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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optical properties of semiconductor nanoparticles doped with 3d and 4f block elements

Doped inorganic nanoparticles (NPs) have enabled researchers across multiple disciplines to create new materials with unique optical, electronic, magnetic, electrical, chemical, and/or compositional properties. In the quest for generating newer functional materials with controlled optical and electronic properties, the use of d- and f-block elements as dopants in the inorganic NPs remain at the forefront of research. This article summarizes experiments that identify the important control parameters, in the form of NP core and surface properties, for promoting host-sensitized dopant emission from d- and f-doped inorganic/semiconductor NPs. We conclude that energy offsets between the dopant and NP electronic levels and an understanding of the charge trapping mechanisms can be used to predict dopant luminescence, and we claim that these features can be used to choose NP host materials by design, rather than by trial and error.

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

Engineering metal–metal oxide surfaces for high-performance oxygen reduction on Ag–Mn electrocatalysts

Understanding fundamental material–property relationships in mixed-element catalyst systems is crucial to advancing the viability of renewable electrochemical energy technologies, an important part of creating a more sustainable future. Herein, we report our insight on the nature and dynamics of highly active silver–manganese oxide (Ag–MnOx) catalyst surfaces for the oxygen reduction reaction (ORR) via a combined experimental–theoretical approach. Experimentally, we synthesize well-mixed Ag–Mn co-deposited thin films that are measurably flat and smooth, despite Mn surface migration and oxidation upon air exposure and electrochemical measurements. Cyclic voltammetry in 0.1 M KOH demonstrates up to 10-fold specific activity enhancements over pure Ag at 0.8 V vs. RHE for Ag-rich films (70–95% Ag in bulk). To further understand the Ag–Mn system, separate samples were synthesized with small amounts of Mn sequentially deposited onto the surface of a pure Ag thin film (Mn@Ag), ranging from partial to full surface coverage (down to 0.3 nm Mn $cm^{-2}_{geo}$ ~ 0.2 μg Mn $cm^{-2}_{geo}$). These sequentially deposited Mn@Ag films show analogous performance to their co-deposited counterparts indicating similar enhanced active sites. With density functional theory (DFT), we calculate that this enhancement arises from the tuned d-band of these material surfaces owing to the optimal hybridization of the electronic structures in specific Ag and MnO x geometries. Together, electrochemical measurements, DFT calculations, X-ray absorption spectroscopy, and valence-band X-ray photoelectron spectroscopy suggest synergistic electronic interactions between Ag and MnOx yield enhanced oxygen adsorption, and thus ORR activity, with DFT highlighting the Ag–MnO x interface sites as the most enhanced. This work demonstrates how combined experimental–theoretical methods can help design electrocatalysts with enhanced electrocatalytic properties and understand the nature of complex mixed metal–metal oxide surfaces.

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

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural changes upon magnetic ordering in magnetocaloric AlFe 2 B 2

With a Curie temperature just above room temperature, AlFe 2 B 2 is a useful magnetocaloric material composed of earth-abundant elements. Here, we employ temperature-dependent high-resolution synchrotron X-ray diffraction to establish with high certainty that the paramagnetic to ferromagnetic transition in AlFe 2 B 2 is of second order, showing no discontinuity in lattice parameters or cell volume. Nevertheless, the lattice parameters undergo anisotropic changes across the transition with distinct differences in the thermal expansion coefficients. While the $a$ and $b$ lattice parameters show a positive thermal expansion, $c$ shows a negative thermal expansion. We link these changes to the respective interatomic distances to determine the contribution of magnetism to the anisotropic structural evolution. The work underpins the possible role of magnetostructural coupling in driving the magnetocaloric effect in AlFe 2 B 2 .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Affine Transformations to Enable Machine Learning for Semi-Quantitative EDS Analysis

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

The Impact of Europium as a Fission Product Surrogate on Chromium Corrosion in Molten Chloride Salt

