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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Thermochemistry of CaO-MgO-Al2O3-SiO2 (CMAS) and Advanced Thermal and Environmental Barrier Coating Systems

CaO-MgO-Al2O3-SiO2 (CMAS) oxides are constituents in a broad number of materials and minerals which have recently inferred to discussions in materials science, planetary science, geochemistry and cosmochemistry communities. In materials science, there is increasing interest in the degradation studies of thermal (TBC) and environmental (EBC) barrier coatings of gas turbines by molten CMAS. These coatings have been explored to be applied on silicon-based ceramics and composites which are lighter and more temperature capable hot-section materials of gas turbines than the current Ni-based superalloys. The degradation of the coatings occurs when CMAS minerals carried by the intake air into gas turbines, e.g. in aircraft engines, reacts at high temperatures (1000C) with the coating materials. This causes premature failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb and Gd) oxide stabilized ZrO2 systems, and stability of the resulting oxides and silicates.

CaO-MgO-Al2O3-SiO2 (CMAS)↗
NASA NTRS2017

Thermochemistry of CaO-MgO-Al2O3-SiO2 (CMAS) and Advanced Thermal and Environmental Barrier Coating Systems

CaO-MgO-Al2O3-SiO2 (CMAS) oxides are constituents in a broad number of materials and minerals which have recently inferred to discussions in materials science, planetary science, geochemistry and cosmochemistry communities. In materials science, there is increasing interest in the degradation studies of thermal (TBC) and environmental (EBC) barrier coatings of gas turbines by molten CMAS. CMAS minerals usually are carried by the intake air into gas turbines, e.g. in aircraft engines, and their deposits react at high temperatures (1000C) with the coating materials. This causes degradation and accelerated failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb, Dy, Gd, Nd and Sm) oxide stabilized ZrO2 or HfO2 systems, and the stability of the resulting oxides and silicates. Plasma sprayed hollow tube samples ( 2.2 mm and 26 mm height) were half filled with CMAS powder, wrapped and sealed with platinum foil, and heat treated at 1310 C for 5h. Samples were characterized by differential scanning calorimetry, X-ray diffraction and cross section electron microscopy analysis.

degradation↗
DOE OSTI2024

Modeling graphene sheet growth and dynamical matrix calculations using molecular dynamics

Molecular dynamics (MD) has been an incredibly useful tool to model physical processes that were synthesized experimentally but not fully understood. MD, through the use of semi-empirical inter-atomic potentials, has allowed understanding of different physical processes in materials science. Yet as well as providing useful insights into materials science, molecular dynamics has a wider range of usability. In this report, I will be detailing how MD can be used to study graphene formation from a carbon liquid which requires high temperatures and pressures. Beyond this, I will describe the usefulness of MD for understanding the physics for phonon transport quantum sensors. To do this, MD was employed to determine the dynamical matrix by treating atoms as coupled oscillators. An accurate understanding of the dynamical matrix of a system is required to calculate the non-equilibrium Green’s function used to describe the phonon transport within phonon wave-guides. I found that, across multiple pressures and temperatures, randomly placed carbon atoms will show evidence of pent-first formation with semi-empirical models. Density functional theory (DFT), on the other hand, was too computationally expensive to use for full scale MD simulations, but we have the possibility of training a machine learned interatomic potential to approximate DFT for carbon in the environments being studied for pent-first graphene sheet formation.

36 MATERIALS SCIENCE↗
NASA NTRS1990

The microgravity environment of the D1 mission

Some characteristic features and results of D1 microgravity measurements are discussed as performed in the Material Science Double Rack (MSDR) and the Materials Science Double Rack for Experiment Modules and Apparatus (MEDEA). Starting with a brief review of the main potential disturbances, the payload aspects of interest to the analysis and the accelerometer measuring systems are described. The microgravity data are analyzed with respect to selected mission events such as thruster firings for attitude control, operations of Spacelab experiment facilities, vestibular experiments and crew activities. The origins are divided into orbit, vehicle, and experiment induced perturbations. It has been found that the microgravity-environment is dictated mainly by payload-induced perturbations. To reduce the microgravity-level, the design of some experiment facilities has to be improved by minimizing the number of moving parts, decoupling of disturbing units from experiment facilities, by taking damping measures, etc. In addition, strongly disturbing experiments and very sensitive investigations should be performed in separate mission phases.

