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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Coupled order parameters and photoinduced domain walls in the charge density wave of (TaSe 4 ) 2 I

The charge density wave in (TaSe 4 ) 2 I has drawn much attention recently as a controversial candidate for an axion insulator where the CDW breaks the chiral symmetry of the Weyl semimetal. Here we use ultrafast x-ray scattering to study the collective modes of this CDW. By measuring several diffraction peaks we find that the order parameter involves coupled optical and acoustic modes. For strong near-infrared excitation, the dynamics of the x-ray diffraction show evidence of photoinduced inversion of both components of the CDW order parameter, and associated domain walls. These results demonstrate the potential of ultrafast methods to induce topological defects through highly nonequilibrium dynamics. In (TaSe 4 ) 2 I these defects should lead to exotic electronic states due to the nontrivial topology of the band structure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

X-ray diffraction technique

This is a guest lecture for University of Idaho graduate-level course Chemical and Materials Analysis. I will be talking about the fundamentals of X-ray Diffraction technique and some examples for its application in material science research.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enabling phase quantification of anhydrous cements via Raman imaging

The phase composition of Portland cements is typically determined using conventional techniques like X-ray Diffraction (XRD) Rietveld analysis, optical microscopy point counting, and electron microscopy. However, these techniques have several limitations that may affect their accuracy in certain sample-specific scenarios. Here, we report a highly accurate phase quantification of 11 different types of commercial, anhydrous cements using a new and complementary technique: Raman imaging. Specifically, for the 4 principal phases, composition from our extensive data (250,000 Raman spectra per sample, error < 0.71%) and those obtained from XRD Rietveld and supplier data have high coefficients of determination (R{sup 2} > 0.98, mean deviation <2%). Additionally, we also quantify 8 secondary phases present in cement clinkers (gypsum, anhydrite, bassanite, syngenite, dolomite, calcite, quartz, and portlandite) with a high degree of confidence, thereby demonstrating that Raman imaging is a highly versatile tool for anhydrous phase quantification in a broad variety of cements.

36 MATERIALS SCIENCE↗
DOE OSTI2020

HOSS: Hybrid Optimization Software Suite

HOSS software, developed at LANL, is a powerful, flexible, and parallelized package that enables the user to predict deformation and failure of materials in a variety of situations. LANL’s HOSS software is an R&D 100 finalist that integrates solid mechanics and computational fluid dynamics into a multi-physics platform able to handle millions of interacting and fracturing solids and/or particle systems. This nextgeneration simulation software integrates solid mechanics and computational fluid dynamics into a multi-physics platform able to provide state-of-the-art simulation solutions for a wide field of applications including oil and gas, construction, mining, defense, material sciences, and biomechanics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

MST e-News Fall 2021

The society’s highly competitive Bronze Medal—only two were awarded—recognizes ASM members who are in early-career positions for their significant contributions in the field of materials science and engineering through technical content and service to ASM and the materials science profession.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis

Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, in this study, we demonstrate an approach to enable real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe 2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe 2 . The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Photoinduced correlations in stochastic dynamics of a solid-state ionic conductor

Photoexcitation by ultrashort laser pulses plays a crucial role in controlling reaction pathways, creating nonequilibrium material properties, and probing complex molecular dynamics. The photoresponse following a laser pulse is generally nonidentical between exposures due to spatiotemporal fluctuations or the stochastic nature of dynamical pathways. However, most ultrafast pump-probe experiments struggle to distinguish intrinsic sample fluctuations from extrinsic apparatus noise, often missing deviations from the averaged response. Leveraging the stability and high photon flux of time-resolved X-ray micro-diffraction at a synchrotron, we characterized stochastic photoinduced dynamics in a solid-state ionic conductor. By analyzing temporal evolutions of the lattice parameter of a single grain, we found that shot-to-shot fluctuations are not independent. Instead, correlations exist between nonequilibrium lattice trajectories following adjacent shots, with a characteristic correlation length of approximately 1500 shots, corresponding to an energy barrier of 0.4 ± 0.1 eV, close to the activation energy of lithium-ion diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2024

First-principles study of the magneto-Raman effect in van der Waals layered magnets

