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Results for “structural ordering”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Molecular Dynamics study of Pb overlayer on Cu(100)

Isothermal-isobaric Molecular Dynamics (MD) simulation of a submonolayer Pb film in c(2x2) ordered structure adsorbed on a Cu(100) substrate showed retention of order to high T. The Embedded Atom Method (EAM) calculated the energy of atoms of overlayer and substrate. The time-averaged squared modulus of the two dimensional structure factor for the Pb overlayer measured the order of the overlayer. The results are for increasing T only, and require verification by simulated cooling.

Karimi, M.↗
DOE OSTI2020

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Three-dimensional atomic structure and local chemical order of medium- and high-entropy nanoalloys

Medium- and high-entropy alloys (M/HEAs) mix several principal elements with near-equiatomic composition and represent a model-shift strategy for designing previously unknown materials in metallurgy, catalysis and other fields. One of the core hypotheses of M/HEAs is lattice distortion, which has been investigated by different numerical and experimental techniques. However, determining the three-dimensional (3D) lattice distortion in M/HEAs remains a challenge. Moreover, the presumed random elemental mixing in M/HEAs has been questioned by X-ray and neutron studies, atomistic simulations, energy dispersive spectroscopy and electron diffraction, which suggest the existence of local chemical order in M/HEAs. However, direct experimental observation of the 3D local chemical order has been difficult because energy dispersive spectroscopy integrates the composition of atomic columns along the zone axes and diffuse electron reflections may originate from planar defects instead of local chemical order. Here, in this work, we determine the 3D atomic positions of M/HEA nanoparticles using atomic electron tomography and quantitatively characterize the local lattice distortion, strain tensor, twin boundaries, dislocation cores and chemical short-range order (CSRO). We find that the high-entropy alloys have larger local lattice distortion and more heterogeneous strain than the medium-entropy alloys and that strain is correlated to CSRO. We also observe CSRO-mediated twinning in the medium-entropy alloys, that is, twinning occurs in energetically unfavoured CSRO regions but not in energetically favoured CSRO ones, which represents, to our knowledge, the first experimental observation of correlating local chemical order with structural defects in any material. We expect that this work will not only expand our fundamental understanding of this important class of materials but also provide the foundation for tailoring M/HEA properties through engineering lattice distortion and local chemical order.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Structure-Dependent Lithium Metal Reactivity of Lithium Lanthanum Titanium Oxide Solid Electrolytes

Ongoing efforts to design stable, ionically conductive solid-state electrolytes (SSEs) for next-generation solid-state batteries make it clear that both long and short-range structural order strongly influence materials performance. However, clear structure-property relationships are generally lacking, making it difficult to develop design rules for improving the (electro) chemical stability of SSEs. Here, in this work, we synthesize epitaxial, single-crystal lithium lanthanum titanium oxide (LLTO) films and demonstrate that the kinetics of Ti 4+ reduction and lithium intercalation depend sensitively on the crystal orientation, with electrochemical stability increasing as LLTO (001) < (110) similar to (112) < (100). However, thermodynamic stability is ultimately unaffected-all orientations fully reduce after extended contact with Li metal. In contrast, amorphous LLTO films exhibit minimal, self-limiting reactivity that results in an interface that is stable to extended contact with Li metal. The results demonstrate the potential to engineer crystal lattice strain and long-range order to differentially tune the stability of the solid electrolyte toward reactive lithium metal and cathode materials, suggesting strategies for enabling the wider deployment of LLTO and other ionically conductive ceramic films in advanced energy storage technologies

