Search NASA⌕ Search

SEARCH · Search NASA

Results for “materials screening”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Upper Stage Engine Composite Nozzle Extensions

Carbon-carbon (C-C) composite nozzle extensions are of interest for use on a variety of launch vehicle upper stage engines and in-space propulsion systems. The C-C nozzle extension technology and test capabilities being developed are intended to support National Aeronautics and Space Administration (NASA) and United States Air Force (USAF) requirements, as well as broader industry needs. Recent and on-going efforts at the Marshall Space Flight Center (MSFC) are aimed at both (a) further developing the technology and databases for nozzle extensions fabricated from specific CC materials, and (b) developing and demonstrating low-cost capabilities for testing composite nozzle extensions. At present, materials development work is concentrating on developing a database for lyocell-based C-C that can be used for upper stage engine nozzle extension design, modeling, and analysis efforts. Lyocell-based C-C behaves in a manner similar to rayon-based CC, but does not have the environmental issues associated with the use of rayon. Future work will also further investigate technology and database gaps and needs for more-established polyacrylonitrile- (PAN-) based C-C's. As a low-cost means of being able to rapidly test and screen nozzle extension materials and structures, MSFC has recently established and demonstrated a test rig at MSFC's Test Stand (TS) 115 for testing subscale nozzle extensions with 3.5-inch inside diameters at the attachment plane. Test durations of up to 120 seconds have been demonstrated using oxygen/hydrogen propellants. Other propellant combinations, including the use of hydrocarbon fuels, can be used if desired. Another test capability being developed will allow the testing of larger nozzle extensions (13.5- inch inside diameters at the attachment plane) in environments more similar to those of actual oxygen/hydrogen upper stage engines. Two C-C nozzle extensions (one lyocell-based, one PAN-based) have been fabricated for testing with the larger-scale facility.

Valentine, Peter G.↗
DOE OSTI2020

Rapid and robust antibody Fab fragment crystallization utilizing edge-to-edge beta-sheet packing

Antibody therapeutics are one of the most important classes of drugs. Antibody structures have become an integral part of predicting the behavior of potential therapeutics, either directly or as the basis of modeling. Structures of Fab:antigen complexes have even greater value. While the crystallization and structure determination of Fabs is easy relative to many other protein classes, especially membrane proteins, broad screening and optimization of crystalline hits is still necessary. Through a comprehensive review of rabbit Fab crystal contacts and their incompatibility with human Fabs, we identified a small secondary structural element from the rabbit light chain constant domain potentially responsible for hindering the crystallization of human Fabs. Upon replacing the human kappa constant domain FG loop (HQGLSSP) with the two residue shorter rabbit loop (QGTTS), we dramatically improved the crystallization of human Fabs and Fab:antigen complexes. Our design, which we call “Crystal Kappa”, enables rapid crystallization of human fabs and fab complexes in a broad range of conditions, with less material in smaller screens or from dilute solutions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2016

Mechanical Model Development for Composite Structural Supercapacitors

Novel composite structural supercapacitor concepts have recently been developed as a means both to store electrical charge and to provide modest mechanical load carrying capability. Double-layer composite supercapacitors are often fabricated by impregnating a woven carbon fiber fabric, which serves as the electrodes, with a structural polymer electrolyte. Polypropylene or a glass fabric is often used as the separator material. Recent research has been primarily limited to evaluating these composites experimentally. In this study, mechanical models based on the Multiscale Generalized Method of Cells (MSGMC) were developed and used to calculate the shear and tensile properties and response of two composite structural supercapacitors from the literature. The modeling approach was first validated against traditional composite laminate data. MSGMC models for composite supercapacitors were developed, and accurate elastic shear/tensile properties were obtained. It is envisioned that further development of the models presented in this work will facilitate the design of composite components for aerospace and automotive applications and can be used to screen candidate constituent materials for inclusion in future composite structural supercapacitor concepts.