The rising interest in molten salts, both as thermal energy storage media and as high-temperature solvents for fissile materials in advanced nuclear reactors, has spurred significant growth in research on structural and functional salt-facing materials. Impurities, such as water, play a significant role in the degradation of materials in molten salt environments. Other impurities, such as fission products, are expected to accumulate in molten salts during normal reactor operation and can affect the properties of the salt and salt-facing materials. During normal reactor operation, rare-earth element species are expected to form as byproducts of fission reactions, potentially degrading or altering the performance of both structural and functional materials exposed to salts. Here, in this work, fission product surrogates, europium metal and EuCl 3 , were added to the KCl-MgCl 2 salt while exposing pure Cr specimens in the melt at 600 and 700 °C for 1000 h. Low reactivity was observed with the baseline salt, while both the Eu and EuCl 3 additions to the salt resulted in increased mass loss of Cr specimens. A capillary electrophoresis technique was developed to enable the detection of low parts-per-million (ppm) concentrations of europium (Eu) in chloride salt both before and after exposure of Cr metal. This technique provides a sensitive and reliable method for monitoring trace levels of Eu, which is critical for understanding the interactions and potential contamination effects in salt-facing materials during reactor operation. This quantification technique can be applied to other cationic and anionic contaminants in halide salts.

chloride salt↗
DOE OSTI2021

Investigation of breakaway corrosion observed during oxide growth in pure and low alloying element content Zr exposed in water at 360°C

The addition of small concentrations of alloying elements to pure zirconium can prevent nuclear fuel cladding material from undergoing unstable oxide growth in aqueous environments at light water reactor operating temperatures. The role of alloying elements in stabilizing the oxide growth is examined in this paper, to better understand the oxide growth stabilization mechanism. To this end, a set of initial, short-duration corrosion experiments were performed, followed by oxide layer characterization. Specifically, ten model Zr alloys were selected to test the effect of small alloying additions on the alloy corrosion rate and corrosion breakaway. Additionally, these alloys were corrosion tested in pure water in an autoclave at 360°C for up to 70 days. The alloys included crystal bar Zr, sponge Zr, and model alloys with small concentrations of Sn, Fe, and Cr. After testing, the alloys were characterized using scanning electron microscopy (SEM) and synchrotron µ-X-ray fluorescence (µXRF) to study how the structure of the oxide and alloying element distribution related to unstable oxide growth. Initial results in the 360°C water environment showed breakaway oxidation may be caused by unstable oxide growth due to heterogeneous distribution of the alloying elements. Heterogeneous distribution of alloying elements was correlated to the occurrence of unstable oxide growth (either nodule-like, grain boundary penetration, or differential grain-to-grain growth). It is possible that this heterogeneity, made possible by low alloying element content, can cause breakaway corrosion, but further study is warranted.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2022

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

In this study, we use a random forest (RF) model to predict the critical cooling rate (R C ) for glass formation of various alloys from features of their constituent elements. The RF model was trained on a database that integrates multiple sources of direct and indirect R C data for metallic glasses to expand the directly measured R C database of less than 100 values to a training set of over 2000 values. The model error on 5-fold cross-validation (CV) is 0.66 orders of magnitude in K/s. The error on leave-out-one-group CV on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass systems for future study, but the model is most useful for the identification of alloy systems likely to contain good glass formers rather than detailed discovery of bulk glass composition regions within known glassy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Influence of the Anionic Zinc-Adeninate Metal–Organic Framework Structure on the Luminescent Detection of Rare Earth Ions in Aqueous Streams

