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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Phase evolution of U-Zr system in a thermal cycling neutron diffraction experiment: as-cast U-35Zr and U-50Zr

Here, phase evolution of as-cast U-35 wt% Zr and U-50 wt% Zr alloys during thermal cycling (303–1073 K) was investigated using in-situ neutron diffraction. Analysis was performed using Rietveld crystal and microstructure refinements from time-of-flight neutron diffraction data, with a focus on evolution of lattice parameter, atom ordering, unit cell volume, and weight fractions during the thermal cycling. Disordered δ-UZr 2 and residual γ-(U,Zr) phase are retained in the field of δ of the U-Zr equilibrium phase diagram for the U-50Zr sample, and in the field between δ and α-U for the U-35Zr sample. The α phase that is present in the reported phase diagrams of U-Zr was not observed in the diffraction patterns. The evolutions of lattice parameter and unit cell volume of δ and γ are affected by both thermal expansion and chemistry.

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

Electrochemical Degradation of Pt 3 Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials

In fuel cell applications, the durability of catalysts is critical for large-scale industrial implementation. However, limited synthesis controllability and spectroscopic resolution impede a comprehensive understanding of degradation mechanisms at the atomic level. In this study, we develop a machine-learned potential (MLP) to simulate the degradation processes for Pt3Co nanoparticles. The precision of MLP is determined to be comparable to that of density functional theory calculations. Using off-lattice kinetic Monte Carlo simulations with MLP, we successfully replicate established experimental trends and offer a logical resolution to ongoing debates regarding atomic orderings. Based on the simulation results, we suggest design principles for Pt3Co nanoparticles that combine high activity and durability. Finally, we validate the wide applicability of our method by successfully applying it to Pt3Ni and Pt3Co0.5Ni0.5 nanoparticles. The research serves as a guideline for developing MLPs for alloy electrochemical catalysts and lays the foundation for designing more durable and active fuel-cell catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Bulk Stoichiometry-Controlled Surface Reconstruction of Nanosized Ni−In Intermetallic Catalysts Steers Methanol Selectivity in CO2 Hydrogenation

Intermetallic compounds (IMCs) are attractive platforms for elucidating structure−catalysis relationships due to their ordered atomic structure and well-defined bulk composition. Yet, how their surfaces reconstruct under reaction conditions and how such reconstruction is governed by bulk stoichiometry remain poorly understood. Here, we show that SiO2-supported Ni−In IMCs undergo reaction-driven surface reconstruction during CO2 hydrogenation and that bulk stoichiometry can be used to steer this evolution toward methanol formation. Among the compositions examined (Ni2In1, Ni1In1, Ni2In3, and Ni1In2), Ni2In3/SiO2 exhibits the highest methanol selectivity (∼70%) and a methanol space-time yield of 652 mg·gmetal−1·h−1 at 250 °C and 30 bar. Combined structural, surface characterization, and kinetic analyses suggest that the intermetallic bulk remains largely preserved, whereas the surface departs from the stoichiometric bulk and evolves toward InOx-enriched surface domains coupled to an electron-rich Ni−In intermetallic phase. The extent of this evolution depends strongly on the bulk Ni:In stoichiometry and is most pronounced for Ni2In3/SiO2. These findings identify bulk stoichiometry as a handle for tuning the working-state surface of intermetallic catalysts and provide a basis for designing methanol synthesis catalysts through controlled surface reconstruction.

Wang, Caiqi [ORNL] (ORCID:0000000198849990)↗
DOE OSTI2025

Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models

Developing transferable machine learning models is trending in data-driven materials research. However, how to apply such models to a specific research domain remains unclear. Here, in this work, we choose high-entropy materials as a platform with a specialized data set containing 145,323 DFT-relaxed materials. This data set is used to explore the role of domain-specific knowledge in training effective models. Our tests with three representative graph neural network architectures indicate the model complexity has much smaller influence on performance than the data itself. Specifically, the consideration of low-energy atomic ordering, structures with diverse elemental coverage, and high-order interactions significantly influences the model performance. We also find that domain knowledge-driven sampling can greatly enhance unsupervised learning techniques. This research highlights that developing specialized data sets is more beneficial than further complicating deep learning architectures. Additionally, physics-inspired sampling algorithms are crucially needed for better machine learning models for a specific materials research domain.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum simulation of an extended Dicke model with a magnetic solid

Abstract The Dicke model describes the cooperative interaction of an ensemble of two-level atoms with a single-mode photonic field and exhibits a quantum phase transition as a function of light–matter coupling strength. Extending this model by incorporating short-range atom–atom interactions makes the problem intractable but is expected to produce new physical phenomena and phases. Here, we simulate such an extended Dicke model using a crystal of ErFeO 3 , where the role of atoms (photons) is played by Er 3+ spins (Fe 3+ magnons). Through terahertz spectroscopy and magnetocaloric effect measurements as a function of temperature and magnetic field, we demonstrated the existence of a novel atomically ordered phase in addition to the superradiant and normal phases that are expected from the standard Dicke model. Further, we elucidated the nature of the phase boundaries in the temperature–magnetic-field phase diagram, identifying both first-order and second-order phase transitions. These results lay the foundation for studying multiatomic quantum optics models using well-characterized many-body solid-state systems.

