Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Structural Properties”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Venturing into Unexplored Phase Space: Synthesis, Structure, and Properties of MgCo 3 B 2 Featuring a Rumpled Kagomé Network

MgCo 3 B 2 , a novel ternary boride in a previously unexplored phase space, was synthesized using the hydride route. In situ powder X-ray diffraction and DFT calculations aided in the discovery of this compound, whose structure was then determined by single-crystal X-ray diffraction. Like the closely related CeCo 3 B 2 , MgCo 3 B 2 crystallizes in centrosymmetric space group P6/mmm (a = 4.883(2) Å, c = 2.926(2) Å at 210 K, Z = 1). Unlike CeCo 3 B 2 , however, it adopts a disordered structure that features a rumpled Kagomé network of Co atoms, and Mg atoms fill the channels of a Co–B framework. Although the structural disorder leads to motifs that are similar to those observed in MgNi 3 B 2 and other related ternary borides, no evidence of an ordered superstructure was found by single-crystal X-ray diffraction or high-resolution powder X-ray diffraction. In the case of CeCo 3 B 2 , boron atoms occupy the center of regular Co 6 trigonal prisms; in MgCo 3 B 2 , boron atoms are shifted from the center of the prism to form B–B dimers with roughly the same length as those found in MgNi 3 B 2 . Magnetic susceptibility data exhibit an unusual temperature dependence that cannot be convincingly modeled by the modified Curie–Weiss equation, consistent with DFT calculations predicting a nonmagnetic ground state. Intrinsic susceptibility at 300 K is 1.42 × 10 –3 emu/mol Oe, which is comparable to that of paramagnetic YCo 3 B 2 and CeCo 3 B 2 with a similar structure and composition. Here, this study showcases the efficacy of combining several methodologies to discover new solids in unexplored phase spaces. This approach includes in situ PXRD data to monitor reactions of precursors upon heating, a diffusion-enhanced synthesis method, and DFT assessment of compound stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ramen

Many computationally unintensive models for material process-structure-property connections exist in the literature (e.g. journal articles and textbooks). However, to our knowledge there is no existing compilation of these models in one package. Many industrially and research relevant workflows involve combinations of many of these models and if the models have inconsistent interfaces, building these workflows can be challenging. Ramen provides a collection of computationally unintensive models for calculating material process-structure-property relationships from the literature, as well as related tools (e.g. for plotting results). These models use a consistent interface structure and all leverage material data files and data structures from the Mist library (also developed at ORNL). Ramen provides a structured package for combining many standard models in one place, with a standardized interface. This approach makes combining models in long/complex chains easy and straightforward.

DeWitt, Stephen [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

Hydrogen Materials Advanced Research Consortium (HyMARC): Sandia Technical Effort

A trilateral agreement has been finalized involving research institutions in Korea, Japan, and the U.S. The project partners are Sandia, LLNL, KIST, KAIST, and AIST. The project title is “Structure-Property Relationships in Metal Alloys for Hydrogen Storage and Processing.” Funding for the U.S. portion of the effort is through NNSA; the PI is Vitalie Stavila. The overall objective of this project is to identify detailed structure-property relationships governing hydrogen separation, purification, storage, and compression in compositionally complex metal alloys.

08 HYDROGEN↗
DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

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

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗
DOE OSTI2021

Crystal structure, magnetic properties and bonding analysis of M 3 Pt 23 Ge 11 (M=Ca, Sr, Ba and Eu)

The properties of Pt-based materials can be intriguing due to the importance of spin-orbit coupling for Pt. Herein, we report four new phases with formulas M 3 Pt 23 Ge 11 (M ​= ​Ca, Sr, Ba and Eu), which adopt the same structure type as Ce3Pt23Si11. Magnetic susceptibility measurements indicate that none of the phases is superconducting above 1.8 ​K, while for Eu 3 Pt 23 Ge 11 ferromagnetic ordering is observed at ~ 3 ​K. The low Curie temperature for that material compared to that of Eu 3 Pt 23 Si 11 may be due to its larger Eu–Eu distance. One potential factor that destabilizes the structure of other rare-earth based M 3 Pt 23 Ge 11 is demonstrated through COHP calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Molecular Rotations, Multiscale Order, Hyperuniformity, and Signatures of Metastability during the Compression/Decompression Cycles of Amorphous Ices

