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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Interface-enhanced conductivities in surfactant-mediated, solution-grown ionic crystalline complexes

Renewable energy is increasingly relying on optimized electrolytes and interfaces. In this work, Tween 20 and sodium chloride are selected as a model system to reveal the effects of surfactants on salt crystallization in the context of ionic conductivity and interface optimization. At a varied crystallization speed and mix ratio, it is demonstrated that the resultant solution-grown ionic crystalline complexes can achieve a highly tunable ion transport with a controllable crystalline interface. X-ray diffraction results rule out the possibility of polymorphism in the NaCl/Tween 20 systems, which further supports the importance of an optimized crystalline network for optimizing permittivity or ionic conductivity. Raman mapping and machine learning techniques are used to perform semantic segmentation on highly heterogeneous NaCl/Tween 20 complexes. Furthermore, FTIR measurements demonstrate that inter- and intra-molecular interactions play critical roles in the formation of these crystals. This work lays a foundation toward future optimization of such complex ion systems for a specific salt or crystallization modifier in energy storage or ion transport applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamics and Structure of Molten Neodymium Titanate

Binary rare earth titanates can be vitrified using levitation melt quenching, and their glasses are promising for optical applications. To better understand their thermophysical properties and dynamics, we have used molecular dynamics (MD) and empirical potential structure refinement (EPSR) to simulate molten neodymium titanate, 83TiO 2 –17Nd 2 O 3 . The models are compared with high-energy synchrotron X-ray diffraction of the melt structure over 1060–1960 K and thermophysical property measurements of electrostatically levitated droplets over 1500–2200 K. As temperature increases, the network periodicity increases and the correlation length decreases, both in monotonic trends that suggest continuous structural evolution through the melt and supercooled liquid regimes. At 1900 K, the mean Ti–O and Nd–O coordination numbers are 5.8(5) and 6.7(5), compared to the glass, 5.72(6) and 7.70(26). The equilibrium melting point is measured as 1860(20) K, over 100 K higher than previous estimates. Viscosity data from MD are in good agreement with experimental measurements. The dynamic propensity of Nd is similar to that of the network-forming Ti, suggesting that Nd does not participate as a conventional modifier. The viscosity of this fragile liquid is likely controlled by the breaking of cation–oxygen bonds and rearrangements of Ti–O polyhedra.

molecular dynamics↗
DOE OSTI2023

A high-strength precipitation hardened cobalt-free high-entropy alloy

Recent studies on precipitation-hardened high-entropy alloys (HEAs) demonstrate their high strength and thermal stability, making them promising materials for high-temperature structural applications such as nuclear reactors. However, many existing HEAs contain cobalt (Co), which is unsuitable for nuclear applications because of the long-term activation issue of Co. Co is also expensive and considered a critical material for other applications. Therefore, it is desired to exclude Co from the composition. A Co-free (Fe 0.3 Ni 0.3 Mn 0.3 Cr 0.1 ) 88 Ti 4 Al 8 HEA was developed and studied in this work. In contrast to previous Co-free HEAs, this alloy is close to equiatomic in its composition and promises a more pronounced high-entropy effect. Scanning electron microscopy, transmission electron microscopy, atom probe tomography, and synchrotron-based, high-energy X-ray diffraction were used to characterize this alloy and revealed a complex four-phase structure, with an FCC matrix, γ’ precipitates, and a network of B2 and χ phase particles. This structure granted 2151 MPa compressive strength and good thermal stability, but with limited ductility and slow precipitation kinetics. A strengthening analysis of the alloy shows that the B2 and χ provided the most significant strengthening contribution, adding 312 MPa and 788 MPa respectively. Furthermore the strengthening effect from the nanoscale γ' is also considerable, adding 608 MPa in total. This study lays the foundation for the continued development of high-strength Co-free HEAs with improved and satisfactory ductility.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Atomic Structure, Dynamics, Changes in Chemical Bonding and Semiconductor-Metal Transition in Sb 2 Se 3 : A Remarkable Material for Quantum Networks and Energy Applications