Ketter, Benjamin [Argonne National Laboratory (ANL↗
DOE OSTI2021

Toward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics

Understanding the driving forces behind the nucleation of different polymorphs is of great importance for material sciences and the pharmaceutical industry. This includes understanding the reaction coordinate that governs the nucleation process and correctly calculating the relative free energies of different polymorphs. Here, we demonstrate, for the prototypical case of urea nucleation from the melt, how one can learn such a one-dimensional reaction coordinate as a function of prespecified order parameters and use it to perform efficient biased all-atom molecular dynamics simulations. The reaction coordinate is learnt as a function of the generic thermodynamic and structural order parameters using the “spectral gap optimization of order parameters (SGOOP)” approach [Tiwary, P. and Berne, B. J. Proc. Natl. Acad. Sci. U.S.A. (2016)] and is biased using well-tempered metadynamics simulations. The reaction coordinate gives insights into the role played by different structural and thermodynamics order parameters, and the biased simulations obtain accurate relative free energies for different polymorphs. This includes an accurate prediction of the approximate pressure at which urea undergoes a phase transition and one of the metastable polymorphs becomes the most stable conformation. Here, we believe the ideas demonstrated in this work will facilitate efficient sampling of nucleation in complex, generic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Role of Chemical Disorder in High Temperature Dislocation Glide in Refractory Multi-principal Element Alloys

Refractory multi-principal element alloys (RMPEAs) combine a chemically disordered lattice with a structurally ordered, single‐phase body‐centered cubic (bcc) crystal structure. Chemical fluctuations in these alloys give rise to significant energy barriers that impede dislocation motion. In this study, we use phase field dislocation dynamics to examine how spatial temperature fluctuations compete with randomness in energy barriers to affect the motion of long screw dislocations in three equi-atomic MoNbTa‐based RMPEAs: MoNbTa, MoNbTaW, and MoNbTaVW. Over a wide range of homologous temperatures (T h ≈ 0–0.6), we determined a screw dislocation ‘flow stress’ as the minimum applied stress to sustain continuous motion over a long excursion distance within a fixed timeframe. All three RMPEAs exhibit temperature dependent flow stress with three characteristic glide regimes: at low homologous temperatures, flow stress drops sharply, and screw glide remains planar and rectilinear; at intermediate homologous temperatures, the flow stress levels off as glide becomes planar but wavy; and at high homologous temperatures, flow stress plateaus as screws exhibit nonplanar, three‐dimensional motion. Glide kinetics and transition temperatures are controlled by the chemically induced fluctuations in the energy landscape. The rectilinear to wavy transition temperature is controlled by statistically weakest local barriers in the glide plane, whereas the wavy to 3D transition temperature is governed by statistically strongest local barriers. At low and intermediate homologous temperatures, the relative spread in energy barriers governs glide behavior by controlling the local kinetics of kink pair formation and kink pinning. At high homologous temperatures, the average barrier height governs the glide behavior by controlling the number of out-of-plane excursions during 3D glide. These findings reveal how random chemical fluctuations determine screw‐driven plasticity in RMPEAs, providing critical insight for the design of high temperature structural alloys.

Defects↗
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 OSTI2023

A comparative study of cerium oxides formed by pulsed laser ablation and continuous wave laser heating

Abstract The formation of Ce 2 O 3 from cerium metal requires complex reducing conditions. Alternatively, we show that different stoichiometries and structures of cerium oxides can be generated in high‐temperature laser experiments by varying the availability of oxygen and the use of either pulsed laser ablation (PLA) or continuous wave (CW) laser heating of cerium metal. X‐ray diffraction and Raman spectroscopy suggest that a low structural order Ce 2 O 3 /CeO 2 mixed structure is formed in a 1% O 2 atmosphere with PLA. For CW heating in a 1% O 2 environment, Raman measurements show the formation of crystalline A‐type Ce 2 O 3 in proximity to the point of laser impingement. These experiments were repeated in 18 O 2 and allowed us to report for the first time the corresponding Raman spectrum of Ce 2 18 O 3 .