Hamacher, H.↗
NASA NTRS1990

Some considerations for various positioning systems and their science capabilities

Containerless processing of materials at elevated temperatures is discussed with emphasis on high temperature chemistry, thermophysical properties, materials science, and materials processing. Acoustic and electromagnetic positioning of high temperature melts are discussed. Results from recent ground based experiments, including KC-135 testing of an acoustic levitator, are presented. Some current positioning technologies and the potential for enhancing them are considered. Further, a summary of these technologies and their science capabilities for the development of future experiments is given.

Rey, Charles A.↗
NASA NTRS2012

Contribution to "AIAA Aerospace Year in Review" article

The NASA Marshall Space Flight Center Microgravity Science Program is dedicated to promoting our understanding of materials processing by conducting relevant experiments in the microgravity environment and supporting related modeling efforts with the intent of improving ground-based practices. Currently funded investigations include research on dopant distribution and defect formation in semiconductors, microstructural development and transitions in dendritic casting alloys, coarsening phenomena, competition between thermal and kinetic phase formation, and the formation of glassy vs. crystalline material. NASA Microgravity Materials Science Principle Investigators are selected for funding either through a proposal in response to a NASA Research Announcement or by collaborating on a team that has successfully proposed to a foreign space agency research announcement. In the latter case, a US investigator can then apply to NASA for funding through an unsolicited proposal. The International Space Station (ISS) facilities used for the experimental investigations are provided primarily by partnering with foreign agencies and often US investigators are working as a part of a larger team studying a specific area of materials science. Facilities for conducting experiments aboard the ISS include the European Space Agency (ESA) Low Gradient Facility (LGF) and the Solidification and Quench (SQF) modular inserts to the Materials Research Rack/Materials Science Laboratory and are primarily used for controlled solidification studies. The French Space Agency (CNES) provided DECLIC facility allows direct observation of morphological development in transparent materials that solidify analogously to metals. The ESA provided Electro ]Magnetic Levitator (EML) is designed to levitate, melt and then cool samples in order to determine material properties, study nucleation behavior, and document phase transitions. Finally, the Microgravity Science Glovebox (MSG) serves as a onboard facility for supporting the hardware required to conduct a number of smaller, short-term investigations.

Grugel, Richard N.↗
DOE OSTI2024

Beyond Human Vision: Exploring Materials with Machine Intelligence

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

artificial intelligence↗
NASA NTRS2000

Quench Module Insert (QMI) and the Diffusion Module Insert (DMI) Furnace Development

The Quench Module Insert (QMI) and the Diffusion Module Insert (DMI) are microgravity furnaces under development at Marshall Space Flight Center. The furnaces are being developed for the first Materials Science Research Rack (MSRR-1) of the Materials Science Research Facility (MSRF), one of the first International Space Station (ISS) scientific payloads. QMI is a Bridgman furnace with quench capability for studying interface behavior during directional solidification of metallic and alloy materials. DMI will be a Bridgman-Stockbarger furnace to study diffusion processes in semiconductors. The design for each insert, both QMI and DMI, is driven by specific science, operations and safety requirements, as well as by constraints arising from resource limitations, such as volume, mass and power. Preliminary QMI analysis and testing indicates that the design meets these requirements.

