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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermally Stable Single-Atom Heterogeneous Catalysts

Single-atom catalysts (SACs) have attracted extensive attention in fields related to energy, environment, and material sciences because of the high atom efficiency and the unique properties of these materials. Many approaches have hitherto been successfully established to prepare SACs, including impregnation, pyrolysis-involved processes, atom trapping, and coprecipitation. However, under typical reaction conditions, single atoms on catalysts tend to migrate or agglomerate, forming nanoclusters or nanoparticles, which lowers their surface free energy. Efforts are required to develop strategies for improving the thermal stability of SACs while achieving excellent catalytic performance. Here in this Progress Report, recent advances in the development of thermally durable single-atom heterogeneous catalysts are discussed. Several important preparation approaches for thermally stable SACs are described in this article. Fundamental understanding of the coordination structures of thermally stable single atom prepared by these methods is discussed. Furthermore, the catalytic performances of these thermally stable SACs are reviewed, including their activity and stability. Finally, a perspective of this important and rapidly evolving research field is provided.

coordination structure↗
DOE OSTI2022

Deep learning the properties of inorganic perovskites

We report the ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Finding the perfect imperfection: Accelerated, computationally driven discovery and design of quantum defects

Optically addressable spin defects have emerged as the leading platforms for quantum sensing and communication in solid-state systems. While traditional efforts have concentrated on a focused set of well-studied defects, recent advances in high-throughput computational methods have shown promise for large-scale exploration of defects across diverse semiconductor hosts. By cataloging key properties of quantum defects in computational databases, high-throughput screening techniques can systematically suggest and design novel candidates. In this article, we highlight recent advances in data-driven quantum defect design aimed at addressing critical materials science challenges such as host materials selection, defect stability, and desirable electronic and optical properties. Here, we emphasize the importance of electronic-structure-guided searches across various materials and illustrate how high-throughput computations contribute to our understanding of design principles for quantum defects. Additionally, we outline ongoing challenges and emerging opportunities in this rapidly developing field.

Xiong, Yihuang [Dartmouth College, Hanover, NH (Un↗
DOE OSTI2024

Synthesis of cerium precursors with alkoxide ligands for degradation to cerium oxide nanoparticles

Lanthanide oxide and sulfide nanoparticles present intriguing theoretical questions regarding their electronic structures, alongside numerous potential applications in material science and catalysis. Although these materials can be formed through hydrolysis, their synthesis via thermolysis is crucial for nanoscale materials and practical applications. This research aims to enhance our understanding of the decomposition mechanisms by focusing on cerium compounds, which are analogous to those studied previously. Currently, our knowledge of the decomposition mechanisms of cerium alkoxide precursors is limited. The objective of this project is to develop a mechanistic understanding of the formation of cerium oxide nanoparticles from cerium alkoxide precursors, employing a variety of techniques. These techniques include collision-induced dissociation in an ion-trap mass spectrometer, nuclear magnetic resonance (NMR) spectroscopy, thermogravimetric analysis coupled with differential scanning calorimetry (TGA-DSC), and gas chromatography-mass spectrometry (GC-MS) to investigate the decomposition mechanisms of the synthesized precursors.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DNA-caged nanoparticles via electrostatic self-assembly

DNA-modified nanoparticles enable DNA sensing and therapeutics in nanomedicine and are also crucial for nanoparticle self-assembly with DNA-based materials. However, methods to conjugate DNA to nanoparticle surfaces are limited, inefficient, and lack control. Inspired by DNA tile nanotechnology, we demonstrate a new approach to nanoparticle modification based on electrostatic attraction between negatively charged DNA tiles and positively charged nanoparticles. This approach does not disrupt nanoparticle surfaces and leverages the programmability of DNA nanotechnology to control DNA presentation. We demonstrated this approach using a variety of nanoparticles, including polymeric micelles, polystyrene beads, gold nanoparticles, and superparamagnetic iron oxide nanoparticles with sizes ranging from 5–20 nm in diameter. DNA cage formation was confirmed through transmission electron microscopy (TEM), neutralization of zeta potential, and a series of fluorescence experiments. DNA cages present “handle” sequences that can be used for reversible target attachment or self-assembly. Handle functionality was verified in solution, at the solid–liquid interface, and inside fixed cells, corresponding to applications in biosensing, DNA microarrays, and erasable immunocytochemistry. Finally, these experiments demonstrate the versatility of the electrostatic DNA caging approach and provide a new pathway to nanoparticle modification with DNA that will empower further applications of these materials in medicine and materials science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Fabrication of ultrathin suspended membranes from atomic layer deposition films