Micromechanics↗
DOE OSTI2021

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M 2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗
NASA NTRS2006

Thermal Dispersion Within a Porous Medium Near a Solid Wall

The regenerator is a key component to Stirling cycle machine efficiency. Typical regenerators are of sintered fine wires or layers of fine-wire screens. Such porous materials are contained within solid-waH casings. Thermal energy exchange between the regenerator and the casing is important to cycle performance for the matrix and casing would not have the same axial temperature profile in an actual machine. Exchange from one to the other may allow shunting of thermal energy, reducing cycle efficiency. In this paper, temperature profiles within the near-wall region of the matrix are measured and thermal energy transport, termed thermal dispersion, is inferred. The data show how the wall affects thermal transport. Transport normal to the mean flow direction is by conduction within the solid and fluid and by advective transport within the matrix. In the near-wall region, both may be interrupted from their normal in-core pattern. Solid conduction paths are broken and scales of advective transport are damped. An equation is presented which describes this change for a wire screen mesh. The near-wall layer typically acts as an insulating layer. This should be considered in design or analysis. Effective thermal conductivity within the core is uniform. In-core transverse thermal effective conductivity values are compared to direct and indirect measurements reported elsewhere and to 3D numerical simulation results, computed previously and reported elsewhere. The 3-D CFD model is composed of six cylinders in cross flow, staggered in arrangement to match the dimensions and porosity of the matrix used in the experiments. The commercial code FLUENT is used to obtain the flow and thermal fields. The thermal dispersion and effective thermal conductivities for the matrix are computed from the results.

Simon, T.↗
NASA NTRS2014

Influential Factors for Liquid Acquisition Device Screen Selection for Cryogenic Propulsion Systems

This paper presents the influential factors which govern screen selection for liquid acquisition devices (LADs) operating in microgravity conditions for future in-space cryogenic propulsion engines and cryogenic propellant depots. Space flight requirements, which include mass flow rate, acceleration level and direction, and thermal environment, dictate screen selection for a particular mission. The five influential factors include bubble point pressure, flow-through-screen pressure drop, wicking rate, screen compliance, and material compatibility. Governing equations and analytical models for these parameters are developed from first principles. A comprehensive survey of the historical data on coarser LAD meshes over four decades of work is conducted, and liquid hydrogen data for finer Dutch Twill meshes (325 x 2300, 450 x 2750, 510 x 3600) from recently concluded experiments is also presented to validate analytical models. Each of these parameters is measurable from ground based tests, making it facile to predict flight system performance. Therefore analytical models in this paper will be valuable for future LAD designs for both cryogenic and storable propulsion systems. Additionally, analysis will be given on the impact of the factors on liquid hydrogen systems.

Fuel Depot↗
DOE OSTI2022

High Strength Steel-Aluminum Components by Vaporizing Foil Actuator Welding

This project aimed to address the challenge of effectively welding dissimilar materials—high-strength steel and high-strength aluminum for creating lightweight, multi-material automotive components. For automotive companies, reducing weight of a vehicle is critical task regulated by the government to solve the issue of greenhouse gas emissions. Production of lightweight cars and trucks can be achieved by substitution of current all-steel structures with multi-material lightweight structures that include high strength-to-weight-ration materials such as high-strength steels, aluminum alloys, magnesium alloys, titanium alloys, coupled with lightweight designs. This requires dissimilar metal welding, which is challenging for state of the art joining processes such as resistance spot welding. The cycle of melting-cooling-freezing during traditional welding that can easily ruin the designed outstanding properties of the advanced base metals, such as aluminum alloys, making the welded area much weaker than the base metals. To weld two different metals with great difference in melting points, such as aluminum and steel, it’s even more difficult or impossible because of the formation of brittle intermetallic compounds at the welded interface. In this project, a novel welding method, developed at OSU, was selected for validation and development. This novel technology enables welding by impact without melting and proves to be robust to join various dissimilar lightweight metals. Termed as vaporizing foil actuator welding or VFAW, the technology uses a thin aluminum foil that is rapidly vaporized by a high current pulse to produce an explosive-like pressure pulse to drive one metallic piece into another at the high speed required for impact welding. This project entailed development of the early-stage welding technology in terms of (a) the consumables, the welding apparatus and the power sources, (b) coupon scale screening of many material combinations including corrosion studies, (c) computational modeling and design of the welded interface as well as of the multi-material prototype component, and (d) mechanical testing for strength and durability at coupon scale and to a certain extent the prototype scale. The all-steel engine cradle of 2016 Chevrolet Cruze was chosen as the baseline prototype component. The target set for the project was to demonstrate a 20% weight reduction at a cost premium of less than $\$ $5/lb saved without compromising on baseline mechanical properties. At project completion, a 12% lighter prototype component was demonstrated with an estimated cost premium of $\$ $9.8/lb saved. Besides prototype level demonstration of the technology, this project also enabled elevation of the technology’s readiness level to where a hydraulically actuated welding head was developed and made ready for deployment at a research and development facility for Tier 1 automotive supplier.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Many-Body Perturbation Theory Approach to Energy Band Alignment at the Crystalline Tetracene–Silicon Interface

