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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Synthesis of Zn2NbN3 ternary nitride semiconductor with wurtzite-derived crystal structure

Binary III-N nitride semiconductors with wurtzite crystal structure such as GaN and AlN have been long used in many practical applications ranging from optoelectronics to telecommunication. The structurally related ZnGeN2 or ZnSnN2 derived from the parent binary compounds by cation mutation (elemental substitution) have recently attracted attention, but such ternary nitride materials are mostly limited to II-IV-N2 compositions. This paper demonstrates synthesis and characterization of zinc niobium nitride (Zn2NbN3) – a previously unreported II2-V-N3 ternary nitride semiconductor. The Zn2NbN3 thin films are synthesized using a one-step adsorption-controlled growth, and a two-step deposition/annealing method that suppresses the loss of Zn and N. Measurements indicate that this sputtered Zn2NbN3 crystalizes in cation-disordered wurtzite-derived structure, in contrast to chemically related rocksalt-derived Mg2NbN3 compound, also synthesized here for comparison using the two-step method. The estimated wurtzite lattice parameter ratio of Zn2NbN3 is 1.55, and the optical absorption onset is at 2.1 eV. Both of these values are lower compared to published Zn2NbN3 computational values of c/a = 1.62 and Eg = 3.5 - 3.6 eV. Additional theoretical calculations indicate that this difference is due to cation disorder in experimental samples, suggesting a way to tune the structural parameters and the resulting properties of heterovalent ternary nitride materials. Overall, this work expands the wurtzite family of nitride semiconductors to include Zn2NbN3, and suggests that related II2-V-N3 and other ternary nitrides should be possible to synthesize.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

No evidence for a strong decrease of planetesimal accretion in old white dwarfs

ABSTRACT A large fraction of white dwarfs are accreting or have recently accreted rocky material from their planetary systems, thereby ‘polluting’ their atmospheres with elements heavier than helium. In recent years, the quest for mechanisms that can deliver planetesimals to the immediate vicinity of their central white dwarfs has stimulated a flurry of modelling efforts. The observed time evolution of the accretion rates of white dwarfs through their multi-Gyr lifetime is a crucial test for dynamical models of evolved planetary systems. Recent studies of cool white dwarf samples have identified a significant decrease of the mass accretion rates of cool, old white dwarfs over Gyr time-scales. Here, we revisit those results using updated white dwarf models and larger samples of old polluted H- and He-atmosphere white dwarfs. We find no compelling evidence for a strong decrease of their time-averaged mass accretion rates for cooling times between 1 and 8 Gyr. Over this period, the mass accretion rates decrease by no more than a factor of the order of 10, which is one order of magnitude smaller than the decay rate found in recent works. Our results require mechanisms that can efficiently and consistently deliver planetesimals inside the Roche radius of white dwarfs over at least 8 Gyr.

Blouin, Simon (ORCID:0000000296321436)↗
DOE OSTI2020

Protected helical transport in magnetically doped quantum wires: Beyond the one-dimensional paradigm

One-dimensional (1D) quantum wires, which are functionalized by magnetic ad-atoms, can host ballistic helical transport. Helicity protects transport from an undesirable influence of material imperfections and makes the magnetically doped wire very promising elements for nanoelectronics and spintronics. However, fabricating purely 1D conductors is experimentally very challenging and not always feasible. In this paper, we show that the protected helical transport can exist even inquasi-1D wires. We model the quasi-1D magnetically doped wire as two coupled dense 1D Kondo chains. Each chain consists of itinerant electrons interacting with localized quantum magnetic moments - Kondo impurities. We have analyzed the regimes of weak-, intermediate- and strong inter-chain coupling and found conditions necessary for the origin of the aforementioned protected transport. Our results give a push for experimental realizations of the helical states in magnetically doped wires.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Temperature- and Rate-Dependent Pathways in Formation of Metastable Silicon Phases under Rapid Decompression

High-pressure metallic beta-Sn silicon (Si-II), depending on temperature, decompression rate, stress, etc., may transform to diverse metastable forms with promising semiconducting properties under decompression. However, the underlying mechanisms governing the different transformation paths are not well understood. Here, two distinctive pathways, viz., a thermally activated crystal-crystal transition and a mechanically driven amorphization, were characterized under rapid decompression of Si-II at various temperatures using in situ time-resolved x-ray diffraction. Under slow decompression, Si-II transforms to a crystalline bc8/r8 phase in the pressure range of 4.3-9.2 GPa through a thermally activated process where the overdepressurization and the onset transition strain are strongly dependent on decompression rate and temperature. In comparison, Si-II collapses structurally to an amorphous form at around 4.3 GPa when the volume expansion approaches a critical strain via rapid decompression beyond a threshold rate. The occurrence of the critical strain indicates a limit of the structural metastability of Si-II, which separates the thermally activated and mechanically driven transition processes. The results show the coupled effect of decompression rate, activation barrier, and thermal energy on the adopted transformation paths, providing atomistic insight into the competition between equilibrium and nonequilibrium pathways and the resulting metastable phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Comparative Analysis of Capacitive Sensors with Different Geometrical layouts for Cable Insulation Degradation Detection

