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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effect of temperature curing on properties and hydration of wollastonite blended magnesium potassium phosphate cements

K-struvite, the main hydrate of magnesium potassium phosphate (MKP) cements, dehydrates at ~50 °C, thus elevated temperatures at service conditions could affect cement properties and durability. In this study, properties and hydration of MKP cement without and with wollastonite were investigated at 20 and 50 °C. In hydrated pure MKP cement K-struvite decomposes progressively over time to MgKPO{sub 4}·H{sub 2}O at 50 °C, which leads to a strong reduction of solid volume and severe strength loss. The presence of wollastonite significantly slows down the decomposition rate of K-struvite, which is still observed after 393 days at 50 °C. K-struvite together with amorphous hydroxyapatite, M-(C)-S-H and CaK{sub 3}H(PO{sub 4}){sub 2} from the wollastonite reaction result in a cement with good short and long-term strength at both 20 and 50 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A splice method for local-to–nonlocal coupling of weak forms

Here, we propose a method to couple local and nonlocal diffusion models. By inheriting desirable properties such as patch tests, asymptotic compatibility and unintrusiveness from related splice and optimization-based coupling schemes, it enables the use of weak (or variational) formulations, is computationally efficient and straightforward to implement. We prove well-posedness of the coupling scheme and demonstrate its properties and effectiveness in a variety of numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response

A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. Furthermore, an iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Experimental study of mechanistic factors influencing solvent-driven fractional crystallization of calcium sulfate

To advance dimethyl ether-driven fractional crystallization (DME-FC), a more sustainable method of water treatment and mineral recovery, a range of chemical equilibria were measured. These include varying concentrations of miscible organic solvents (MOS) used to experimentally measure the solvent-induced solid-liquid equilibrium (SLE) of calcium sulfate (CaSO 4 ) in water. Seven MOS, including dimethyl ether (DME), acetonitrile (MeCN), 1,4-dioxane, tetrahydrofuran (THF), acetone, ethanol, and diethylamine, were screened to establish trends associated with molecular volume, functional groups, and physical properties. The effect of MOS on CaSO 4 removal differed at concentrations <0.15 mol fraction MOS; MOS with greater molecular volume (THF, 1,4-dioxane, and diethylamine) induced greater CaSO 4 precipitation on a per mole basis. The solvent-induced SLE for all MOS converged between 0.15 and 0.2 mol fraction MOS, reaching a CaSO 4 concentration consistent with a water to MOS hydration ratio of 5:1 to 6:1, which may correspond to the solvent generating a solution-based pseudo-clathrate structure with continuity within the solution. Finally, solution pseudo-clathrate structures provide a mechanistic basis for DME-FC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Gamma-ray spectroscopic characterization of long, rapidly-grown KSr 2 I 5 :Eu crystals

Collimation and volumetric studies were done to evaluate the gamma-ray spectroscopic performance of KSr 2 I 5 :Eu scintillators grown via the vertical Bridgman method. To demonstrate practical crystal growth for large-scale production, Ø1 in. × 6 in. long crystals were grown using fast pulling rates up to 5 mm/h and short cooling times. The long crystals were sectioned into shorter Ø1 in. cylinders to investigate sample-to-sample variation of scintillation properties, the effects of self-absorption and scintillation non-uniformity using different gamma-ray excitation configurations. The best light output and energy resolution achieved for the largest volume and aspect ratio crystals (60 cm 3 Ø1 in x 4.7 in. long) were 53,500 ph/MeV and 4.6% at 662 keV. Finally, we produced eight Ø1 in. × 1 in. (12.5 cm 3 ) cylindrical samples, four from each boule, with energy resolutions between 3.1%-3.5% at 662 keV.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multiphysics simulation of TRISO fuel compacts and the effects of homogenization on silver release predictions