Antimony sesquiselenide has become an outstanding functional material for photovoltaics, energy storage and transformation, memory and photonic applications. Sb 2 Se 3 is one of the most successful emerging solar light absorbers and has also been identified as a highly promising ultralow-loss phase-change material (PCM) for next-generation coherent nanophotonic processors, photonic tensor cores, quantum and neuromorphic networks. Unlike benchmark telluride PCMs, Sb 2 Se 3 features a quasi-one-dimensional (1D) crystalline structure consisting of (Sb 4 Se 6 ) ∞ ribbons, lacks the typical PCM chemical bonding, and undergoes an extended semiconductor-metal transition above the melting point. Consequently, the origin of high optical contrast between crystalline (SET) and amorphous (RESET) logic states remains elusive and presents a significant challenge. Using high-energy X-ray diffraction and Raman spectroscopy over a wide temperature range, supported by first-principles simulations and complemented by thermal, optical and electrical measurements, as well as by 121 Sb-Mossbauer spectroscopy, the quasi-1D network of orthorhombic antimony sesquiselenide was found to undergo significant evolution in amorphous and supercooled Sb 2 Se 3 , leading to lower coordination, shorter interatomic distances and a higher p-electron density on antimony, indicating changes in chemical bonding. The observed novel Sb 2 Se 3 nanocrystalline polymorph, characterized by trigonal antimony coordination and more isolated Sb-Se ribbons, could help reduce multiple trapping defect states in the bandgap, which are typical of orthorhombic Sb 2 Se 3 , thereby enhancing the power-conversion efficiency of photovoltaic devices. Semimetallic and metallic liquid Sb 2 Se 3 exhibit a gradual transformation into a denser 2D and/or 3D network with higher antimony coordination. Localized electron states in the pseudogap are becoming extended, leading to an increase in electronic conductivity σ following the relationship σ ∝ N(E F ) 2 . Liquid Sb 2 Se 3 also appears to be strongly fragile, with a nonmonotonic change in viscosity and higher atomic mobility in the metallic liquid. Furthermore, these results explain extraordinary functionalities of Sb 2 Se 3 for photonic and energy applications.

antimony↗
DOE OSTI2022

Characterizing defect structures in AM steel using direct electron detection EBSD

The mechanical properties of additive and traditionally manufactured alloys are largely dependent on the characteristics and distribution of dislocation cell networks that develop during the fabrication process. This work demonstrates the ability to quantitatively characterize these dislocation structures by high angular resolution electron backscatter diffraction analysis using a direct electron detector. The defect structures are characterized in terms of the geometrically necessary dislocation density and the associated Burgers vector and line direction. Furthermore, the results are discussed in terms of potential defect formation mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An additively manufactured near-eutectic Al-Ce-Ni-Mn-Zr alloy with high creep resistance

A new additively manufactured (AM) Al-7.5Ce-4.5Ni-0.4Mn-0.7Zr (wt.%) near-eutectic alloy is reported, which shows unprecedented creep resistance up to 400 °C (a homologous temperature of 0.72). The eutectic solidification microstructure comprises ~ 27 vol% of coarsening-resistant second phase network with an ultrafine (<100 nm) inter-phase spacing. Both Mn and Zr contribute to creep resistance of the alloy. Small amount of Mn addition promotes selection of coarsening resistant phases without compromising the alloy processability. Zr not only improves hot-tearing resistance, but further enhances the second phase coarsening resistance resulting in improved creep resistance. Neutron diffraction performed during creep deformation reveals that the underlying mechanism for creep resistance in this alloy is impedance to dislocation motion stemming from the ultrafine eutectic solidification microstructure, whereas load transfer strengthening becomes less effective as the creep temperature increases. The second phase forms a continuous network in the as-fabricated condition, which is maintained during long-term creep at 300 °C. However, this network is fragmented into fine dispersoids at higher temperatures. It is proposed that the rate-limiting deformation mechanism at 300–400 °C is (i) dislocation climb for the alloy with fragmented second phase dispersoids and (ii) Orowan looping for the alloy with a continuous second phase network. In conclusion, the present design of an AM-processable multicomponent eutectic alloy with high creep resistance can be applied to other metallic systems exhibiting eutectic reactions, with expected extreme creep resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗
DOE OSTI2026