We model, via large-scale molecular dynamics simulations, the isothermal compression of low-density amorphous ice (LDA) to generate high-density amorphous ice (HDA) and the corresponding decompression extending to negative pressures to recover the low-density amorphous phase (LDA HDA ). Both LDA and HDA are nearly hyperuniform and are characterized by a dynamical HBN, showing that amorphous ices are nonstatic materials and implying that nearly hyperuniformity can be accommodated in dynamical networks. In correspondence with both the LDA-to-HDA and the HDA-to-LDA HDA phase transitions, the (partial) activation of rotational degrees of freedom activates a cascade effect that induces a drastic change in the connectivity and a pervasive reorganization of the HBN topology which, ultimately, break the samples’ hyperuniform character. Key to this effect is the rapid rate at which changes occur, and not their magnitude. The inspection of structural properties from the short- to the long-range shows that signatures of metastability are present at all length-scales, hence providing further solid evidence in support of the liquid–liquid critical point scenario. LDA and LDA HDA differ in terms of HBN and structural properties, implying that they are distinct low-density glasses. Our work unveils the role of molecular rotations in the phase transitions between amorphous ices and shows how the unfreezing of rotational degrees of freedom generates a cascade effect that propagates over multiple length-scales. Our findings greatly improve our basic understanding of water and amorphous ices and can potentially impact the field of molecular network-forming materials at large.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Temperature Air Stability of Electrodeposited Copper Cobalt Oxide and Copper Manganese Oxide Absorber Coatings for Concentrating Solar Power

The solar absorptance of a thermal receiver surface significantly affects the photothermal conversion efficiency of concentrating solar power (CSP) plants. The stability of solar receivers and coatings applied to improve solar absorptance at high temperatures is critical for efficient and long-term operation. This study analyzes the high-temperature air stability of multiscale fractal textured electrodeposited copper manganese oxide and copper cobalt oxide solar receiver coatings over a range of temperatures. The study also investigates the effect of high temperature on phase stability, chemical and morphological evolution, and the optical properties of the two solar absorber materials. A computational model is developed to determine the absorptance of the textured absorbers and is shown to match the experimental measurements closely over a range of heat treatment temperatures from 450 °C to 850 °C and durations up to 100 h. Degradation curves and coating stability maps are developed from the study. Copper manganese oxide and copper cobalt oxide coatings exhibit good thermal stability up to ~815 °C and 630 °C, respectively, with minimal changes in their optical and structural properties with thermal efficiency greater than 90%. Furthermore, the study offers valuable insights into the high-temperature annealing effects on the optical and structural properties of multiscale copper alloyed coatings fabricated by electrodeposition and presents a means of designing the optimal temperature range for maximizing absorber efficiency in practical applications.

14 SOLAR ENERGY↗
DOE OSTI2021

Synthesis, Magnetic Properties, and Electronic Structure of Magnetic Topological Insulator MnBi 2 Se 4

The intrinsic magnetic topological insulators MnBi 2 Te 4 and MnBi 2 Se 4 support novel topological states related to symmetry breaking by magnetic order. Unlike MnBi 2 Te 4 , the study of MnBi 2 Se 4 has been inhibited by the lack of bulk crystals, as the van der Waals (vdW) crystal is not the thermodynamic equilibrium phase. In this study, we report the layer-by-layer synthesis of vdW MnBi 2 Se 4 crystals using nonequilibrium molecular beam epitaxy. Atomic-resolution scanning transmission electron microscopy and scanning tunneling microscopy identify a well-ordered vdW crystal with septuple-layer base units. The magnetic properties agree with the predicted layered antiferromagnetic ordering but disagree with its predicted out-of-plane orientation. Instead, our samples exhibit an easy-plane anisotropy, which is explained by including dipole–dipole interactions. Angle-resolved photoemission spectroscopy reveals the gapless Dirac-like surface state, which demonstrates that MnBi 2 Se 4 is a topological insulator above the magnetic-ordering temperature. These studies show that MnBi 2 Se 4 is a promising candidate for exploring rich topological phases of layered antiferromagnetic topological insulators.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Factors Influencing Preferential Anion Interactions during Solvation of Multivalent Cations in Ethereal Solvents

Most multivalent secondary batteries have employed electrolytes composed of cyclic ether solvents such as tetrahydrofuran or linear glycol ether solvents (glymes) such as 1,2-dimethoxyethane (G1). A robust understanding of multivalent cation solvation tendencies in these classes of solvents provides insight into corresponding structure–property relationships which, in turn, promotes the design and discovery of improved electrolytes. In this work, our goal is to systematically address how electrolyte constituent properties, namely, ether solvent structure and dication size, direct the solvation interactions of divalent electrolytes and their resultant properties. This study utilizes pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy in conjunction with Raman spectroscopy and ionic conductivity measurements to elucidate the preferential interactions between multivalent cations, anions, and solvent molecules along with their correlated ion dynamics. These investigations incorporate two representative divalent cations (Ca 2+ and Zn 2+ ) as well as two ethereal solvent representatives from both the cyclic ether and glyme structural classes. The results reveal that anions coordinate more readily with divalent cations in cyclic ethers than in glymes. Furthermore, the coordination of the anions with Ca 2+ , i.e., contact-ion pair (CIP) formation is more pronounced than with Zn 2+ in a glyme solvent of limited chain length (G1), providing insight into cation size effects that are important for translating solvation behavior across various multivalent electrolytes. Importantly, we find that specific anion coordination is more strongly controlled by solvent structure than by salt concentration in the practical range of 0.1–0.5 M. However, simply reducing these inner-sphere inter-ionic interactions by changing solvent structure does not necessarily de-correlate ionic motion. Instead, concentration-dependent changes in molar ionic conductivity suggest that second-shell interactions, i.e., solvent separated ion pairs (SSIPs), are prevalent in these electrolytes and that the solution dielectric constant, which is increased by the presence of dipolar ion pairs, is critical for controlling these interactions. These findings thus provide a basis for understanding the physical chemistry of multivalent battery electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Synthesis, Structure, and Properties of Volatile Lanthanide Dialkyltriazenides

