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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Optical Property Requirements for Glasses, Ceramics and Plastics in Spacecraft Window Systems

This is a preliminary draft of a standard published by the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) that is intended to provide uniform window optical design requirements in support of the development of human-rated spaceflight hardware. The material covered in this standard is based on data from extensive testing by the Advanced Sensing and Optical Measurement Branch at NASA Langley Research Center, and compiled into requirements format by the NASA JSC Structural Engineering Division. At the time of this initial document release, a broader technical community has not reviewed this standard. The technical content of this standard is primarily based on the Constellation Program Orion Crew Exploration Vehicle Window Optical Properties Requirements, CxP 72407, Baseline. Unlike other optical requirements documents available for human rated spacecraft, this document includes requirements that ensure functionality for windows that contain glass/ceramic and/or plastic window substrate materials. These requirements were derived by measuring the optical properties of fused silica and aluminosilicate glass window assemblies and ensuring that the performance of any window assembly that includes a plastic pane or panes will meet the performance level of the all-glass assemblies. The resulting requirements are based upon the performance and parameter metrology testing of a variety of materials, including glass, transparent ceramics, acrylics, and polycarbonates. In general, these requirements are minimum specifications for each optical parameter in order to achieve the function specified for each functional category, A through D. Because acrylic materials perform at a higher level than polycarbonates in the optics regime, and CxP/Orion is planning to use acrylic in the Orion spacecraft, these requirements are based heavily on metrology from that material. As a result, two of the current Category D requirements for plastics are cited in such a way that will result in the screening out of polycarbonates. It is acknowledged that many polycarbonates can perform the functions of Category D, such as piloting and imagery with lens with apertures up to 25mm, without performance issues. Therefore, this forward warns users that certain requirements, such as birefringence and wavefront, for Category D plastics need to be revised to allow those polycarbonates that perform adequately in Category D to be accepted, while at the same time, screen out those materials that do not perform up to par. At the time of document release, the requirements in question have been identified by a TBD beside the proposed requirement criteria (which is based upon acrylic performance). Vehicles that are designed with acrylic materials for windowpanes are encouraged to use the values presented in this document for all requirements, in order to ensure adequate optical performance.

Lynda R Estes↗
DOE OSTI2022

Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Recovery of Rare Earths, Precious Metals and other Critical Materials from Geothermal Waters with Advanced Sorbent Structures - CRADA 355 (Abstract)

The ability to recover valuable trace level minerals from geothermal brines using high-performance solid-phase sorbents will be explored and developed. A compressive range of sorbent materials will be screened for application to metal extraction from geothermal brines. Preferred sorbents from extraction of trace levels of rare earths (REs), precious metals (PMs), and other critical/strategically valuable materials (CMs) such as Zn, Mn, Te, Sc, Se and U from geothermal brines will be identified. For the preferred sorbents PNNL will determine the volumes they are capable of providing efficient extraction from. The thermal and chemical limits (including, acid, sulfur, salt) for performance of the preferred sorbent materials will be determined; with a target of at least 125°C and perhaps as high as 400° C. Sorbent form factors (including, packed bed, membrane, mats) that can function efficiently and be installed cost effectively in geothermal energy plants will be assessed for chemical and economic viability. Material regeneration and cyclic utilization will be demonstrated, targeting hundreds to thousands of cycles. Options for recovery and purification (including, selective separation of heavy REs) of collected materials will be explored. A techno-economic analysis (TEA) will be performed to assess the best approach to provide a value-added extraction process for geothermal energy systems. The sorbent materials and engineering analysis will be applicable to other industrial processes in which secondary recovery of valuable materials could provide economic benefit.