Marquez Peraca, Nicolas↗
DOE OSTI2025

The effect of blue and infrared laser melting frequency on oxide morphology in 304L

Continuous Wave Laser Beam (LB) melting offers control over the localised heating and cooling of melt pools for welding, brazing, additive manufacturing and solidification. Research into laserliquid metal interactions has primarily focused on the heat and mass transport under large thermal gradients imposed by the localised melting conditions. Recent commercial availability of blue (450 nm) wavelength lasers has enabled the printing of material with poor IR (1064) nm attenuation like Cu. However, little research has been conducted into varying input laser frequency and scan rate at fixed absorption to understand the effects of laser light on surface oxide formation and the underlying liquid metal thermodynamic response. Here, this article conducts laser melting of 304L under Blue (450 nm) and Infrared (IR, 1064 nm) laser frequencies and examines their impact on laser oxide thickness, chemistry and coloration. We find that laser frequencies induce changes in the oxide layer thickness and chemistry that cannot be explained using conventional thermal absorption shifts and fluid dynamics. We suggest that light coupling may have thermodynamic implications for the chemical potential of the liquid metal, which may drive the observed phase behaviour. Identifying mechanisms for the tuning of the chemical potential via laser frequency control could enable the ability to control solidification microstructure and atomic ordering in laser welding and additive manufacturing processes.

304L↗
DOE OSTI2021

Design and Demonstration of AlxIn1-xP Multiple Quantum Well Light-Emitting Diodes

Direct bandgap AlxIn1-xP alloys offer an advantage for red and amber light-emitting diode (LED) operation over conventional (AlxGa1-x)0.5In0.5P alloys due to their higher direct bandgap energies. However, the coupled variation of its bandgap energy and lattice constant present challenges for fabricating quantum well-based LED devices on GaAs substrates. Here, we present the design and demonstration of AlxIn1-xP red and amber LEDs incorporating multiple quantum well structures. Strain balancing the quantum well layers and manipulating the AlxIn1-xP conduction band energy through control of spontaneous atomic ordering produce structures with higher electron confinement barriers than comparable (AlxGa1-x)0.5In0.5P LEDs. We also discuss future improvements that must be made to realize high efficiency red and amber LEDs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hardware, methodology and applications of 2 + D backscatter Mössbauer spectroscopy with simultaneous x-ray and γ-ray detection

Abstract A unique method is presented for the acquisition and analysis of 57 Fe backscatter Mössbauer spectra with simultaneous detection of the resonant 14.4 keV γ -rays and the characteristic 6.4 keV x-rays, using a custom-built multi-parameter analyser constructed on the basis of commercial analogue to digital converters and high-speed digital latches. The system allows for the simultaneous registration of Doppler-modulation velocities and photon energies, with up to 4096 and 8192 digital channels respectively. This arrangement is in contrast to most related systems, which detect at a single narrow energy window per detector. Samples of arbitrary atomic structure, morphology and surface topography can be studied without altering the setup or the analysis procedure, provided that the samples are at least micrometre sized. The hardware and software that are used to acquire data with minimal dead time are described and the custom and self-contained methods for post-measurement energy discrimination, background correction and velocity-axis folding are discussed. The data are fit using a general Hamiltonian model for the nuclear energy levels of 57 Fe and a quantum mechanical description of the angular momentum coupling is utilised, with consideration of the crystalline and chemical disorder of the sample under examination. Three examples of distinct magnetic systems, with thicknesses ranging from 5 μ m to 6 mm, that were studied using this method are presented, these are: an amorphous CoFeB-based ribbon with ultra-soft coercivity for high-frequency applications, magnetically hard Nd-Fe-B thick films on Si substrates, examined in both as-deposited and annealed states, and a sample from the nickel-rich iron meteorite NWA 6259 that contains the atomically ordered, elevated coercivity, L 1 0 phase of FeNi, tetrataenite. The wide applicability and usefulness of this method is thus demonstrated on three distinct sample morphologies that required little to no surface preparation prior to examination.