This paper presents a comparative analysis between an Interdigital Capacitor (IDC) sensor and a Serpentine Capacitive (SRC) sensor. The effects of electrode layouts on sensor performance are studied by means of finite element simulation where the geometrical dimensions (except electrode length) and material characteristics of both sensors are identical. Conformal IDC and SRC sensors with equal sensor area are modeled on a cable surface. The simulation results show a tradeoff between IDC and SRC sensors in terms of sensitivity and penetration depth. The SRC sensor shows 13.3% increase in sensitivity and 16.8% increase in capacitance compared to the IDC sensor, though the electric field penetration depth of the SRC sensor is 18.8% lower than the IDC sensor. In addition, the effects of change in electrode width, inter-electrode gap, and number of electrodes on SRC and IDC sensor comparative performance are evaluated.

Interdigital Capacitance, Serpentive Capacitive Se↗
DOE OSTI2021

Thermal Analysis of a 50 kW Three-Phase Wireless Charging System

In this paper, the thermal analysis of a 50 kW three-phase wireless charging system (WCS) is presented. Addressing the thermal challenge is essential for designing a compact charging pad for 50 kW and higher-power WCS pads. Low thermal conductivity of the Litz wire and ferrite, and uneven distribution of the coil, and core loss cause the high temperature in the charging pads. In this paper, the loss distribution of a 50 kW three-phase WCS is investigated, and the temperature distribution is simulated using finite element analysis (FEA) considering the magnetic and non-magnetic materials of a charging pad. The thermal characteristics of an extremely high-power density 50 kW WCS prototype are tested experimentally for 10 minutes of operation. The simulation and experimental results show that the coil temperature increases to 65°C, the core temperature varies between 50°C to 150°C, and the packaging temperature increases to 65°C after 10 minutes of operation at rated 50 kW output power.

Mohammad, Mostak↗
DOE OSTI2024

Embedding of Optical Fibers with Electric Field Assisted Sintering

The Electric Field Assisted Sintering (EFAS) technique was used for embedding the fibers, involving rapid heating via an electric current and pressure, reducing fiber exposure to high temperatures. Stainless steel guide tubes were inserted at the fiber-matrix junctions to prevent fiber breakage during the sintering process. The study varied key EFAS parameters—temperature (800°C to 980°C), pressure (40-50 MPa), and hold time (5-10 minutes)—to assess how they influenced fiber embedding. After embedding, the fibers were inspected using optical frequency domain reflectometry (OFDR) to evaluate optical losses and strain along the fiber length. The OFDR scans confirmed that the fibers remained intact, and subsequent transmission tests demonstrated no macroscopic fractures. The embedded samples were also analyzed using scanning electron microscopy (SEM) and X-ray computed tomography (CT) scans. These tests confirmed good bonding between the fiber and matrix with no cracking, though some porosity was present in samples fabricated at lower temperatures. At higher temperatures (980°C), the fiber-matrix bonding was continuous and defect-free, with metal coatings aiding in the bonding process. Elemental analysis showed interdiffusion between the fiber coatings and matrix materials, particularly between the gold coating and stainless steel. Helium leak tests revealed that lower sintering temperatures resulted in matrix porosity, causing leaks, but samples fabricated at 980°C were leak-tight.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks

Evaluating the mechanical response of fiber-reinforced composites can be extremely time-consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input–output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally used for linear elastic materials, and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 h to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.

Sun, Yixuan (ORCID:0000000311093380)↗
DOE OSTI2024

Assessment of Cementitious Composites for High-Temperature Geothermal Wells

High-temperature (HT) geothermal wells can provide green power 24 hours a day, 7 days a week. Under harsh environmental and operational conditions, the long-term durability requirements of such wells require special cementitious composites for well construction. This paper reports a comprehensive assessment of geothermal cement composites in cyclic pressure function laboratory tests and field exposures in an HT geothermal well (300–350 °C), as well as a numerical model to complement the experimental results. Performances of calcium–aluminate cement (CAC)-based composites and calcium-free cement were compared against the reference ordinary Portland cement (OPC)/silica blend. The stability and degradation of the tested materials were characterized by crystalline composition, thermo-gravimetric and elemental analyses, morphological studies, water-fillable porosity, and mechanical property measurements. All CAC-based formulations outperformed the reference blend both in the function and exposure tests. The reference OPC/silica lost its mechanical properties during the 9-month well exposure through extensive HT carbonation, while the properties of the CAC-based blends improved over that period. The Modified Cam-Clay (MCC) plasticity parameters of several HT cement formulations were extracted from triaxial and Brazilian tests and verified against the experimental results of function cyclic tests. These parameters can be used in well integrity models to predict the field-scale behavior of the cement sheath under geothermal well conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A Multiscale Modeling Approach to Cryocompressed Hydrogen Storage Pressure Vessels – Part II: Constitutive Modeling and Finite Element Analysis