15 GEOTHERMAL ENERGY↗
NASA NTRS2004

Measuring Permeability of Composite Cryotank Laminants

This paper describes a test method developed to identify whether certain materials and material systems are suitable candidates for large pressurized reusable cryogenic tanks intended for use in current and future manned launch systems. It provides a quick way to screen numerous candidate materials for permeability under anticipated loading environments consistent with flight conditions, as well as addressing reusability issues. cryogenic tank, where the major design issue was hydrogen permeability. It was successfully used to evaluate samples subjected to biaxial loading while maintaining test temperatures near liquid hydrogen. After each sample was thermally preconditioned, a cyclic pressure load was applied to simulate the in-plane strain. First permeability was measured while a sample was under load. Then the sample was unloaded and allowed to return to ambient temperature. The test was repeated to simulate reusability, in order to evaluate its effects on material permeability.

Oliver, Stanley T.↗
DOE OSTI2020

An automatically curated first-principles database of ferroelectrics

Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. Finally, we contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.

36 MATERIALS SCIENCE↗
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↗
NASA NTRS2005

Candidate Materials Evaluated for a High-Temperature Stirling Convertor Heater Head

The Department of Energy and NASA have identified Stirling Radioisotope Generators (SRGs) as a candidate power system for use on long-duration, deep-space science missions and Mars rovers. One of the developments planned for an upgraded version of the current SRG design is to achieve higher efficiency by increasing the overall operating temperature of the system. Currently, the SRG operates with a heater head temperature of 650 C and is fabricated from the nickel-base superalloy 718. The current operating temperature is at the limit of alloy 718 s capability, and any planned increase in temperature will be contingent on identifying a more capable material from which to fabricate the heater head. To this end, personnel at the NASA Glenn Research Center are evaluating advanced materials for a high-temperature heater head to allow a higher convertor temperature ratio and, thus, increase the system efficiency. A generic list of properties that were used to screen the candidate materials follows: (1) creep, (2) fabricability, (3) helium gas containment, (4) long-term stability and compatibility, (5) ability to form a hermetical closeout seal, and (6) ductility and toughness.

Bowman, Randy R.↗
DOE OSTI2022

Reflection Optical Imaging to Study Oxygen Evolution Reactions

Discovery of high-efficiency water-splitting catalysts is critical for clean hydrogen fuel generation. Quantitative and reliable chemical imaging methods can play a crucial role in providing critical catalyst efficiency information for materials development and screening. Here, we developed a reflection optical imaging method to map the performance of transition metal oxide catalysts for oxygen evolution reaction (OER). The generated high concentration of oxygen in the electrolyte will decrease the refractive index of the solutions near the electrode and change the reflectivity of optical images. We successfully imaged and measured the OER on cobalt oxide, and the localized current density and overpotential distribution maps have been extracted. The oxidation and reduction of cobalt oxide can be imaged with our reflection optical imaging method as well. These maps showed that the catalytic activities are quite different at different locations, and some regions showed much higher activities. This is due to the non-uniformity created by the catalyst synthesis process and demonstrates the importance of using the optical imaging method to map the localized catalytic efficiency. In addition, the NiFe alloy’s OER catalytic activities have been studied as well. The proposed reflection optical imaging method could find many applications in catalyst activity measurement and screening.

Electrochemistry↗
NASA NTRS1971

Evaluation of heat- and blast-protection materials

A program was initiated at the Kennedy Space Center in December 1967 and conducted through December 1969 to evaluate the performance of heat- and blast-protection materials for ground support equipment used during the Apollo/Saturn launches. Materials believed to be generally suitable for heat and blast protection were subjected to launch-exposure tests. Tests were made during the Apollo/Saturn 502, 503, and 505 launches. Tests were also made in a local laboratory, as an alternative to the restrictive requirements of launch-exposure tests, to determine the effects of torch-flame exposure on ablative materials. Five materials were found to be satisfactory in all major test categories. It was determined that torch-flame tests can probably be utilized as an acceptable substitute for the booster-engine-exhaust exposure tests for basic screening of candidate materials.