Engineering↗
DOE OSTI2026

Quasi-One-Dimensional Spin Excitations in the Iron Pnictide NaFe 0.53 ⁢Cu 0.47 ⁢As

Spectroscopic measurements in model 1D correlated systems offer insights for understanding their two-dimensional counterparts, which include the cuprate and iron pnictide/chalcogenide superconductors. A major challenge is the identification of such correlated systems with dominantly 1D physics. Here, in this Letter, inelastic neutron scattering measurements on NaFe 0.53⁢ Cu 0.47 ⁢As single crystal directly reveal quasi-1D spin excitations, resulting from atomic order that leads to magnetic Fe and nonmagnetic Cu chains. The dominant exchange interaction is antiferromagnetic along the chain [𝑆⁢𝐽 ∥ ≈ 90.1⁢(3) meV], whereas the inter-chain couplings are much weaker [𝑆⁢𝐽 ⊥ ≈ −2.4⁢(1) meV and 𝑆⁢𝐽 c ≈ 0.15⁢(5) meV]. The quasi-1D spin excitations in NaFe 0.53 ⁢Cu 0.47⁢ As stem from both the Néel and stripe vectors, with Néel excitations sensitive to Fe impurities on the Cu site. The spin excitations in quasi-1D NaFe 0.53 ⁢Cu 0.47⁢ As and quasi-2D FeSe exhibit a striking resemblance, suggesting a common origin for their coexistent stripe and Néel excitations. Our findings demonstrate magnetic dilution in NaFeAs leads to dimension reduction of its magnetic degree of freedom, presenting a strategy for discovering low-dimensional quantum materials.

Wang, Yifan [Zhejiang Univ., Hangzhou (China)]↗
DOE OSTI2025

Nucleation and growth of polar clusters with in-phase tilts into a long-range ferroelectric matrix in a sodium niobate based complex relaxor

In this study, we have investigated the temperature dependence of atomic ordering at multiple length scales in a lead-free sodium niobate-based relaxor, i.e., 0.75 NaNbO 3 -0.25 Ba 0.9⁢ Ca 0.1⁢ TiO 3 (NN-25BCT) via synchrotron x-ray diffraction, Raman spectroscopy, and pair distribution function analysis. High-resolution synchrotron x-ray powder diffraction (SXRD) measurements reveal a ferroelectric phase transition in the relaxor ferroelectric NN-25BCT below the Vogel-Fulcher freezing temperature (𝑇 VF ≈ 270 K). In addition, SXRD analysis demonstrates the competition between in-phase octahedral tilting and ferroelectric order at the long-range scale using mode crystallography. On the other hand, Raman spectroscopic analysis provides evidence of polar ordering for 𝑇 > 𝑇 VF (with tetragonal symmetry) persisting up to the Burns temperature (𝑇 B ). Furthermore, pair distribution function (PDF) analysis reveals the presence of a polar antiferrodistortive tetragonal phase with 𝑃⁢4⁢𝑏𝑚 space group at short ranges throughout the studied temperatures (i.e., 110 K ≤ 𝑇 ≤500 K), irrespective of nonpolar long-range ordering above 𝑇 VF . Therefore, our measurements provide direct evidence for the presence of polar ordering at short ranges and their gradual transformation into long-range polar ordering using an integrated multiscale structural analysis. In conclusion, as a result of a transition from relaxor to a ferroelectric phase in the vicinity of room temperature, NN-25BCT can be exploited for applications in pyroelectric detectors, electrocaloric devices, and multilayered ceramic capacitors.

36 MATERIALS SCIENCE↗
NASA NTRS1985

ac susceptibility of thermally annealed and neutron irradiated Cu-Ni alloys

Thermal annealing and high-flux neutron irradiation are used to vary the degree of short-range atomic order in Cu-Ni alloys of composition 40, 50, and 60 at. pct Ni. The magnetic state is measured by ac magnetic susceptibility measurements. It is shown that annealing at 350 C causes significant changes in the susceptibility of all the samples. In the 50 and 60 at. pct Ni samples, the transition is broadened and extended to higher temperatures, while the 40 at. pct Ni sample changes from a paramagnetic system to a weakly ferromagnetic system. The neutron irradiation, in contrast to the thermal treatment, causes the development of smaller size cluster formations. The irradiated 60 at. pct Ni sample exhibits no change in the shape of its susceptibility curve from that of the quenched sample, whereas, the 40 pct alloy is changed, by irradiation, from a paramagnetic system to a spin-glass system.