This work studies the impact of explicit and homogenized modeling approaches on the multiphysics simulation of TRistructural ISOtropic (TRISO) fuel compacts in prismatic High Temperature Gas Reactors (HTGRs) and the silver release predictions. TRISO fuel compacts exhibit complex double heterogeneity that significantly affects heat conduction, neutron transport, and fission products release. In this work, we use Cardinal, a multiphysics tool based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to couple neutron transport and heat conduction. OpenMC is used for neutron transport and the MOOSE heat transfer module is used for the heat conduction. Then, we use BISON for the silver release predictions. Two different modeling approaches—explicit modeling of individual TRISO particles and homogenized representation using effective thermal properties—are compared at high TRISO packing fractions (20% and 40%) across varying power densities. Results demonstrate that homogenization significantly under-predicts peak temperatures, for the case of high power/TRISO, there is a difference of 83.73 K in the maximum temperature. Additionally, homogenization underestimates the silver release predictions compared to explicit modeling. Specifically, the temperature and power density differences lead to significant differences in silver release predictions. This work demonstrates the importance of accurately modeling heterogeneity of the TRISO particles to reliably predict fission product release and assess reactor safety margins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Predicting Oxygen Off-Stoichiometry and Hydrogen Incorporation in Complex Perovskite Oxides

Chemically and structurally complex solid compounds, including those with significant off-stoichiometry, are rapidly extending new material functionality across a variety of applications. Accelerated development of these compounds requires accurate predictions of material defect properties including effective defect formation energies and equilibrium defect concentrations. Traditional first-principles approaches typically examine dilute defect concentrations and relatively ordered atomic structures to identify the lowest energy defect sites. These approaches are rarely suitable for describing the disorder present in these systems and its influence on defect formation, which can lead to unphysically large predictions for defect concentrations. Here, we demonstrate a new method to accurately predict the temperature and pressure dependence of oxygen vacancy concentrations and proton interstitial concentrations in complex oxides. This method extends standard dilute defect calculations to incorporate atomic and magnetic disorder, employs the ensemble descriptions of defect sites resulting in improved predictions of defect formation energies, and accounts for effects beyond the dilute defect limit. To demonstrate our method, we show that the predicted defect concentrations in perovskites used as ceramic fuel cell cathodes, including Ba0.5Sr0.5Fe0.8Zn0.2O3-d, Ba0.5Sr0.5Co0.8Fe0.2O3-d, and BaCo1-x-y-zFexZryYzO3-d, are in good agreement with experimental values, thereby opening the door for predictive design of complex oxides by these applications.

DFT↗
DOE OSTI2023

Unexpected Thermomechanical Behavior of Off-Stoichiometry Epoxy/Amine Materials

Recent studies on off-stoichiometric thermosets reveal unique viscoelastic behavior derived from increased free volume and physical interactions between chain ends. To understand structural characteristics arising from cure and its effect on properties, we developed a Monte Carlo model based on step-growth polymerization. Our model accurately predicted structure–property trends for a two-component system of EPON 828 (EPON) and ethylenediamine. A second epoxy monomer, D.E.R. 732 (DER), was investigated to modulate T g . Binary mixtures of EPON and DER in off-stoichiometric, amine-rich formulations resulted in nonlinear evolution of thermomechanical properties with respect to initial formulation stoichiometry. Modifying our model with kinetic parameters allowing for differential epoxide/amine reaction kinetics only partially accounted for trends in T g , suggesting that spatiotemporal contributions-not captured by our model-were significant determinants of material properties compared to polymer architecture for three-component systems. Here these findings underpin the importance of spatial awareness in modeling to inform the development of dynamic thermosets.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of Composition on Mechanical Properties and Conductivity of the Dual-Ion Conductor Na 1+ x Mn x /2 Zr 2– x /2 (PO 4 ) 3 for Solid-State Batteries

Here we report the dependence of mechanical properties and ionic conductivity of the Na 1+ x Mn x /2 Zr 2– x /2 (PO 4 ) 3 (designated as NMZP) ionic conductor on its composition (with x = 0.5, 1.0, 1.5, and 2.0). Local mechanics was studied by instrumented nanoindentation, and the properties of interest included the elastic modulus, hardness, and fracture toughness. Overall, the material becomes more compliant and soft with the increase in the Na content. The elastic modulus reduces from 120 to 80 GPa, and nanoindentation hardness reduces from 7.8 to 4.2 GPa when x changes from 0.5 to 2.0. The relationship with fracture toughness ( K C ) is highly nonlinear: fracture toughness first decreases and then increases with the increase in the sodium content, reaching a maximum value of 0.89 MPa × m 1/2 at x = 2.0. Such a relationship was found to correlate with the formation of a glassy phase in NMZP at intermediate Na concentration. The maximum ionic conductivity coincides with the maximum fracture toughness in this material.