La 3+ Networks and Speciation in the Molten State: Impact of Spacer Salt Selection on Structural Heterogeneity

We recently introduced the concept of a “spacer salt” that creates structural heterogeneity and intermediate range order. Put simply, a fully networked salt melt, such as LaCl 3 or UCl 3 , becomes disrupted by the introduction of ions that do not participate in the network. One of the results of this disruption is the experimental observation of two characteristic distances between the multivalent cations: the shorter “in-network” distance and the longer “across-network” distance spaced by the lowvalency salt. The longer characteristic distance, absent if there is no spacer salt, is the culprit for a new first sharp diffraction peak in scattering experiments. Intuitively, it would appear to follow from this analysis that higher concentrations of the lower-valency salt would further separate multivalent cations, resulting in a shift to lower q values of this first sharp diffraction peak. We will show experimentally and computationally that this is not always the case because multiple other factors enter into play.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
DOE OSTI2021

Rapid 3D nanoscale coherent imaging via physics-aware deep learning

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Mapping the structural trends in zinc aluminosilicate glasses

The structure of zinc aluminosilicate glasses with the composition (ZnO) x (Al 2 O 3 ) y (SiO 2 ) 1-x-y , where 0 ≤ x < 1, 0 ≤ y < 1, and x + y < 1, was investigated over a wide composition range by combining neutron and high-energy x-ray diffraction with 27 Al magic angle spinning nuclear magnetic resonance spectroscopy. The results were interpreted using an analytical model for the composition-dependent structure in which the zinc ions do not act as network formers. Four-coordinated aluminum atoms were found to be in the majority for all the investigated glasses, with five-coordinated aluminum atoms as the main minority species. Mean Al–O bond distances of 1.764(5) and 1.855(5) Å were obtained for the four- and five-coordinated aluminum atoms, respectively. The coordination environment of zinc was not observed to be invariant. Instead, it is dependent on whether zinc plays a predominantly network-modifying or charge-compensating role and, therefore, varies systematically with the glass composition. The Zn–O coordination number and bond distance were found to be 4.36(9) and 2.00(1) Å, respectively, for the network-modifying role vs 5.96(10) and 2.08(1) Å, respectively, for the charge-compensating role. The more open coordination environment of the charge-compensator is related to an enhanced probability of zinc finding bridging oxygen atoms as nearest-neighbors, reflecting a change in the connectivity of the glass network comprising four-coordinated silicon and aluminum atoms as the alumina content is increased.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Resolving intragranular stress fields in plastically deformed titanium using point-focused high-energy diffraction microscopy

Abstract The response of a polycrystalline material to a mechanical load depends not only on the response of each individual grain, but also on the interaction with its neighbors. These interactions lead to local, intragranular stress concentrations that often dictate the initiation of plastic deformation and consequently the macroscopic stress–strain behavior. However, very few experimental studies have quantified intragranular stresses across bulk, three-dimensional volumes. In this work, a synchrotron X-ray diffraction technique called point-focused high-energy diffraction microscopy (pf-HEDM) is used to characterize intragranular deformation across a bulk, plastically deformed, polycrystalline titanium specimen. The results reveal the heterogenous stress distributions within individual grains and across grain boundaries, a stress concentration between a low and high Schmid factor grain pair, and a stress gradient near an extension twinning boundary. This work demonstrates the potential for the future use of pf-HEDM for understanding the local deformation associated with networks of grains and informing mesoscale models. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Skyrmion‐Excited Spin‐Wave Fractal Networks

