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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systems, devices, and methods for contact measurement and modulation of material properties

Materials and devices are provided for the sensing and manipulation of biomechanical and physiochemical properties of tissues or tissue surfaces. Examples of use include soft tissues such as skin or adipose tissues or more dense tissues such as muscle or heart or dense tissues such as bone. The materials and devices provide for in vivo measurements of biomechanical properties at the tissue surface, e.g. near surface regions of the epidermis or dermis or underlying structures. The devices can be non-invasive and/or non-destructive to the material and, especially for the biomaterials, can be biocompatible and/or biodegradable. The materials and devices can use ultrathin, stretchable networks of mechanical actuators and sensors constructed with nanoribbons of piezoelectric materials.

Dagdeviren, Canan↗
DOE OSTI2024

Spacecraft surface charging as a function of material properties

Spacecraft material behavior plays a very important role in space missions. Spacecraft immersed in plasma get charged by absorbing plasma particles and by emitting electrons from spacecraft surfaces via photoelectron and secondary electron emission. Spacecraft charging depends heavily on material properties such as work function, secondary electron yield, dielectric constant, and electric conductivity among other. Material properties are typically assumed to be static in charging models. However, it is well known that this is not the case in space. This makes spacecraft charging predictions very challenging. Material properties are well characterized before the spacecraft is put in orbit through characterization in the lab under clean conditions. However, once in space, material properties change due to the harsh and very dynamic space environment. We present a new capability to predict material behavior in space from first-principles modeling. The ongoing effort seeks to couple material models, density functional theory (DFT) and molecular dynamic (MD) codes, with environment models, plasma kinetic codes. This preliminary study will show results of surface charging as a function of material work function, dielectric constant, and conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

NSUF Rad-AFM: Nanoscale Material Property Measurements of Radioactive Materials

Many advances in our understanding of radiation induced damage mechanisms are due to imaging techniques including transmission electron microscopy and scanning electron microscopy. However, the effect of these radiation induced defects characterized on the atomic to micrometer scale are often interpreted by changes in bulk material properties measured in the hundreds of microns to cm scale. In order to bridge this gap, additional tools need to be developed for use with radioactive materials. To this end a new Asylum Infinity multimodal atomic force microscope (AFM) has been added to the library of Nuclear Science User Facility (NSUF) instruments for use on radioactive materials. This instrument is housed in the Radiochemical Processing Laboratory (RPL) which is a Department of Energy Hazard Category II Non-Reactor Nuclear Facility operated by Battelle at Pacific Northwest National Laboratory. This allows for the handling, sample preparation, and characterization of highly radioactive materials providing a unique capability to understand fundamental properties of nuclear materials and the effects of radiation induced damage.

Riechers, Shawn L.↗
DOE OSTI2016

Material properties of perovskites in the quasi-ternary system LaFeO{sub 3}–LaCoO{sub 3}–LaNiO{sub 3}

An overview is presented on the variation of electrical conductivity, oxygen permeation, and thermal expansion coefficient as a function of the composition of perovskites in the quasi-ternary system LaFeO{sub 3}–LaCoO{sub 3}–LaNiO{sub 3}. Powders of thirteen nominal perovskite compositions were synthesized under identical conditions by the Pechini method. The powder X-ray diffraction data of two series, namely La(Ni{sub 0.5}Fe{sub 0.5}){sub 1−x}Co{sub x}O{sub 3} and LaNi{sub 0.5-x}Fe{sub x}Co{sub 0.5}O{sub 3}, are presented after the powders had been sintered at 1100 °C for 6 h in air. The measurements revealed a rhombohedral structure for all compositions except LaNi{sub 0.5}Fe{sub 0.5}O{sub 3} for which 60% rhombohedral and 40% orthorhombic phase was found. The maximum DC electrical conductivity value of the perovskites at 800 °C was 1229 S cm{sup −1} for the composition LaCoO{sub 3} and the minimum was 91 S cm{sup −1} for the composition LaCo{sub 0.5}Fe{sub 0.5}O{sub 3}. The oxygen permeation of samples with promising conductivities at 800 °C was one order of magnitude lower than that of La{sub 0.6}Sr{sub 0.4}Co{sub 0.8}Fe{sub 0.2}O{sub 3} (LSCF). The highest value of 0.017 ml cm{sup −2} min{sup −1} at 950 °C was obtained with LaNi{sub 0.5}Co{sub 0.5}O{sub 3}. The coefficients of thermal expansion varied in the range of 13.2×10{sup −6} K{sup −1} and 21.9×10{sup −6} K{sup −1} for LaNi{sub 0.5}Fe{sub 0.5}O{sub 3} and LaCoO{sub 3}, respectively. {sup 57}Fe Mössbauer spectroscopy was used as probe for the oxidation states, local environment and magnetic properties of iron ions as a function of chemical composition. The substitution had a great influence on the chemical properties of the materials. - Graphical abstract: Compositional dependence of isothermal electrical conductivity at 800 °C and thermal expansion coefficient in the series La(Fe{sub 0.5}Ni{sub 0.5}){sub (1−x)}Co{sub x}O{sub 3} and LaNi{sub 0.5−x}Fe{sub x}Co{sub 0.5}O{sub 3}.

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

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.