25 ENERGY STORAGE↗
DOE OSTI2023

Role of Cations in the Cation-Driven Assembly Process and their Effect on the Charge Storage Properties of Bilayered Vanadium Oxide and Reduced Graphene Oxide Heterostructures in Alkali Ion Systems

Exfoliated δ-Li x V 2 O 5 ·nH 2 O (ex-LVO) and reduced graphene oxide (rGO) heterostructures were constructed using different assembling cations (i.e., Li⁺, Na⁺, and K⁺ ions). The ex-LVO and rGO nanoflakes were stacked together using a concentrated chloride solution of each assembling cation and vacuum annealed at 200 °C to form three distinct two-dimensional (2D) layered architectures. X-ray diffraction and thermogravimetric analysis confirmed that the assembling ions can control the interlayer spacing of the bilayered vanadium oxide (BVO) phase as well as impact the crystallographic water content, which in turn affects the electrochemical performance. Scanning electron microscopy, scanning transmission electron microscopy (STEM), electron energy-loss spectroscopy (EELS), and X-ray photoelectron spectroscopy confirmed that a 2D heterointerface formed between LVO and rGO and that the cations used to assemble the heterostructure are trapped in the interlayer BVO region. High-resolution STEM imaging also showed the rGO dispersion throughout the LVO layers. Moreover, STEM-EELS identified a V 2 O 3 phase that forms along the rGO interface and can stabilize the materials during cycling. A charge storage mechanism analysis, combined with the galvanostatic intermittent titration technique, found that increased interlayer spacings of the BVO phase and using the assembling cations to define intercalation sites for identical charge-carrying ions lead to improved ion diffusion and increased capacities during cycling. Therefore, the Li⁺ and Na⁺ ion assembled heterostructures showed improved charge-carrying ion diffusion and charge storage capacities in each of their respective charge storage systems (i.e., Li-ion and Na-ion half-cells). In total, the cation used for heterostructure assembly can modify the final material structure and tailor the ion diffusion and charge storage capacity to tune its properties for the desired electrochemical system using a variety of 2D materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Optimized In Situ Doping Strategy Stabling Single-Crystal Ultrahigh-Nickel Layered Cathode Materials

Single-crystal Ni-rich cathodes offer promising prospects in mitigating intergranular microcracks and side reaction issues commonly encountered in conventional polycrystalline cathodes. However, the utilization of micrometer-sized single-crystal particles has raised concerns about sluggish Li + diffusion kinetics and unfavorable structural degradation, particularly in high Ni content cathodes. Herein, we present an innovative in situ doping strategy to regulate the dominant growth of characteristic planes in the singlecrystal precursor, leading to enhanced mechanical properties and effectively tackling the challenges posed by ultrahigh-nickel layered cathodes. Compared with the traditional dry-doping method, our in situ doping approach possesses a more homogeneous and consistent modifying effect from the inside out, ensuring the uniform distribution of doping ions with large radius (Nb, Zr, W, etc). Further, this mitigates the generally unsatisfactory substitution effect, thereby minimizing undesirable coating layers induced by different solubilities during the calcination process. Additionally, the uniformly dispersed ions from this in situ doping are beneficial for alleviating the two-phase coexistence of H2/H3 and optimizing the Li + concentration gradient during cycling, thus inhibiting the formation of intragranular cracks and interfacial deterioration. Consequently, the in situ doped cathodes demonstrate exceptional cycle retention and rate performance under various harsh testing conditions. Our optimized in situ doping strategy not only expands the application prospects of elemental doping but also offers a promising research direction for developing high-energy-density single-crystal cathodes with extended lifetime.