Magneto-Raman spectroscopy has been used to study spin-phonon coupling in two-dimensional (2D) magnets. Raman spectra of CrI 3 show a strong dependence on the magnetic order within a layer and between the layers. Here we carry out the first systematic theoretical investigation of the magneto-Raman effect in 2D magnets by performing density functional theory calculations and developing a generalized polarizability model. Our first-principles simulations well reproduce experimental Raman spectra of CrI 3 with different magnetic states. The model reveals how the change of spin orientation in each layer is coupled to the layer’s vibration to induce or eliminate the spin-dependent anti-symmetric off-diagonal terms in the Raman tensor for altering the selection rules. We also uncover that the correlation between phonon modes and magnetic orders is a universal phenomenon, which should exist in other phonon modes and 2D magnets. Our predictive simulations and modeling are expected to guide the research in 2D magnets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polarization rotation in a ferroelectric BaTiO 3 film through low-energy He-implantation

Domain engineering in ferroelectric thin films is crucial for next-generation microelectronic and photonic technologies. Here, a method is demonstrated to precisely control domain configurations in BaTiO 3 thin films through low-energy He ion implantation. The approach transforms a mixed ferroelectric domain state with significant in-plane polarization into a uniform out-of-plane tetragonal phase by selectively modifying the strain state in the film’s top region. This structural transition significantly improves domain homogeneity and reduces polarization imprint, leading to symmetric ferroelectric switching characteristics. The demonstrated ability to manipulate ferroelectric domains post-growth enables tailored functional properties without compromising the coherently strained bottom interface. The method’s compatibility with semiconductor processing and ability to selectively modify specific regions make it particularly promising for practical implementation in integrated devices. This work establishes a versatile approach for strain-mediated domain engineering that could be extended to a wide range of ferroelectric systems, providing new opportunities for memory, sensing, and photonic applications where precise control of polarization states is essential.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Interplay of ferromagnetic and antiferromagnetic interactions in epitaxial Co 3 ZnN

Antiperovskite nitrides with the general formula M 3 AN have attracted significant attention due to their tunable electronic and magnetic properties. Among them are many cobalt-based compounds predicted to exhibit high thermodynamic stability and intriguing magnetic behavior. Here, we report the synthesis and magnetic characterization of epitaxial Co3ZnN thin films grown by radio frequency sputtering on SrTiO 3 (STO) and MgO substrates. X-ray diffraction confirms phase-pure (00l)-oriented films with cube-on-cube epitaxy on STO, with a c-lattice parameter of 3.752 Å. Magnetic measurements reveal clear hysteresis at 2 K with a coercive field of ∼0.11 T and a small net moment of 0.108 μ B /f.u., suggesting either a canted antiferromagnetic (AFM) or ferrimagnetic (FiM) configuration. Temperature-dependent magnetization measurements show a transition near 25 K, with strong AFM interactions with Curie–Weiss temperature (Θ) = −80.13 K. Complementary density functional theory and Monte Carlo simulations indicate a ferromagnetic (FM) ground state, with the FM–AFM energy difference decreasing systematically with increasing supercell size, consistent with competition between FM and AFM/FiM interactions. These results highlight Co3ZnN as a magnetically complex antiperovskite nitride with competing exchange interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design of nematic liquid crystals to control microscale dynamics

The dynamics of small particles, both living such as swimming bacteria and inanimate, such as colloidal spheres, has fascinated scientists for centuries. If one could learn how to control and streamline their chaotic motion, that would open technological opportunities in the transformation of stored or environmental energy into systematic motion, with applications in micro-robotics, transport of matter, guided morphogenesis. This review presents an approach to command microscale dynamics by replacing an isotropic medium with a liquid crystal. Orientational order and associated properties, such as elasticity, surface anchoring, and bulk anisotropy, enable new dynamic effects, ranging from the appearance and propagation of particle-like solitary waves to self-locomotion of an active droplet. By using photoalignment, the liquid crystal can be patterned into predesigned structures. In the presence of the electric field, these patterns enable the transport of solid and fluid particles through nonlinear electrokinetics rooted in anisotropy of conductivity and permittivity. Director patterns command the dynamics of swimming bacteria, guiding their trajectories, polarity of swimming, and distribution in space. This guidance is of a higher level of complexity than a simple following of the director by rod-like microorganisms. Namely, the director gradients mediate hydrodynamic interactions of bacteria to produce an active force and collective polar modes of swimming. The patterned director could also be engraved in a liquid crystal elastomer. When an elastomer coating is activated by heat or light, these patterns produce a deterministic surface topography. The director gradients define an activation force that shapes the elastomer in a manner similar to the active stresses triggering flows in active nematics. Finally, the patterned elastomer substrates could be used to define the orientation of cells in living tissues. The liquid-crystal guidance holds a major promise in achieving the goal of commanding microscale active flows.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning continuous scattering length density profiles from neutron reflectivities using convolutional neural networks

Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and physics-based models provides information about spatially resolved neutron scattering length density (NSLD) profiles. Recent improvements in data acquisition systems have allowed acquiring thousands of NR curves in a couple of hours, which has led to a need for automated data analysis tools to interpret NR measurements in real-time. Here, we present a machine learning analysis workflow that uses a series of models, based on a convolutional neural network (CNN), to learn the relation between the NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly from NRs. The usefulness of our CNN-based models is demonstrated by constructing NSLDs from NRs of several films containing homopolymer polyzwitterions and diblock copolymers mixed with different types of salts. Comparisons of the NSLDs with those constructed using ad hoc multi-layer models reveal a very good agreement, suggesting the potential of CNN-based models for real-time automated data analysis of NRs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spin Decoherence Dynamics of Er 3+ in CeO 2 Films

Developing telecom-compatible spin-photon interfaces is essential towards scalable quantum networks. Erbium ions (Er 3+ ) exhibit a unique combination of a telecom (1.5 mu m) optical transition and an effective spin-1=2 ground state, but identifying a host that enables heterogeneous device integration while preserving long optical and spin coherence remains an open challenge. In this work, we explore the potential of Er 3+ :CeO 2 films on silicon and study the Er 3+ spin coherence, offering low nuclear spin density and the potential for on-chip integration. We demonstrate a 38.8 mu s spin coherence, which can be extended to 176.4 mu s with dynamical decoupling. Pairing experiments with cluster correlation expansion calculations, we identify spectral diffusion induced by bath Er 3+ spin flip-flops as the dominant decoherence mechanism and provide pathways to millisecond-scale coherence.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Maximal Entanglement Limit in Statistical and High-energy Physics

These lectures advocate the idea that quantum entanglement provides a unifying foundation for both statistical physics and high-energy interactions. I argue that, at sufficiently long times or high energies, most quantum systems approach a Maximal Entanglement Limit (MEL) in which phases of quantum states become unobservable, reduced density matrices acquire a thermal form, and probabilistic descriptions emerge without invoking ergodicity or classical randomness. Within this framework, the emergence of probabilistic parton model, thermalization in the break-up of confining strings and in high-energy collisions, and the universal small-x behavior of structure functions arise as direct consequences of entanglement and geometry of high-dimensional Hilbert space.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Relating Texture and Thermomechanical Processing Variables in Mg–Zn–Ca Alloys

It is well known that the strong basal texture commonly produced in magnesium alloy sheets leads to poor formability at room temperature. A sizable body of work has explored how changing the alloy composition and rolling conditions can yield more desirable textures; however important thermomechanical variables, such as the feed rate during rolling, are often not included in the literature, making it difficult to correlate how changes in processing affect the final crystallographic texture. This work explores the texture evolution and grain refinement in Mg-Zn-Ca alloys during plane strain compression (PSC) using a Gleeble thermomechanical simulator. This instrument allows for precise control and capture of the thermomechanical history of the sample. The texture and grain morphology of the compressed samples were characterized using Electron Backscatter Diffraction (EBSD). The texture results will be used to identify which alloys and processing conditions should be scaled up for future rolling studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhancing fatigue life by ductile-transformable multicomponent B2 precipitates in a high-entropy alloy

Catastrophic accidents caused by fatigue failures often occur in engineering structures. Thus, a fundamental understanding of cyclic-deformation and fatigue-failure mechanisms is critical for the development of fatigue-resistant structural materials. Here we report a high-entropy alloy with enhanced fatigue life by ductile-transformable multicomponent B2 precipitates. Its cyclic-deformation mechanisms are revealed by real-time in-situ neutron diffraction, transmission-electron microscopy, crystal-plasticity modeling, and Monte-Carlo simulations. Multiple cyclic-deformation mechanisms, including dislocation slips, precipitation strengthening, deformation twinning, and reversible martensitic phase transformation, are observed in the studied high-entropy alloy. Its improved fatigue performance at low strain amplitudes, i.e., the high fatigue-crack-initiation resistance, is attributed to the high elasticity, plastic deformability, and martensitic transformation of the B2-strengthening phase. This study shows that fatigue-resistant alloys can be developed by incorporating strengthening ductile-transformable multicomponent intermetallic phases.

36 MATERIALS SCIENCE↗