Catchings, R. M., III↗
NASA NTRS1995

A Method Based on Radiative Cooling for Detecting Structural Changes in Undercooled Metallic Liquids

We introduce a structure-sensitive parameter for undercooled melts which can be measured in containerless processing experiments. We have established that the ratio, R(T), of hemispherical total emissivity epsilon(sub T)(T) to constant-pressure specific heat c(sub p)(T) can serve as an indicator which is sensitive to any changes in short range atomic order in undercooled metallic melts. R(T) (triple bonds) epsilon(sub T)(T)/c(sub p)(T) values for nickel, zirconium, and silicon have been obtained using the high temperature electrostatic levitator while the levitated melts were undergoing purely radiative cooling into the deeply undercooled region. R(T) plots for undercooled liquid nickel and zirconium indicate no significant change in short-range structure from their melting temperatures to 15% undercooling. In contrast, liquid silicon shows marked short-range structural changes beginning above its melting temperature and extending throughout the undercooled region. The short-range structure of liquid silicon is related to the highly-directional covalent bonding which characterizes its solid form. The nickel and zirconium data show that epsilon(sub T) varies linearly with T, in support of metal emissivity theories.

Rulison, Aaron J.↗
NASA NTRS2004

Science Using an Electrostatic Levitation Furnace in the MUCAT Sector at the APS

The original motivation for the construction of the BESL prototype was to obtain the first proof of a 50-year-old hypothesis regarding the solidification of liquid metals. Since the 1950s it has been known that under proper conditions liquid metals can be cooled below their melting temperature (undercooled) without crystallizing to the stable solid phase. In 1952 Frank proposed that this was because the atoms in the metallic liquid were arranged with the symmetry of an icosahedron, a Platonic solid consisting of 20 tetrahedra (4-sided pyramid-shaped polyhedra) arranged around a common center. Since this local atomic order is incompatible with the long-range translational periodicity of crystal phases, a barrier is formed to the formation of small regions of the crystal phase, the nucleation barrier. A proof of Frank's hypothesis required a direct correlation between measured icosahedral order in the undercooled liquid and the nucleation barrier. The tendency of sample containers to catalyze nucleation obscured this relation, requiring containerless techniques. Combining containerless processing techniques for electrostatically levitated droplets (ESL) with x-ray synchrotron methods, a team from Washington University, St. Louis, MO, NASA Marshall Space Flight Center, and MUCAT at the APS demonstrated an increasing icosahedral order in TiZrNi liquids with decreasing temperature below the melting temperature. The increased icosahedral order caused the transformation of the liquid to a metastable icosahedral quasicrystal phase, instead of the stable tetrahedrally-coordinated crystal intermetallic, giving the first clear demonstration of the connection between the nucleation barrier and the local structure of the liquid, verifying Frank's hypothesis for this alloy.

Goldman, A.↗
NASA NTRS2015

Recognizing Sulfate and Phosphate Complexes Adsorbed onto Nanophase Weathering Products on Mars

Nanophase weathering products (i.e., secondary phases that lack long-range atomic order) have been recognized on the martian surface via orbital observations and in-situ measurements from landed missions. Allophane, a poorly crystalline, hydrated aluminosilicate, has been identified at the regional scale in models of thermal-infrared (TIR) data from the Thermal Emission Spectrometer (TES) and at the local scale from visible/near-IR (VNIR) data from the Compact Reconnaissance Impact Spectrometer for Mars (CRISM) instrument and phase calculations of Alpha Particle X-ray Spectrometer (APXS) data of rocks encountered by the Mars Exploration Rovers (MER) Spirit and Opportunity. Nanophase iron oxides (npOx) have been recognized in rocks and soils measured by the Mössbauer Spectrometer on Spirit and Opportunity. Furthermore, analyses of X-ray diffraction data measured by the CheMin instrument onboard the Mars Science Laboratory rover Curiosity indicate rock and soil samples are comprised of approx. 20-50 wt.% X-ray amorphous materials. Chemical measurements by landed missions indicate the presence of sulfur and phosphorus in martian rocks in soils, and APXS data from Gusev crater demonstrate abundances of up to approx. 5 wt.% P2O5 and approx. 30 wt.% SO3. However, the speciation of phosphorus and sulfur is not always evident. On Earth, phosphate and sulfate anions can be chemisorbed onto the surfaces of nanophase weathering products. This process may also occur on Mars, and calculations of the composition of the amorphous component at Gale crater using CheMin mineral models and APXS data show that amorphous material is enriched in volatiles, including S. Here, we examine the ability to detect chemisorbed sulfate and phosphate complexes by analyzing sulfate- and phosphate-adsorbed nanophase weathering products using instruments similar to those on landed and orbital missions.

Rampe, E. B.↗
NASA NTRS2019

Physically-Informed Artificial Neural Networks for Atomistic Modeling of Materials

A new approach is presented for the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. A number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials are demonstrated. Some of the specific materials systems include silicon and aluminum, as well as alloys and compounds. Computational aspects of PINN potentials are discussed and future developments in this field are outlined.

J Hickman↗