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

Synthesis of core/shell nanocrystals with ordered intermetallic single-atom alloy layers for nitrate electroreduction to ammonia

Structurally ordered intermetallic nanocrystals (NCs) and single-atom catalysts (SACs) are two emerging catalytic motifs for sustainable chemical production and energy conversion. However, both have synthetic limitations which can lead to the aggregation of NCs or metal atoms. Single-atom alloys (SAAs), which contain isolated metal atoms in a host metal, can overcome the aggregation concern because of the thermodynamic stabilization of single atoms on host metal surfaces. We report a direct solution-phase synthesis of Cu/CuAu core/shell NCs with tunable SAA layers. This synthesis can be extended to other Cu/CuM (M = Pt, Pd) systems, in which M atoms are isolated in the copper host. Using this method, the density of SAAs on a copper surface can be controlled, resulting in both low and high densities of single atoms. Alloying gold into the copper matrix introduced ligand effects that optimized the chemisorption of *NO 3 and *N. As a result, the densely packed Cu/CuAu material demonstrated a high selectivity toward NH 3 from the electrocatalytic nitrate reduction reaction with an 85.5% Faradaic efficiency while maintaining a high yield rate of 8.47 mol h -1 g -1 . This work advances the design of atomically precise catalytic sites by creating core/shell NCs with SAA atomic layers, opening an avenue for broad catalytic applications.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Controlling flexible structures with second order actuator dynamics

The control of flexible structures for those systems with actuators that are modeled by second order dynamics is examined. Two modeling approaches are investigated. First a stability and performance analysis is performed using a low order finite dimensional model of the structure. Secondly, a continuum model of the flexible structure to be controlled, coupled with lumped parameter second order dynamic models of the actuators performing the control is used. This model is appropriate in the modeling of the control of a flexible panel by proof-mass actuators as well as other beam, plate and shell like structural numbers. The model is verified with experimental measurements.

Inman, Daniel J.↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reconfigurable Grating Diffraction Structural Color in Self-Assembled Colloidal Crystals

Self-assembled colloidal crystals display structural colors due to light diffracted from their microscale, ordered structure. This color arises due to Bragg reflection (BR) or grating diffraction (GD); the latter mechanism is much less explored than the former. Here the design space for generating GD structural color is identified and its relative advantages are demonstrated. Electrophoretic deposition is used to self-assemble crystals with fine crystal grains from colloids of diameter 1.0 µm. The structural color in transmission is tunable across the full visible spectrum. The optimum optical response—represented by both color intensity and saturation—is observed at low layer number (≤5 layers). The spectral response is well predicted by Mie scattering of the crystals. Taken together, the experimental and theoretical results demonstrate that vivid grating colors with high color saturation can be produced from thin layers of micron-sized colloids. These colloidal crystals extend the potential of artificial structural color materials.

42 ENGINEERING↗
DOE OSTI2022

Revisiting Lebedev’s one-century old experiment

One hundred years ago, world-famous scientist A. A. Lebedev performed a set of classical measurements on annealed optic crown glasses. He found that these glasses exhibited characteristic endothermic effects in a particular temperature range. To explain these phenomena, Lebedev proposed a hypothesis that the glasses contain tiny quartz crystals. This initial hypothesis was quickly disapproved, and the origin of the endothermic effect of glasses remains an unsolved puzzle. This work uses recently proposed nanoflake model of silica glass structure to explain the endothermic effect of various glasses. The new model differs from the popular continuous random network theory in that it emphasizes the medium-range ordering structure of glasses. According to the nanoflake based theory, the endothermic effect of glasses is caused by the transition from ordered one-dimensional structures into disordered structure in glasses. The new theory also predicts that the temperature range of the endothermic effect is dependent on both glass composition and cooling rates during glass formation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamics of dust particles confined in imposed potential structures in strongly magnetized, low-temperature plasmas