25 ENERGY STORAGE↗
DOE OSTI2020

Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

How an electrical current can stabilize a molecular nanojunction

Molecular junctions at the brink of their stability typically undergo structural changes that affect their conductance properties. This effect provides a feedback mechanism that can stabilize molecular junctions against current-induced bond-rupture.

Chemistry↗
DOE OSTI2025

Metal ion dopant-induced famatinite to chalcostibite phase transformation of copper antimony sulphide colloidal nanostructures: effect on photophysical and pseudocapacitance properties

Here, the strategic doping of transition metal ions into copper antimony sulphide (CAS) semiconducting nanostructures can significantly influence their photophysical and pseudocapacitive properties, for which there are few reports and therefore is the focus of this study. Accordingly, highly crystalline metal ion (Mn 2+ , Fe 2+ , Co 2+ , Ni 2+ and Zn 2+ ) doped off-stoichiometric copper rich/poor, antimony-rich/poor, and sulphur-poor CAS nanostructures (10–23 nm) were grown via colloidal (hot-injection) synthesis using metal diethyldithiocarbamate precursors. Importantly, metal ion doping significantly influences the structure and composition of the off-stoichiometric CAS nanostructures. Data from powder X-ray diffraction, Raman spectroscopy, high-resolution scanning/transmission electron microscopy, and energy dispersive X-ray spectroscopy confirm that the heavier metal ion dopants induce a novel phase transformation from famatinite (fCAS) to chalcostibite (cCAS) nanostructures. The influence of the metal ion dopants is also observed in the optical properties of as-synthesized nanostructures. This involves blue-shifted ultraviolet-visible absorption, reduced Urbach tailing, and tunable bandgaps between 2.17 and 2.38 eV. Also, the doped nanostructures display broad visible-near infrared photoluminescence via a triple radiative pathway with relatively short decay lifetimes between 0.2 and 6.1 ns. This is mediated by electronic transitions involving intrinsic (copper/antimony/sulphur interstitial) and extrinsic (metal ion interstitial) defect states. Additionally, electrodes prepared from Mn 2+ -doped fCAS nanostructures show enhanced pseudocapacitance via Na+ surface adsorption and intercalation relative to undoped fCAS electrodes, while Zn 2+ -doped cCAS electrodes exhibit pseudocapacitance via a combination of Na+ surface adsorption, intercalation, and redox reactions. These electrodes exhibit reduced charge transfer resistance, improved electronic conductivity and notably enhanced specific capacitance (∼222 F g −1 ), and charge transport, as measured in 1 M Na 2 SO 4 electrolyte via cyclic voltammetry and electrochemical impedance spectroscopy. To this end, the discovery of the metal ion-induced phase transformation presents a new avenue for optimizing the functional properties of fCAS and cCAS nanostructures, highlighting the critical role of metal ion-related defects in controlling the optical and electrochemical properties, towards potential solar absorption and energy storage applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Proton damage in (Y,Lu,Gd) 3 (Al,Ga) 5 O 12 :Ce mixed garnet scintillators

Scintillators are vital components for nuclear instrumentation and its applications, including plasma diagnostics and imaging. As yields in controlled fusion experiments increase, the radiation tolerance of scintillator candidates for use in instrumentation is of particular importance. High radiation exposure can damage scintillating materials and alter the optical properties. The effects of radiation damage in Ce-doped mixed garnet ceramics over the compositional range (Y,Gd,Lu) 3 (Al,Ga) 5 O 12 are investigated using optical techniques. The samples were exposed to 200 keV protons to an accumulated fluence of 10 16 protons per square centimeter, then characterized using diffuse reflectance spectroscopy (DRS). DRS with visible light can assess the radiation tolerance of opaque poly-crystalline samples, which can be easily sintered from powders and thus offer distinct advantages in characterization compared to single crystals. Qualitative trends in induced absorption are presented as a function of composition, and the ideal cerium dopant concentration for Y 2 LuAl 5 O 12 is determined to be 0.60–0.75 mol.%.

47 OTHER INSTRUMENTATION↗