Rare earth elements (REEs) are critical to numerous technologies; however, a combination of increasing demand, environmental concerns, and monopolistic marketplace conditions has spurred interest in boosting the domestic REE production from sources such as coal utilization byproducts. The economic viability of this approach requires rapid, inexpensive, and sensitive analytical techniques capable of characterizing the REE content during resource exploration and downstream REE processing (e.g., analyzing REE separation, concentration, and purification production steps). Luminescence-based sensors are attractive because many REEs may be sensitized to produce element-specific emission. Hence, a single material may simultaneously detect and distinguish multiple REEs. Metal–organic frameworks (MOFs) can sensitize multiple REEs, but their viability has been hindered by sensitivity and selectivity challenges. Understanding how the MOF structure impacts the REE sensing efficacy is critical to the rational design of new sensors. Here, we evaluate the sensing performance of seven different anionic zinc-adeninate MOFs with different organic linkers and/or structures for the visible-emitting REEs Tb, Dy, Sm, and Eu. The choice of a linker determines which REEs are sensitized and significantly influences their sensitivity and selectivity against competing species (here, Fe(II) and HCl). For a given linker, structural changes to the MOF can further fine-tune the performance. The MOFs produce some of the lowest detection limits (sub-10 ppb for Tb) reported for the aqueous sensitization-based REE detection. Importantly, the most selective MOFs demonstrated the ability to sensitize the REE signal at sub-ppm levels in a REE-spiked acid mine drainage matrix, highlighting their potential for use in real-world sensing applications.

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

Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this article, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. In conclusion, our numerical results show that MLMC can accelerate UQ tasks around 2.23x, compared to the classical Monte Carlo (MC) method, which is widely known as ensemble average in the CPFEM literature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On residual stress analysis and microstructural evolution for stainless steel type 304 spent nuclear fuel canisters weld joint: Numerical and experimental studies

Chloride-induced stress corrosion cracking (CISCC) in the weldments of austenitic stainless steel canisters is one of the primary safety concerns during the dry storage of used nuclear fuel at Independent Spent Fuel Storage Installations in coastal areas. In order to evaluate the CISCC behavior in the canister, a 3D sequentially coupled thermo-mechanical finite element model was built to simulate the residual stresses induced by two intersecting longitudinal and circumferential multi-pass welds in austenitic stainless steel mockup canister. Weld-induced residual stresses from simulation were in good agreement with the experimental measurement results by deep-hole drilling and contour methods. Through-wall high tensile axial and hoop stresses were observed in both the fusion zone and heat affected zone (HAZ) in the longitudinal and circumferential weld, respectively. On the other hand, the microstructure within the weldments of 304 stainless steel mockup was characterized using optical microscopy, scanning electron microscopy, electron back-scattered diffraction and energy dispersive X-ray spectroscopy methods. A few amount of MnS inclusions in both the base metal and HAZ, and a lot of (Mn,Si) x O y inclusions in the fusion zone were found. Meanwhile, there is some α'-martensite in the base metal, while the martensite was reversed in the HAZ. In the fusion zone, there is a lot of δ-ferrite. The martensite reversion phenomenon was also verified by the combination of Feritscope test and thermal simulation, which could predict a martensite reversion temperature as 400C°. The welding residual stresses and microstructure analysis results could offer the instructive information for the prediction of CISCC behavior of the spent nuclear fuel canisters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure and lithium insertion in oxides of molybdenum

Oxides of molybdenum, MoO x , have a rich structural chemistry arising from the accommodation of oxygen deficiency as MoO 3 is reduced and varied redox behavior arising from the ability of Mo to take on several different oxidation states. We review MoO 3 , MoO 2 , and all the reduced Mo oxides with intermediate compositions for their performance as Li-ion battery electrode materials. These reduced oxides are perhaps the most structurally diverse in the field of energy storage materials, taking on structures ranging from ones with crystallographic shear to bronze-like structures and distorted rutile. The crystal structure can have a significant impact on the performance of battery materials, which makes the reduced Mo oxides a promising domain of study. Electrochemical studies of these oxides from as early as 1971 to as recently as 2022 are compiled, and characteristics of capacity, capacity retention, and rate performance are compared. We find that certain oxides indeed display promising and highly reversible capacities for Li + storage. Typical redox voltages for Mo oxides lie in a regime that hinders maximizing energy density when they are paired with higher-voltage cathodes or lower-voltage anodes. The possibility of decreasing the redox voltage in the future will expand the promise of these materials while offering an alternative to more critical elements such as Nb.