Magnetic skyrmions exhibit unique, technologically relevant pseudo-particle behaviors which arise from their topological protection, including well-defined, 3D dynamic modes that occur at microwave frequencies. During dynamic excitation, spin waves are ejected into the interstitial regions between skyrmions, creating the magnetic equivalent of a turbulent sea. However, since the spin waves in these systems have a well-defined length scale, and the skyrmions are on an ordered lattice, ordered structures from spin-wave interference can precipitate from the chaos. This work uses small-angle neutron scattering (SANS) to capture the dynamics in hybrid skyrmions and investigate the spin-wave structure. Performing simultaneous ferromagnetic resonance and SANS, the diffraction pattern shows a large increase in low-angle scattering intensity, which is present only in the resonance condition. This scattering pattern is best fit using a mass fractal model, which suggests the spin waves form a long-range fractal network. The fractal structure is constructed of fundamental units with a size that encodes the spin-wave emissions and are constrained by the skyrmion lattice. Furthermore, these results offer critical insights into the nanoscale dynamics of skyrmions, identify a new dynamic spin-wave fractal structure, and demonstrate SANS as a unique tool to probe high-speed dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Octahedral oxide glass network in ambient pressure neodymium titanate

Abstract Rare-earth titanates form very fragile liquids that can be made into glasses with useful optical properties. We investigate the atomic structure of 83TiO 2 -17Nd 2 O 3 glass using pair distribution function (PDF) analysis of X-ray and neutron diffraction with double isotope substitutions for both Ti and Nd. Six total structure factors are analyzed (5 neutron + 1 X-ray) to obtain complementary sensitivities to O and Ti/Nd scattering, and an empirical potential structure refinement (EPSR) provides a structural model consistent with the experimental measurements. Glass density is estimated as 4.72(13) g cm −3 , consistent with direct measurements. The EPSR model indicates nearest neighbor interactions for Ti-O at $$\overline{r}_{TiO}$$ r ¯ TiO = 1.984(11) Å with coordination of $$n_{TiO}$$ n TiO = 5.72(6) and for Nd-O at $$\overline{r}_{NdO}$$ r ¯ NdO = 2.598(22) Å with coordination of $$n_{NdO}$$ n NdO = 7.70(26), in reasonable agreement with neutron first order difference functions for Ti and Nd. The titanate glass network comprises a mixture of distorted Ti-O 5 and Ti-O 6 polyhedra connected via 71% corner-sharing and 23% edge-sharing. The O-Ti coordination environments include 15% nonbridging O-Ti 1 , 51% bridging O-Ti 2 , and 32% tricluster O-Ti 3 . This structure is highly unusual for oxide glasses melt-quenched at ambient pressure, as it consists of Ti-O x predominantly in octahedral (with nearly no tetrahedral) coordination.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructural evolution in shear-punch tests: A comparative study of pure Cu and Cu-Cr alloy

Understanding the mechanisms behind microstructural evolution during shear deformation has been a long-standing area of interest. However, establishing a connection between microstructure, mechanical properties, and extent of shear deformation is challenging and requires refined experimental approaches. Shear-punch testing (SPT) provides a controlled method to introduce shear into small volumes of material that later can be subjected to detailed microstructural characterization. In this study, we utilize an SPT device to induce shear deformation to pure copper and a binary copper-chromium alloy. Electron backscatter diffraction and transmission electron microscopy were used to study the mechanisms of plastic deformation after SPT. Our results indicate that shear deformation of pure Cu produces a dense network of intercepting microshear bands upon sustained deformation. Twin boundaries undergo degradation into high angle grain boundaries due to simultaneous deviation from the axis-angle pair condition of 60° misorientation on [111] direction. The presence of 50% volume Cr particles in the soft Cu matrix fundamentally altered the shear deformation mechanism. Preferential deformation of the Cu matrix led to accelerated shear-induced formation of low and high angle grain boundaries, and subsequent grain refinement. Comparatively, no grain refinement occurred in pure Cu at a strain ~10 times larger (ϵ = 4.73) than that of the copper-chromium case (ϵ = 0.42). Overall, our study sheds light on the microstructural evolution during shear deformation and highlights the significant influence of particle reinforcements on the shear deformation mechanisms of metals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis

X-ray free electron laser experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the “droplet-type” models, which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent x-ray speckle patterns, our algorithm achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now has not been accessible. Our artificial intelligence-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work.

47 OTHER INSTRUMENTATION↗