Subjected to thermomechanical cycling, composite cryogenic hydrogen (H2) storage pressure vessels experience high stresses in the carbon fiber (CF)/epoxy overwrap which can lead to vessel failure due to a combination of degradation mechanisms such as matrix cracking, fiber/matrix debonding, delamination, and fiber rupture. The present paper is the second part of a set of two articles on this subject that addresses analysis and design of these pressure vessels. Predictive finite element (FE) modeling capabilities have been used to support a material acceptance process that aims at providing guidance to evaluate specialty resins, vessel liner options, and CF composites through thermomechanical testing. We have applied a multiscale modeling approach recently developed (B.N. Nguyen et al., International Journal of Hydrogen Energy, 2019, https://doi.org/10.1016/j.ijhydene.2019.09.200) to perform three-dimensional (3D) FE analysis and design of a cryo-compressed H2 storage pressure vessel for its operation in a large temperature range from room to cryogenic temperatures. The developed approach termed the micro-meso-macro approach determines the laminar stresses in different layers of the vessel in addition to constituent (i.e., fiber and matrix) stresses that are important to the design of the composite overwrap for its layup and material combination to reduce the risk of vessel failure.

Nguyen, Ba Nghiep↗
DOE OSTI2022

Utilizing Coal as a Source for REE/CMs

The Department of Energy’s program to produce Rare Earth Elements (REE) and Critical Minerals (CM) from coal and related materials was created to alleviate concerns over U.S. dependence on foreign supplies and to provide domestic industry a secure supply of the materials they need to participate in evolving markets. The lack of a mature domestic REE supply chains, however, present a major obstacle to achieving this goal. Engineers in the National Energy Technology Laboratory (NETL)’s Research and Innovation Center (RIC) have conducted numerous market and techno-economic analyses on domestic REE needs and pathways, as well as extensive interviews with industry leaders, all with the goal of investigating the opportunities for coal-derived REE and CM products. This talk will highlight some of the major challenges and key takeaways from this work (Presented at the ACS 26th Annual Green Chemistry & Engineering Conference).

Summers, William↗
DOE OSTI2020

Evaluation of The NSUF Reactor Activation and Damage (RAD) Calculator: Assessing the Accuracy of the Activation Calculator

Nuclear Science User Facilities developed an activation calculator as part of the Combined Material Experiment Toolbox project. The activation calculator estimates the radionuclide concentration in an irradiated sample to indicate when and where the samples may best be examined to only help with scoping the experiment. To verify the accuracy of the calculator, the results from the specific gamma dose rate [mrem/hr/g] at various cooldown times were compared to equivalent results generated in ORIGEN. Most elements were tested as a sample composed solely of that element. These samples were exposed to thirteen different lengths of irradiation in all reactor positions included in the calculator. For elements of interest (i.e., ones currently included in the Nuclear Fuels and Materials Library), 83% of the test cases fell within the allowed tolerance for this scoping tool of ±50%. The overall error is logarithmic with irradiation length and is almost completely independent of reactor position. The primary source of error is missing nuclear data and reactions. NSUF believes that the RAD Calculator can be deployed to the user community because it is designed to provide estimates to aid researchers in planning experiments, and users are cautioned about the limitations of the calculator.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Behavior of copper‐containing high‐entropy alloys in harsh metal‐dusting environments

Abstract Metal dusting is still an unresolved issue at high temperatures. Currently, two material‐related strategies to mitigate metal dusting are described in the literature. On the one hand, highly alloyed materials are used, which contain large amounts of protective oxide‐forming elements, such as Cr, Al, and Si. The second mitigation strategy is based on inhibiting the catalytic effect of Fe, Ni, and Co. These elements all strongly catalyze the formation of solid carbon from the gas phase. Combining the catalytic protection of Cu alloying for metal dusting with protection by a classical alumina/chromia barrier is a native feature that high‐entropy alloys (HEAs) can offer. In this study, the behavior of different equiatomic HEAs with and without Al and/or Cu are studied when exposed at 620°C in a highly aggressive metal‐dusting environment.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced Characterization of Solid-State Battery Materials Using Neutron Scattering Techniques

Advanced batteries require advanced characterization techniques, and neutron scattering is one of the most powerful experimental methods available for studying next-generation battery materials. Neutron scattering offers a non-destructive method to probe the complex structural and chemical processes occurring in batteries during operation in truly in situ/in operando measurements with a high sensitivity to battery-relevant elements such as lithium. Neutrons have energies comparable to the energies of excitations in materials and wavelengths comparable to atomic distances in the solid state, thus giving access to study structural and dynamical properties of materials on an atomic scale. In this review, a broad overview of selected neutron scattering techniques is presented to illustrate how neutron scattering can be used to gain invaluable information of solid-state battery materials, with a focus on in situ/in operando methods. These techniques span multiple decades of length and time scales to uncover the complex processes taking place fundamentally on the atomic scale and to determine how these processes impact the macroscale properties and performance of functional battery systems. This review serves the solid-state battery research community by examining how the unique capabilities of neutron scattering can be applied to answer critical and unresolved questions of materials research in this field. A thorough and broad perspective is provided with numerous practical examples showing these techniques in action for battery research.

25 ENERGY STORAGE↗
DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