have been made at the GSECARS beamline 13-ID-D of the Advanced Photon Source for the past several years for determination of sound velocity of liquids using the ultrasonic techniques in a 1000-ton Kawai-type multianvil apparatus. Temperature of the sound velocity measurements has been extended to ~2400 K at 4 GPa and ~2000 K at 8 GPa to enable studies of liquids with very high melting temperatures, such as the silicate liquids.

58 GEOSCIENCES↗
DOE OSTI2024

Handling and Tagging Techniques for Implanting Juvenile American Shad with a New Acoustic Microtransmitter

In this article, the use of telemetry techniques to better understand the behavior and survival of juvenile American shad (Alosa sapidissima), as they migrate through hydropower systems, has been challenging because shad are widely known to be particularly sensitive to handling. The goal of this study was to develop a tagging protocol using a new, acoustic micro transmitter that minimizes the detrimental effects of the tagging process and maximizes post-tagging survival of juvenile American shad. Limiting out-of-water handling and the use of brackish saltwater (7.5 parts per thousand) before and after tagging improved survival for shad tagged using a simple pectoral implantation method. This protocol provides a detailed, step-by-step procedure for tagging juvenile shad with acoustic transmitters. Fish tagged using this procedure and held in the laboratory for 60 days had an 81.5% survival rate, compared to 70% for their untagged counterparts. The successful tagging and handling practices developed in this study could be applied to field telemetry studies of juvenile shad and other sensitive species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques

Privacy-preserving machine learning is a field of study that explores how to protect and preserve the privacy of sensitive data while allowing the data to be used by machine learning algorithms. This field has had substantial industry investment due to heightened concerns about privacy in the technology industry, with a focus in two broad application areas: financial services and healthcare. Numerous privacy-preserving methods have also been proposed for international safeguards, but they have been difficult to enact because the data they require is con- sidered sensitive or proprietary by the nuclear facility operator. This work examines how current privacy-preserving approaches might be used to enable the International Atomic Energy Agency (IAEA) to use that data to contribute to a safeguards conclusion about a state while giving nuclear operators confidence that their sensitive data is adequately protected. This paper begins by exploring several broad categories of privacy-preserving techniques including homomorphic encryption, secure multiparty computation, secure enclaves, and zero-knowledge proofs. Then we discuss some of the security considerations related to using these methods, potential use cases, and a conceptual system design for applying privacy-preserving methods in international safeguards.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Short-term nodal load forecasting based on machine learning techniques

This paper introduces an advanced Short-term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low-dimensional space compared to the original high-dimensional load space where features are barely possible to depict. Additionally, Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state-of-the-art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day-ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Towards Long‐Term Stable Perovskite Solar Cells: Degradation Mechanisms and Stabilization Techniques

It is certain that perovskite materials must be a game‐changer in the solar industry as long as their stability reaches a level comparable with the lifetime of a commercialized Si photovoltaic. However, the operational stability of perovskite solar cells and modules still remains unresolved, especially when devices operate in practical energy‐harvesting modes represented by maximum power point tracking under 1 sun illumination at ambient conditions. This review article covers from fundamental aspects of perovskite instability including chemical decomposition pathways under light soaking and electrical bias, to recent advances and techniques that effectively prevent such degradation of perovskite solar cells and modules. In particular, fundamental causes for permanent degradation due to ion migration and trapped charges are overviewed and explain their interplay between ions and charges. Based on the degradation mechanism, recent advances on the strategies are discussed to slow down the degradation during operation for a practical use of perovskite‐based solar devices.

14 SOLAR ENERGY↗
DOE OSTI2020

Advanced Characterization Techniques for Overcoming Challenges of Perovskite Solar Cell Materials

Abstract In the last 10 years, organic–inorganic hybrid perovskite solar cells have achieved unprecedented advances, to the point where they now exhibit extremely high efficiency. However, long‐term stability and areal scalability limitations impede the commercial application of perovskite materials, and appropriate diagnosistic tools have become necessary to evaluate perovskite materials. Characterization of perovskite materials is regularly misinterpretated, due to unique intrinsic and extrinsic factors: degradation from the measurement source, ion migration, phase transition, and separation. Herein, studies on perovskites are reviewed that have used advanced characterization tools to overcome characterization challenges. Cryogenic temperature assisted measurements mitigate degradation or phase transitions induced by the measurement source. In situ measurements can track the variation of perovskite materials depending on external stimuli. Spatial material properties are able to be evaluated by the use of multidimensional mapping techniques. An overview of these advanced characterization tools that can overcome the challenges associated with established tools provides the opportunity for further understanding perovskite materials and solving the remaining challenges on the road to commercialization.