In this report, ultrathin films suspended as freestanding membranes are critical to many microelectronic and materials science applications. However, fabrication methods are currently limited in either their flexibility, due to material selectivity issues during the final membrane release, or their scalability. Here, we demonstrate a novel fabrication process for suspending ultrathin films with thicknesses as low as 4 nm and lateral dimensions up to 20 x 1000 μm from a variety of materials grown by atomic layer deposition. A silicon nitride membrane serves as the support for a sacrificial polymer layer and an ultrathin atomic layer deposition film which, after plasma etching, will form the membrane. The high chemical selectivity between atomic layer deposition-grown transition metal nitrides and oxides and the sacrificial polymer means that ultrathin films of a variety of materials can be released without damage using a single process. Electrically conductive titanium nitride membranes can be produced by this method and are of significant interest for electron microscopy applications. Electron transparency of titanium nitride membranes was found to be ~14% higher than silicon nitride of the same thickness, and of similar conductivity to graphite, meaning that ultrathin, conductive, and electron transparent membranes can be fabricated at scale. These membranes are ideal supports for electron and photon characterization techniques, as well as microelectromechanical system applications that require a conductive membrane.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electronic Structure, Spectroscopy and Correlation Effects in Novel Materials

This project was targeted on electronic structure, spectroscopic studies, and correlation effects in a variety of novel materials of great current interest. Spectroscopies resolved highly in momentum, energy or spatial dimensions are playing a key role in unraveling the nature of the ground state and excitation properties in wide classes of novel materials. The seminal insights thus obtained are of critical importance not only for answering some of the fundamental questions facing condensed matter physics and materials science today, but also for understanding and thus helping to design and develop new materials with desirable properties, which will continue to be the key to the survival of mankind and its energy needs as a technological society long into the future. However, spectroscopies do not provide a direct map of electronic states, but act as a very complex ‘filter’ or ‘mapping’ of the underlying spectrum. This connection between the electronic states and measured spectra—called the ‘matrix element effect’—is in general an extremely complex function of the phase space of the experiment (e.g. energy/polarization of photons in photoemission), presenting both a challenge and an opportunity. So motivated, this project pursued techniques for realistic treatment of electronic spectra of a wide variety of materials, which served as a prelude to formulating and implementing methodologies for making direct connection with various spectroscopies such as ARPES, STS/STM, and inelastic light scattering. Specific systems are topological materials, including 2D ultrathin films beyond graphene, novel superconductors, and nanocrystals, among others. Our goal was to exploit the strengths of various spectroscopies to piece together the most complete picture of electronic states in systems of current interest, enabling direct and sharpened confrontations with theoretical models, and also to help advance the reach of various spectroscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Generalized kinetic model for defect evolution in irradiated materials

A fundamental and open problem in materials science is to determine how the structural properties of irradiated and self-irradiated materials evolve in time. Structural defects generated by irradiation lead to changes in a material’s macroscopic properties, and these macroscopic changes, such as volumetric swelling, can in turn give rise significant alterations of the functionality of a material. Here, we develop a unified analytical model to understand the complex interplay between aggregation, fragmentation, and recombination processes involving mobile clusters of defects such as bubbles, voids, and interstitial atoms in materials subject to radiative sources. Specifically, we employ a mean field approach to derive a system of coupled kinetic equations that describes both the time evolution of the density of each type of defect and how clusters of each defect form and grow as the material ages.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison of Orientation Mapping in SEM and TEM

In this work, multiple experimental configurations for performing nanoscale orientation mapping are compared to determine their fidelity to the true 3D microstructure of a sample. Transmission Kikuchi Diffraction (TKD) experiments in an SEM and nanobeam diffraction (NBD) experiments in a TEM were performed on thin electrodeposited hard Au films with two different microstructures. The Au samples either have a grain size that is >50 nm or <20 nm. The same regions of the samples were measured with TKD apparatuses at 30 kV in an SEM with detectors in the horizontal and vertical configurations and in the TEM at 300 kV. Under the proper conditions, we demonstrate all three configurations can produce data of equivalent quality.Each method has strengths and challenges associated with its application and representation of the true microstructure. The conditions needed to obtain high-quality data for each acquisition method and the challenges associated with each will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integrated Computational Materials and Mechanical Modeling for Additive Manufacturing of Alloys with Graded Structure Used in Fossil Fuel Power Plants

Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Solid-state batteries and the critical role of interfaces

The Grand Challenge for the next generation of energy storage technologies is no longer the identification of electroactive cathode or anode materials thanks to extensive worldwide synthesis efforts along with theory and modeling like the Materials Project.1 Instead, the critical challenges revolve around assembling materials in the right architecture to achieve maximum performance and cell life at reasonable temperatures and pressures. Nowhere is this more critical than on the next generation of energy storage technologies revolving around all solid-state batteries. These batteries are the ultimate challenge for materials science requiring new ways to assemble multiple dissimilar materials such that: (1) interfaces are optimized to facilitate ion motion across the different compounds while (2) maintaining chemical stability and (3) simultaneously preserving the crystal structures of each phase during (4) large volume changes due to shuttling of lithium, at (5) room temperature and under (6) atmospheric pressure. To address these requirements will require insights and expertise from research fields outside the traditional lithium-ion battery community such as solid oxide fuel cells, synthesis science, barrier layers, interface formers, sintering, and mechanical properties.