Several dialkyltriazenide complexes of the lanthanide elements neodymium, europium, and erbium have been prepared; these include the homoleptic complex Er(Bu t N 3 Bu t ) 3 , the tetrahydrofuran monoadducts Ln(Bu t N 3 Bu t ) 3 (THF) where Ln = Nd or Eu, and the lithium salts [Li(THF)][Ln(MeN 3 Bu t ) 4 ] where Ln = Eu or Er. Crystal structures, nuclear magnetic resonance data, and infrared data are reported for all complexes. Further, the di-tert-butyltriazenide complexes are thermally stable, sublime at reasonably low temperatures, and show smooth volatilization without decomposition, which make them potentially useful in lanthanide separation processes and as chemical vapor deposition precursors for lanthanide nitrides and other phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Regulating Exciton–Phonon Coupling to Achieve a Near‐Unity Photoluminescence Quantum Yield in One‐Dimensional Hybrid Metal Halides

Abstract Low‐dimensional hybrid metal halides are emerging as a highly promising class of single‐component white‐emitting materials for their unique broadband emission from self‐trapped excitons (STEs). Despite substantial progress in the development of these metal halides, many challenges remain to be addressed to obtain a better fundamental understanding of the structure–property relationship and realize the full potentials of this class of materials. Here, via pressure regulation, a near 100% photoluminescence quantum yield (PLQY) of broadband emission is achieved in a corrugated 1D hybrid metal halide C 5 N 2 H 16 Pb 2 Br 6 , which possesses a highly distorted structure with an initial PLQY of 10%. Compression reduces the overlap between STE states and ground state, leading to a suppressed phonon‐assisted non‐radiative decay. The PL evolution is systematically demonstrated to be controlled by the pressure‐regulated exciton–phonon coupling which can be quantified using Huang–Rhys factor S . Detailed studies of the S ‐PLQY relation for a series of 1D hybrid metal halides (C 5 N 2 H 16 Pb 2 Br 6 , C 4 N 2 H 14 PbBr 4 , C 6 N 2 H 16 PbBr 4 , and (C 6 N 2 H 16 ) 3 Pb 2 Br 10 ) reveal a quantitative structure–property relationship that regulating S factor toward 28 leads to the maximum emission.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Detecting Thermally-Induced Spinodal Decomposition with Picosecond Ultrasonics in Cast Austenitic Stainless Steels

Destructive techniques to monitor nuclear reactor component health may not always be available during service, as they are time-consuming and often require pre-installed inspection coupons. Non-destructive evaluation (NDE) techniques can bridge this gap by rapidly identifying the state of mission-critical reactor components, via inference between NDE-measurable material properties and those of ultimate interest, such as ductility and toughness. Here, we demonstrate one such inference about the health of thermally aged cast austenitic stainless steels. Observations of surface acoustic wave peak (SAW) splitting correlate with spinodal decomposition-induced embrittlement as destructively measured by Charpy impact energy. Elastodynamic calculations and molecular dynamics simulations of the effects of spinodal decomposition on elastic moduli support that the new acoustic modes present are due to stiffening in the δ-ferrite domains. Finally, this discovery enables one to probe structure-property relationships in materials in a greatly accelerated manner, suggesting that similar inference methods can be used to determine material fitness-for-service, or to quickly uncover new structure-property relationships.

304-type stainless steel↗
DOE OSTI2023

Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization

Discovery of structure-property relationships in thin film alloys of complex metal oxides enabled by high-throughput materials synthesis and characterization facilities is demonstrated here with a case-study. In this study, thin films of binary transition metal oxides (Ti–Zn) are prepared by pulsed laser deposition with continuously varying Ti:Zn ratio, creating combinatorial samples for exploration of the properties of this material family. The atomic structure and electronic properties are probed by spatially resolved techniques including x-ray absorption near edge structures (XANES) and x-ray fluorescence (XRF) at the Ti and Zn K-edge, x-ray diffraction, and spectroscopic ellipsometry. The observed properties as a function of Ti:Zn ratio are resolved into mixtures of five distinguishable phases by deploying multivariate curve resolution analysis on the XANES spectral series, under constraints set by results from the other characterization techniques. First-principles computations based on density function theory connect the observed properties of each distinct phase with structural and spectral characteristics of crystalline polymorphs of Ti–Zn oxide. Continuous tuning of the optical absorption edge as a function of Ti:Zn ratio, including the unusual observation of negative optical bowing, exemplifies a functional property of the film correlated to the phase evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