Eisenbach, Markus↗
DOE OSTI2019

A Brief Note About Scaling Material Properties

Often tables of material properties are computed for a specific chemical or isotopic composition. When we use those pre-computed tables to model real experiments, there can be differences between the as-built material compositions and the pre-computed tables. This document describes several ways that we can scale these tables to account for this. We typically scale every lookup we make into the table in an attempt to account for dynamic changes in material composition over the coarse of the calculation. Even if the compositions are not changing dynamically in the calculation, these same scalings can be used to adjust the material properties from a near by composition to the as-built composition.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems

Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Common workflows for computing material properties using different quantum engines

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

AGC 3 Irradiated Material Properties Analysis

This report documents the analysis of the irradiated material property data from the Advanced Graphite Creep (AGC)-3 graphite specimens. This is the third in a series of six irradiation test trains planned as part of the AGC experiment to fully characterize the neutron irradiation effects and radiation creep behavior of current nuclear graphite grades. The AGC-3 capsule was irradiated in the Idaho National Laboratory Advanced Test Reactor at a nominal temperature of 800°C, beginning with irradiation Cycle 152B on November 28, 2012 and ending with Cycle 155B on April 12, 2014, with a total received dose range of 0.9–3.7 dpa. Larger creep and control specimens located more centrally in the capsule received a dose of 1.0 – 3.7 dpa. AGC-3 was designed to provide irradiation conditions similar to AGC-1 and AGC-2 capsules (similar graphite grades tested, specimen dimensions, mechanical loading conditions) but at a different nominal irradiation temperature of 800°C. AGC-3 was irradiated for a short duration to provide material property values at lower dose levels. AGC-4 will have a longer duration and provide material property values at higher dose levels. After irradiation, material property and dimensional strain measurements were conducted on all AGC-3 specimens (from 11 nuclear graphite grades) using the same equipment and approved standards as were conducted before irradiation. The specimen loading configuration for all graphite grades within AGC-3 followed a similar pattern as earlier AGC capsules to provide easy future comparison of all irradiated material property data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Deciphering chemical order/disorder and material properties at the single-atom level

Perfect crystals are rare in nature. Real materials often contain crystal defects and chemical order/disorder such as grain boundaries, dislocations, interfaces, surface reconstructions and point defects. Such disruption in periodicity strongly affects material properties and functionality. Despite rapid development of quantitative material characterization methods, correlating three-dimensional (3D) atomic arrangements of chemical order/disorder and crystal defects with material properties remains a challenge. On a parallel front, quantum mechanics calculations such as density functional theory (DFT) have progressed from the modelling of ideal bulk systems to modelling ‘real’ materials with dopants, dislocations, grain boundaries and interfaces; but these calculations rely heavily on average atomic models extracted from crystallography. To improve the predictive power of first-principles calculations, there is a pressing need to use atomic coordinates of real systems beyond average crystallographic measurements. Here we determine the 3D coordinates of 6,569 iron and 16,627 platinum atoms in an iron-platinum nanoparticle, and correlate chemical order/disorder and crystal defects with material properties at the single-atom level. We identify rich structural variety with unprecedented 3D detail including atomic composition, grain boundaries, anti-phase boundaries, anti-site point defects and swap defects. We show that the experimentally measured coordinates and chemical species with 22 picometre precision can be used as direct input for DFT calculations of material properties such as atomic spin and orbital magnetic moments and local magnetocrystalline anisotropy. The work presented here combines 3D atomic structure determination of crystal defects with DFT calculations, which is expected to advance our understanding of structure–property relationships at the fundamental level.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Synthesis and Engineering Materials Properties of Fluid Phase Chemical Hydrogen Storage Materials for Automotive Applications

Among candidates for chemical hydrogen storage in PEM fuel cell automotive applications, ammonia borane (AB, NH3BH3) is considered to be one of the most promising materials due to its high practical hydrogen content of 14-16 wt%. This material is selected as a surrogate chemical for a hydrogen storage system. For easier transition to the existing infrastructure, a fluid phase hydrogen storage material is very attractive and thus, we investigated the engineering materials properties of AB in liquid carriers for a chemical hydrogen storage slurry system. Slurries composed of AB and high temperature liquids were prepared by mechanical milling and sonication in order to obtain stable and fluidic properties. Volumetric gas burette system was adopted to observe the kinetics of the H2 release reactions of the AB slurry and neat AB. Viscometry and microscopy were employed to further characterize slurries engineering properties. Using a tip-sonication method we have produced AB/silicone fluid slurries at solid loadings up to 40wt% (6.5wt% H2) with viscosities less than 500cP at 25°C.

ammonia borane↗
DOE OSTI2022

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Assessment of Material Properties for TRISO Fuel Particles in PARFUME

The purpose of this study is to identify the material properties that have the largest impact on the failure probability of tristructural isotropic (TRISO)-coated fuel particles under irradiation. For the most part, these material properties were obtained from experimental data of historical TRISO fuel development programs in Europe and the United States. The adequacy of these historical constitutive material properties is being evaluated as part of the Advanced Gas Reactor Fuel Development and Qualification Program. The TRISO fuel performance modeling code PARFUME was used to assess the material properties that have the main influence on the probability of failure of the silicon carbide (SiC) layer of TRISO fuel particles. Although some of these material properties exhibit a large variability according to published data, only a few of them have a significant impact on the SiC failure probability, namely the irradiation-induced creep and dimensional change of the inner pyrolytic carbon layer.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