A new phenomenon called imposed ordered structures has been of particular interest to the dusty plasma community within the past several years. These structures are a new type of pattern formation within a dusty plasma in which the dust particles become fixed to a background confining potential whose spatial structure is determined by some conducting element present in the plasma. In previous works, this element has typically been a conducting wire mesh. One of the unanswered questions is whether the dust particles become trapped beneath the conducting surface of the mesh or in the gaps (holes) between the wires. This work makes use of a new electrode whose shape is designed to mimic that of wire mesh, but with much larger dimensions to facilitate in situ diagnostic measurements. Observations of the dust show that particles become confined to the regions beneath the holes of this new electrode. Measurements of the dust particle velocities allow for a determination of the kinetic energy within the dust cloud which show that the particles are in an energetic steady state. Further, comparisons of spatial profiles of the particle velocities also show that the dust particles typically reside in areas of increased plasma glow, possibly being trapped by plasma filaments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Electron energy loss spectra from silica glass optical fibers

To investigate the possible structural differences between silica glass fibers and bulk silica glasses, electron energy loss spectroscopy (EELS) has been used to study the short-range and medium-range structures of both forms of silica glasses. The short-range structure of silica glass, such as the coordination and symmetry, was investigated by the energy loss near edge structure (ELNES) of Si L 2,3 -edges. Furthermore, the ordering structure in the medium-range was analyzed by the exponential optical absorption edge also known as the Urbach edge of the glasses. The optical absorption data were obtained from the low energy loss spectrum of EELS through Kramers-Kronig analysis. The results show that silica fiber has the same short-range structure as the bulk specimen, but is significantly more disordered than the bulk glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Creating Favorable Pt/Co Interfaces via a Two‐Step Approach for Constructing Highly Durable PtCo Intermetallic Fuel Cell Catalysts

Structurally ordered PtCo intermetallics are one of the most promising oxygen-reduction catalysts in proton exchange membrane fuel cells (PEMFCs) due to their intrinsically improved catalytic activity and stability relative to PtCo solid-solution alloys. However, increasing the heating temperature to achieve a desirable high degree of ordering results in severe particle agglomeration and low mass activity and stability. Herein, a two-step synthesis approach is developed to create an L1 2 -Pt 3 Co intermetallic structure with an increased ordering degree and well-dispersed ultrafine particles. The first step of the synthesis yields ultrafine Pt nanoparticles that are well-dispersed on the ZIF-8-derived carbon support. The second adsorption step enables us to fine-tune the Pt and Co interfaces, assisted by optimal amino acids, to establish a favorable Co-rich environment around fine Pt nanoparticles, facilitating Co diffusion into the Pt crystalline under mild thermal conditions (<800 °C). In conclusion, this two-step ordered L1 2 -Pt 3 Co catalyst is systematically evaluated using membrane electrode assemblies under heavy-duty vehicle (HDV) conditions and demonstrated exceptional performance and durability, retaining 1.35 A cm -2 only a 7% loss in current density at 0.7 V after an extensive accelerated stress test of 150,000 voltage cycles.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1975

Applications of a quadratic extended interior penalty function for structural optimization

A quadratic extended interior penalty function formulation especially well suited for second-order unconstrained optimization procedures is presented. Analytical derivatives of constraints and an approximate analysis technique are used. Minimum-mass design results are presented which indicate that the combination of these procedures can help make mathematical programming a useful optimization tool for large-order structural design problems with a large number of design variables and multiple constraints. Examples include statically loaded high- and low-aspect-ratio wings simultaneously subjected to stress, displacement, minimum gage and, in some cases, maximum strain constraints.

Haftka, R. T.↗
NASA NTRS2004

A Robust and Engineerable Self-Assembling Protein Template for the Synthesis and Patterning of Ordered Nanoparticle Arrays

Self-assembling biomolecules that form highly ordered structures have attracted interest as potential alternatives to conventional lithographic processes for patterning materials. Here we introduce a general technique for patterning materials on the nanoscale using genetically modified protein cage structures called chaperonins that self-assemble into crystalline templates. Constrained chemical synthesis of transition metal nanoparticles is specific to templates genetically functionalized with poly-Histidine sequences. These arrays of materials are ordered by the nanoscale structure of the crystallized protein. This system may be easily adapted to pattern a variety of materials given the rapidly growing list of peptide sequences selected by screening for specificity for inorganic materials.

McMillan, R. Andrew↗