Kim, Min‐cheol↗
DOE OSTI2022

Combining Experimental and Theoretical Techniques to Gain an Atomic Level Understanding of the Defect Binding Mechanism in Hard Carbon Anodes for Sodium Ion Batteries

Sodium ion batteries (NIBs) are an attractive alternative to lithium–ion batteries in applications that require large–scale energy storage due to sodium's high natural abundance and low cost. Hard carbon (HC) is the most promising anode material for NIBs; however, there is a knowledge gap in the understanding of the sodium binding mechanism that prevents a rational design of HC. This study tunes sucrose–derived HC via synthesis temperature then evaluates the structural, physical, and electrochemical properties. Neutron total scattering is used to generate structural models by fitting pair distribution functions (PDF) with a combination of molecular dynamics and reverse Monte Carlo methods. From this model, the number and type of structural features are identified, quantified, and correlated to the galvanostatic charge/discharge. A method of PDF “fingerprinting” binding sites using Na probe atoms is developed and analyzing these PDFs reveals an atomistic view of ion binding sites responsible for “defect” storage mechanisms. Combining these techniques results in an atomic–level study that provides a big picture of the Na–binding mechanism in NIBs, which allows for more precise tuning of the structure–property relationships in the future. Finally, the methodologies developed will also enable new strategies for the analysis of amorphous functional materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Monitoring Structural and Electronic Changes of Supported Metal Catalysts Using Combined X‐Ray Techniques

Supported metal nanoparticle catalysts have become increasingly crucial for many catalytic applications. However, long-term catalyst stability remains a problem due to catalyst deactivation caused by coke formation and sintering. The deposition of a thin overcoating via atomic layer deposition (ALD) onto metal-supported nanoparticles has shown to greatly inhibit catalyst deactivation. This work utilizes a model catalyst system comprised of Pt nanoparticles supported on Al 2 O 3 to demonstrate the effect of an atomically thin overcoating on supported metal nanoparticles. Continuous operando small-angle X-ray scattering (SAXS) and X-ray absorption near edge spectroscopy (XANES) monitor structural and electronic changes to the catalyst and overcoating during calcination. SAXS data fitting reveals the formation of nanopores in the overcoating at high temperatures, while XANES monitors the oxidation state of the Pt catalyst. Herein, the usefulness of combined X-ray techniques is demonstrated to characterize supported metal catalysts to further understanding of the synergistic effects of the ALD overcoating to aid in the design of new catalyst materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Investigation of porous polydimethylsiloxane structures with tunable properties induced by the phase separation technique

This article reports the fabrication and characterization of porous polydimethylsiloxane (PDMS) structures developed by the solvent evaporationinduced phase separation technique. Here, ternary systems containing water/ tetrahydrofuran (THF)/PDMS with various concentrations are produced to form a stable solution. The porous PDMS structures are formed by removing the solvent (THF) and nonsolvent (water) phases during the stepping heat treatment procedure. The analytical ternary phase diagram is constructed based on the thermodynamic equilibrium state in the polymer solution to explain the stable/unstable formulations and the possible composition change path. The results show that the isolated pores with the adjustable pore size ranging from 330 to 1900 μm are obtained by tuning the water to the THF ratio. The mechanical properties of the porous PDMS structures are determined by conducting the tensile tests on the prepared dog bone-shaped specimens. A wide range of elastic modulus ranging between 0.49 and 1.05 MPa was achieved without affecting the density of the porous sample by adjusting the solvent and non-solvent content in the solution. It is shown that the flexibility of the porous structures can be improved by reducing the ratio of water to THF and decreasing the PDMS content. The porosity measurements reveal that the PDMS concentration is the major phase controlling the porosity of the structure, while the effect of water/THF is negligible.

42 ENGINEERING↗
DOE OSTI2021

A Comprehensive Review of Selected Major Categories of Lithium Isotope Separation Techniques

The ability to generate materials with an enriched isotopic abundance, that is one that differs from natural abundance, is critical in terms of technology. Isotopes are used in a wide range of applications, including radioactive tracers, nondestructive tests, radiotherapy in human medicine, nuclear fuels, and isotope‐substituted compounds for chemistry and biology research, as well as environmental geochemical signature tracers. A commonly discussed research topic is the isotopic separation of various elements—uranium, hydrogen, lithium, and iodine, among many others. Among these isotopes, lithium isotopes are especially important in various applications including strategic areas. These isotopes are light and hence their separation techniques are not very obvious. Herein, all the main categories of Li isotope separation are reviewed in depth with a focus on electrochemical technologies for stable lithium isotope separation.

Murali, Arun↗
DOE OSTI2025

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