Morrison, J. D.↗
NASA NTRS2014

Hot Hydrogen Testing of Tungsten-Uranium Dioxide (W-UO2) CERMET Fuel Materials for Nuclear Thermal Propulsion

CERMET fuel materials are being developed at the NASA Marshall Space Flight Center for a Nuclear Cryogenic Propulsion Stage. Recent work has resulted in the development and demonstration of a Compact Fuel Element Environmental Test (CFEET) System that is capable of subjecting depleted uranium fuel material samples to hot hydrogen. A critical obstacle to the development of an NCPS engine is the high-cost and safety concerns associated with developmental testing in nuclear environments. The purpose of this testing capability is to enable low-cost screening of candidate materials, fabrication processes, and further validation of concepts. The CERMET samples consist of depleted uranium dioxide (UO2) fuel particles in a tungsten metal matrix, which has been demonstrated on previous programs to provide improved performance and retention of fission products1. Numerous past programs have utilized hot hydrogen furnace testing to develop and evaluate fuel materials. The testing provides a reasonable simulation of temperature and thermal stress effects in a flowing hydrogen environment. Though no information is gained about radiation damage, the furnace testing is extremely valuable for development and verification of fuel element materials and processes. The current work includes testing of subscale W-UO2 slugs to evaluate fuel loss and stability. The materials are then fabricated into samples with seven cooling channels to test a more representative section of a fuel element. Several iterations of testing are being performed to evaluate fuel mass loss impacts from density, microstructure, fuel particle size and shape, chemistry, claddings, particle coatings, and stabilizers. The fuel materials and forms being evaluated on this effort have all been demonstrated to control fuel migration and loss. The objective is to verify performance improvements of the various materials and process options prior to expensive full scale fabrication and testing. Post test analysis will include weight percent fuel loss, microscopy, dimensional tolerance, and fuel stability.

Hickman, Robert↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗
DOE OSTI2020

Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

36 MATERIALS SCIENCE↗
NASA NTRS1982

Fatigue and fracture research in composite materials

The fatigue, fracture, and impact behavior of composite materials are investigated. Bolted and bonded joints are included. The solutions developed are generic in scope and are useful for a wide variety of structural applications. The analytical tools developed are used to demonstrate the damage tolerance, impact resistance, and useful fatigue life of structural composite components. Standard tests for screening improvements in materials and constituents are developed.

T. Kevin O'Brien↗
DOE OSTI2021

Methods to accelerate high-throughput screening of atomic qubit candidates in van der Waals materials

The discovery of atom-like spin emitters associated with defects in two-dimensional (2D) wide-bandgap (WBG) semiconductors presents new opportunities for highly tunable and versatile qubits. So far, the study of such spin emitters has focused on defects in hexagonal boron nitride (hBN). However, hBN necessarily contains a high density of nuclear spins, which are expected to create a strong incoherent spin-bath that leads to poor coherence properties of spins hosted in the material. Therefore, identification of new qubit candidates in other 2DWBG materials is necessary. Given the time demands of ab initio methods, new approaches for rapid screening and calculations of identifying properties of suitable atom-like qubits are required. In this work, we present two new methods for rapid estimation of the zero-phonon line (ZPL), a key property of atomic qubits in WBG materials. First, the ZPL is calculated by exploiting Janak’s theorem. For finite changes in occupation, we provide the leading-order estimate of the correction to the ZPL obtained using Janak’s theorem, which is more rapid than the standard method (ΔSCF). Next, we demonstrate an approach to converging excited states that is faster for systems with small strain than the standard approach used in the ΔSCF method. We illustrate these methods using the case of the singly negatively charged calcium vacancy in SiS2, which we are the first to propose as a qubit candidate. This work has the potential to assist in accelerating the high-throughput search for quantum defects in materials, with applications in quantum sensing and quantum computing.