Hybrid inorganic–organic semiconductor (HIOS) interfaces are of interest for new photovoltaic devices operating above the Shockley–Queisser limit. Predicting energy band alignment at the interfaces is crucial for their design, but represents a challenging problem due to the large scales of the system, the energy precision required and a wide range of physical phenomena that occur at the interface. To tackle this problem, many-body perturbation theory in the non-self-consistent GW approximation, orbital relaxation corrections for organic semiconductors, and line-up potential method for inorganic semiconductors which allows for tractable and accurate computing of energy band alignment in crystalline van-der-Waals hybrid inorganic–organic semiconductor interfaces are used. In this work, crystalline tetracene physisorbed on the clean hydrogen-passivated 1 × 2 reconstructed (100) silicon surface is studied. Using this computational approach, it is found that the energy band alignment is determined by an interplay of the mutual dynamic dielectric screening of two materials and the formation of a dipole layer due to a weak hybridization of atomic/molecular orbitals at the interface. The significant role of the exchange-correlation effects in predicting band offsets for the hybrid inorganic–organic semiconductor interfaces is also emphasized.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

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

On the Sensitivity to Density-Functional Approximations for CO Binding Energies of Single-Atom Catalysts in Nitrogen-Doped Graphene

Density functional theory (DFT) methods are the working horse in screening new catalytic materials. They are widely used to predict trends in binding energies, which are then used to compare the activity of different materials. Here, the binding strength of CO is an important descriptor to the CO 2 reduction catalytic activity of the single transition metal atoms embedded on nitrogen-doped graphene (TM/NG). In this work, however, we show that CO binding strengths in different TM/NG has very different sensitivity to DFT methods. Specifically, Fe/NG CO binding energy changes dramatically with the percentage of exact exchange in the functional; Co/NG does less so, while Ni/NG nearly has no change. Such varying behaviors is a direct result of different local spin configurations, similar to the performance of DFT methods for metal porphyrin complexes. Therefore, caution should be exercised when using DFT binding energies for quantitative predictions in TM/NG single atom catalysis.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Compositional screening of Ce-doped (Gd,Lu,Y) 3 (Al,Ga) 5 O 12 ceramics prepared by quenching from melt and their luminescence properties

Quenching from the melt using an Optical Floating Zone furnace was investigated as a possible high-throughput preparation method in order to screen novel scintillating materials. To validate this method, polycrystalline rare-earth aluminum garnets and yttrium gallium aluminum garnets were synthesized and characterized by X-ray diffraction, photoluminescence and radioluminescence emission spectra as well as radioluminescence imaging and compared to the previously reported properties of those materials. In order to achieve rapid, but quantitative comparison of the materials, pellets fabricated from the synthesized powders were sintered; two different sintering conditions were investigated and compared. Therefore, a simplistic energy deposition and light absorption model for brightness of opaque samples under X-ray irradiation was developed to estimate the relative scintillation efficiency of the materials. Based on the results, Y 3 Al 5 O 12 , LuY 2 Al 5 O 12 and GdY 2 Al 5 O 12 seem to have the highest scintillation efficiency among the prepared samples.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design of High-Performance Lead-Free Quaternary Antiperovskites for Photovoltaics via Ion Type Inversion and Anion Ordering