25 ENERGY STORAGE↗
DOE OSTI2025

Fundamental Insights into Cathode Stability: Linking Compositional Tuning and Local Coordination in Complex Metal Oxides under Aqueous Transformations

Compositional tuning of complex metal oxides in Li-ion battery materials influences their performance as well as their end-of-life behavior, in particular, the tendency to release toxic metal cations in aqueous solution. We modeled ternary variants of a parent LiCoO 2 delafossite structure by varying the metal identity and relative amounts. This yielded ten model formulations of Li(A 4/6 B 1/6 C 1/6 )O 2 , where the material is enriched with the A metal and doped with B and C, with Ni, Mn, Co, Fe, Al, V, and Ti as constituent metals. To assess their stability in aqueous conditions, metal release energetics were calculated using a combination of Density Functional Theory calculations and thermodynamics. Metal release in ternary oxides is dictated by subtle variations in the coordination environment of the leaving group. To identify governing chemical features across diverse compositions with varying local coordination environments, we leverage random forest regression and descriptor importance analysis. A key result is that metal–oxygen orbital hybridization, quantified using a projected density-of-states-derived descriptor, H d/p , provides a physically grounded measure of interaction strength that governs metal release energetics. This refined perspective goes beyond conventional oxidation state considerations and offers more robust insights for materials science. Finally, we model defect surface-bound O 2 dimer formation as a proxy for reactive oxygen species (ROS) generation. The results show that Ni-rich compositions more readily stabilize spin-polarized O 2 dimers, corroborating experimental reports of an increased ROS-driven biological response. In conclusion, our results establish a compositional and electronic basis for metal release and surface oxygen reactivity that form a rationale for complex metal oxide design principles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep Learning for Spectroscopic X-ray Nano-Imaging Denoising

Synchrotron transmission X-ray microscopy with absorption near edge structure (TXM-XANES) is a powerful tool for investigating the structure and composition of materials at nano- to meso-scales. It is, however, often challenged by high levels of noise that obscure critical details at the single-pixel level. To address this issue, a deep learning-based algorithm is developed for suppressing the image noise, grounded in self-supervised learning principles. In contrast to traditional image denoising methods, this approach successfully enhances the visibility of fine details while significantly reducing the noise in the X-ray images. Through this advancement, the potential of the approach for improving the accuracy and interpretability of the TXM-XANES data is demonstrated, thereby enabling more precise detection of nanoscale phenomena such as inhomogeneous cation redox and metal segregation in battery cathode materials. This technique offers an effective new avenue for harnessing the full potential of synchrotron TXM-XANES imaging, paving the way for a range of exciting new studies in materials science and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Phase Stability of Yttrium Titanium Oxide

This proposed study aims at elevating Idaho National Laboratory (INL)’s portfolio in nuclear material research on two major fronts: 1) expanding INL’s material characterization capabilities to provide holistic microstructural and microchemical characterization ranging from atomic scale to millimeter scale, and 2) filling the knowledge gap on the effect of microstructural evolution on material phase stability at extreme conditions resembling those inside nuclear reactors, using Yttrium-Titanium-Oxide (YTO) as a model material. Previous research indicated the kinetics of phase transition of YTO materials varies between bulk samples and powder samples, suggesting that microstructural characteristics such as defect, micro-strain, and crystallite size could play a significant role in phase transition. The effect of crystallite size on the thermodynamics of phase transition has been well documented for other ceramic materials at nanometer crystallite size, with potential impacts from surface and strain energy and defects. In this proposed study, we will perform microstructural (micro-strain, crystallite size, defects, phase) and microchemical (elemental mapping) analyses through in-situ high temperature measurements using X-ray diffraction (XRD; at millimeter scale for powder and bulk samples) and transmission electron microscopy (TEM, at nanometer scale and atomic scale for powder and thin foil samples), to provide a comprehensive view of the effect of microstructural and microchemical evolution on phase transition and phase stability of YTO materials. These findings will also establish a roadmap and provide new insights on the phase stability of other materials that are critical to nuclear energy and other renewable energy infrastructure. Besides advancing our understanding of this fundamental material science question, this project will also enable the development of state-of-the-art material characterization capabilities at INL/MFC for microstructural and microchemical analysis at high temperature conditions. The new collaboration between the High-Resolution Materials Characterization group at INL and the Electron Microscopy group at the University of Florida (UF) in this project will open the possibilities for more fruitful and synergetic cutting-edge nuclear material research in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Roadmap on electronic structure codes in the exascale era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.

36 MATERIALS SCIENCE↗