Kuate Defo, Rodrick (ORCID:0000000242788196)↗
DOE OSTI2023

Modelling and analysis of salt-convection effect on oxide reduction process for uranium oxides using smoothed particle hydrodynamics

Oxygen ion transport via molten salt continuum is a key kinetic feature of electrochemical reduction process for uranium oxides in molten salt. During the electrolysis, the evolution of oxygen ion concentration field in molten salt continuum appears from porous metallic layer to open bulk salt. Despite the important role affecting process throughput, its effect coupled with controlled salt movement was not adequately investigated. To capture the reactive oxygen ion transport phenomena, a multidimensional reduction model based on smoothed particle hydrodynamics (SPH) was combined with the advection-diffusion model. A pelletized oxide fuel bed capturing interconnected salt continuum is considered as a reactant material geometry, which allows forced convective delivery of molten salt through the reactant materials with a relatively low pressure head. Here, in this study, reduction time of a pelletized-fuel bed was quantified with respect to key design parameters of the reactant material configuration. The simulation results demonstrated the advantage of salt convection which effectively removes oxygen ion products from the reactant materials and consequently accelerates the reduction process. Moreover, the reduction-time correlation of a single pelletized fuel was developed as a function of relevant dimensionless parameters and was extended to the application for a pelletized-fuel bed. Finally, several insights and implications in the use of a pelletized oxide fuel were presented. The developed SPH-based framework enabled multi-physics analysis of governing dynamics in appreciating and screening candidate reactant material configurations in a cost-effective manner.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Calorimetric metal vapor adsorption energies for characterizing industrial catalyst support materials

Many important catalysts and electro-catalysts for energy and environmental technologies involve late transition metal atoms and nanoparticles dispersed across the surface of high-surface-area support materials, for example, oxide nanoparticles. The surface reactivity and long-term stability against deactivation of these materials depends strongly on the strength of bonding of the metal atom and nanoparticles to the support surface. Here, we introduce a novel calorimetry method that implements a LiTaO 3 crystal for heat detection and extends metal adsorption calorimetry to surfaces of nanomaterials that are deposited from liquid solutions, as is typical when preparing industrial catalyst supports. This allows one to measure the strength of metal atom bonding and the adhesion energy of metal nanoparticles to clean surfaces of technologically relevant catalyst supports. We demonstrate that here by measuring the heat of Ag vapor adsorption onto the clean surfaces of high-surface-area TiO 2 anatase powdered support materials (i.e., 5-nm-diameter nanoparticles). These heats of adsorption show a pulse-to-pulse standard deviation of only 9.3 kJ/mol (3.3%) for pulses containing only 1% of a monolayer of Ag vapor, with potential for a 5-fold reduction in noise if used with another metal evaporator. The calorimeter was also proven to work well measuring heats of Ag adsorption on calcium niobate(001) nanosheets deposited as thin films from liquid solution. Here, this calorimeter opens up the possibility of using metal adsorption calorimetry to screen catalyst support materials for the strengths with which they bind metal atoms and metal nanoparticles, offering new opportunities to discover better support materials or for basic scientific study of structure / function relationships with respect to metal / support bond energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Atomic oxygen testing with thermal atom systems - A critical evaluation

The use of thermal atom (kinetic energy near 0.04 eV) test methods as a materials selection and screening technique for LEO spacecraft is critically evaluated in this paper. The physics and chemistry of the thermal atom environments are shown to produce specific mass loss rates (mg/sq cm per min) and reaction efficiencies (Re) radically different from those produced in the LEO environment. A response surface study shows that specific mass loss rates change rapidly with plasma-asher parameters and seldom agree with flight data. FEP Teflon is shown to react by a different mechanism than Kapton, polyethylene, or graphite. The Re (Re = volume of material removed/oxygen atom) of Kapton, polyethylene, Mylar, Tedlar, FEP Teflon, and graphite measured in a flowing afterglow apparatus are 0.001 to 0.0001 those measured with high-energy atoms (kinetic energy 1.5 eV or greater) in beam systems or in LEO. The effect of sample temperature and atom impact energy on Re is discussed. A simple kinetic model describing the reaction of atomic oxygen with polymer surfaces is developed. Guidelines and recommendations for thermal atom testing and interpretation of test results are presented.

Koontz, Steven L.↗