The emergence of halide double perovskites significantly increases the compositional space for lead-free and air-stable photovoltaic absorbers compared to halide perovskites. Nevertheless, most halide double perovskites exhibit oversized band gaps (>1.9 eV) or dipole-forbidden optical transition, which are unfavorable for efficient single-junction solar cell applications. The current device performance of halide double perovskite is still inferior to that of lead-based halide perovskites, such as CH 3 NH 3 PbI 3 (MAPbI 3 ). Here, by ion type inversion and anion ordering on perovskite lattice sites, two new classes of pnictogen-based quaternary antiperovskites with the formula of X 6 B 2 AA' and X 6 BB'A 2 are designed. Phase stability and tunable band gaps in these quaternary antiperovskites are demonstrated based on first-principles calculations. Further photovoltaic-functionality-directed screening of these materials leads to the discovery of 5 stable compounds (Ca 6 N 2 AsSb, Ca 6 N 2 PSb, Sr 6 N 2 AsSb, Sr 6 N 2 PSb, and Ca 6 NPSb 2 ) with suitable direct band gaps, small carrier effective masses and low exciton binding energies, and dipole-allowed strong optical absorption, which are favorable properties for a photovoltaic absorber material. The calculated theoretical maximum solar cell efficiencies based on these five compounds are all larger than 29%, comparable to or even higher than that of the MAPbI 3 based solar cell. Finally, our work reveals the huge potential of quaternary antiperovskites in the optoelectronic field and provides a new strategy to design lead-free and air-stable perovskite-based photovoltaic absorber materials.

three-dimensional electronic transport↗
DOE OSTI2024

Engineering self-deliverable ribonucleoproteins for genome editing in the brain

The delivery of CRISPR ribonucleoproteins (RNPs) for genome editing in vitro and in vivo has important advantages over other delivery methods, including reduced off-target and immunogenic effects. However, effective delivery of RNPs remains challenging in certain cell types due to low efficiency and cell toxicity. To address these issues, we engineer self-deliverable RNPs that can promote efficient cellular uptake and carry out robust genome editing without the need for helper materials or biomolecules. Screening of cell-penetrating peptides (CPPs) fused to CRISPR-Cas9 protein identifies potent constructs capable of efficient genome editing of neural progenitor cells. Further engineering of these fusion proteins establishes a C-terminal Cas9 fusion with three copies of A22p, a peptide derived from human semaphorin-3a, that exhibits substantially improved editing efficacy compared to other constructs. We find that self-deliverable Cas9 RNPs generate robust genome edits in clinically relevant genes when injected directly into the mouse striatum. Overall, self-deliverable Cas9 proteins provide a facile and effective platform for genome editing in vitro and in vivo.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Screening of hot electrons in the ferroelectric semiconductor In 2 Se 3

We report that in a conventional semiconductor, electrons (e) scatter efficiently with longitudinal-optical phonons (ph LO ) via the Fröhlich interaction, e.g., in polaron formation or hot-electron cooling. Recent studies on lead halide perovskites have suggested that the Fröhlich interaction may be sufficiently screened by the polar lattice. Here we show that hot-electron cooling in a layered ferroelectric semiconductor In 2 Se 3 slows down by 50% as temperature is increased from 115 to 387 K; this decrease is about four times more than that predicted by the Fröhlich model. We suggest that screening of Fröhlich interaction by polar domains or fluctuations in the ferroelectric semiconductor may be responsible for the reduced e-ph LO scattering. Such enhanced screening in polar materials featuring anharmonic lattice instability may also reduce carrier scattering with other charge carriers or with charged defects, leading to improved optoelectronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets

Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.

Choi, Jong Youl↗
DOE OSTI2021

Imaging electric field with electrically neutral particles

It used to think that is impossible to determine/measure electric field inside a physically isolated volume, especially inside an electrically shielded space, because a conventional electric-field sensor can only measure electric field at the location of the sensor, and when an electric-field source is screened by conductive materials, no leakage electric field can be detected. For first time, we experimentally demonstrated that electrically neutral particles, neutrons, can be used to measure/image electric field behind a physical barrier. This work enables a new measurement capability that can visualize electric-relevant properties inside a studied sample or detection target for scientific research and engineering applications.

42 ENGINEERING↗