25 ENERGY STORAGE↗
DOE OSTI2021

Experimental Study of Mechanical Response of Polyurethane Rubber at Varying Strain Rates for W80-4 System

This report describes an experimental study to determine the mechanical behavior of the polyurethane rubber material that was used in the W80-4 MCC Shock/Breach Phase 1 test series. Compression experiments were conducted on cylindrical specimens over a wide range of loading rates to characterize the material over the range of strain rates that were experienced in the shock/breach testing. Additionally, specimen diameter was varied to determine the effect of confinement on the material response and was found to be significant. This data is used to populate a material model to enable accurate analyses and finite element simulations of the shock/breach test series.

36 MATERIALS SCIENCE↗
DOE OSTI2023

FINITE ELEMENT MODEL MESH REFINEMENT EFFECTS ON QUALIFICATION OF NUCLEAR GRADE GRAPHITE COMPONENTS

The American Society of Mechanical Engineers (ASME) provides the full and simplified design-by-analysis probabilistic assessments for determining acceptance of nuclear grade graphite core components. The assessments can be characterized by three parts: (1) a component stress distribution, often determined by a finite element (FE) model; (2) a Weibull probability density function (pdf) that characterizes the experimental tensile strength distribution; and (3) the post-processor, which combines the FE model and the Weibull strength distribution in accordance with the full and simplified assessments to determine component acceptance. It is known that the level of mesh refinement in FE models can affect the modeled component’s calculated stress distribution. Depending on the component geometry, the stress distribution may converge with sufficient refinement. It was previously unknown whether the acceptance decision resulting from the full and simplified assessments might change even with sufficient mesh refinement. This study explores that question using experimental strength results for a dog-bone geometry for two graphite grades, IG-110 and PCEA. The simplified assessment has two criteria that must be met, the first limits the combined membrane stress by the allowable stress and the second limits the peak equivalent stress by the allowable stress scaled by the ratio of flexural to tensile strength. In the application of the simplified assessment, convergence of the peak equivalent stress required extreme mesh refinement, however, the acceptance decision was not affected. It is hypothesized that more complex geometries with stress concentrations may present mesh refinement effects on the simplified assessment acceptance decision. Mesh refinement did affect the acceptance decision in the full assessment for the applied pressure loadings in this study. This work suggests component stress distribution convergence is not a sufficient criteria for POF convergence in the full assessment and that mesh refinement should continue until the POF has converged, especially where the resulting POF is bordering the SRC acceptable POF limit.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

FIB-ToF-SIMS characterization of irradiated U-10Zr

Post-irradiation examination (PIE) is critical for the performance assessment and qualification of nuclear fuels. Secondary ion mass spectrometry (SIMS) is a powerful materials characterization technique that allows for elemental and isotopic mapping with a depth resolution greater than EDS and EPMA. However, it has not yet been applied to PIE of metallic nuclear fuel. Here, in this work, we characterize an fast neutron spectrum irradiated U-10Zr fuel sample using a time-of-flight SIMS (ToF-SIMS) system connected to a FIB/SEM system, which allows for flexible sample analysis compared to a dedicated ToF-SIMS instrument. Analysis of the resulting hyperspectral micrograph data was aided by the development of an unsupervised machine learning (ML) algorithm that iterates on existing methods to segment the 3D micrographic datasets based on the similarity of mass spectra. The results showed that the FIB-ToF-SIMS instrument was potentially capable of spatially resolving closed fission gas bubbles in 3D by continued ion sputtering of the analyzed volume. Additionally, the ML algorithm proved useful in revealing the chemical segregation of light fission products (those with an atomic mass between approximately 85–105 amu, such as ruthenium and rhodium) plus matrix zirconium, heavy fission products (those with an atomic mass between approximately 135–150 amu, such as the lanthanides) and uranium. Future studies are planned to conduct FIB-ToF-SIMS analysis on more irradiated U-Zr samples to study the constituent redistribution.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