Crouch, Myscha R.↗
NASA NTRS1999

An Overview of the MSFC Electrostatic Levitation Facility

Containerless processing represents an important area of research in microgravity materials science. This method provides access to the metastable state of an undercooled melt. Containerless processing provides a high-purity environment for the study of reactive, high-temperature materials. Reduced gravity affords several benefits for containerless processing, for example greatly reduced positioning forces are required and therefore samples of greater mass can be studied. Additionally in reduced gravity, larger specimens will maintain spherical shape which will facilitate modeling efforts. Space Systems/LORAL developed an Electrostatic Containerless Processing System (ESCAPES) as a materials science research tool for investigations of refractory solids and melts. ESCAPES is designed for the investigation of thermophysical properties, phase equilibria, metastable phase formation, undercooling and nucleation, time-temperature- transformation diagrams and other aspects of materials processing. These capabilities are critical to the research programs of several Principal Investigators supported by the Microgravity Materials Science Program of NASA.

Rogers, J. R.↗
DOE OSTI2024

Comparative Study of Large Language Model Architectures on Frontier

Large language models (LLMs) have garnered significant attention in both the AI community and beyond. Among these, the Generative Pre-trained Transformer (GPT) has emerged as the dominant architecture, spawning numerous variants. However, these variants have undergone pre-training under diverse conditions, including variations in input data, data preprocessing, and training methodologies, resulting in a lack of controlled comparative studies. Here we meticulously examine two prominent open-sourced GPT architectures, GPT-NeoX and LLaMA, leveraging the computational power of Frontier, the world’s first Exascale supercomputer. Employing the same materials science text corpus and a comprehensive end-to-end pipeline, we conduct a comparative analysis of their training and downstream performance. Our efforts culminate in achieving state-of-the-art performance on a challenging materials science benchmark. Furthermore, we investigate the computation and energy efficiency, and propose a computationally efficient method for architecture design. To our knowledge, these pre-trained models represent the largest available for materials science. Our findings provide practical guidance for building LLMs on HPC platforms.

Yin, Junqi↗
NASA NTRS2000

The Electrostatic Levitation Facility at NASA's Marshall Space Flight Center

Containerless processing is an important area of research in materials science. Electrostatic levitation (ESL) represents an emerging technology which permits containerless processing in a vacuum environment. NASA's Marshall Space Flight Center (MSFC) established a levitation facility to provide a critical resource to the microgravity materials science research community to continue and enhance ground-based research in the support of the development of flight experiments during the transition to Space Station. During ESL processing, charged specimens are levitated in the electrostatic field produced by the system's electrodes. Three sets of positioning electrodes represent the heart of the MSFC system. Two dual-axis position sensitive detectors provide input for the PID control-loop computer. Sample position is maintained by adjusting the control voltages for the power supplies of the positioning electrodes. A UV source refreshes the charge on specimens during processing via the photoelectric effect. Lasers permit sample heating independent of positioning. The processing chamber typically operates under vacuum condition approximately = 10(exp -7) Torr. Electrostatic levitation provides a materials science research tool for investigations of refractory solids and melts. Topics of investigation include thermophysical properties, phase equilibria, metastable phase formation, undercooling and nucleation, time-temperature-transformation diagrams and other aspects of materials processing. Current capabilities and recent results of processing studies for metals, alloys and oxides will be reviewed.

Rogers, Jan R.↗
NASA NTRS2002

QMI: Rising to the Space Station Design Challenge

The Quench Module Insert (QMI) materials processing furnace is being designed to operate for 8000 hours over four years on the International Space Station (ISS) as part of the first Materials Science Research Rack (MSRR-1) of the Materials Science Research Facility (MSRF). The Bridgman-type furnace is being built for the directional solidification processing of metals and alloys in the microgravity environment of space. Most notably it will be used for processing aluminum and related alloys. Designing for the space station environment presents intriguing design challenges in the form of a ten-year life requirement coupled with both limited opportunities for maintenance and resource constraints in the form of limited power and space. The long life requirement has driven the design of several features in the furnace, including the design of the heater core, the selection and placement of the thermocouples, overall performance monitoring, and the design of the chill block. The power and space limitations have been addressed through a compact furnace design using efficient vacuum insulation. Details on these design features, as well as development test performance results to date, are presented.

Carswell, W. E.↗
NASA NTRS1965

Research on fiber-reinforced composites

Research in materials science - high temperature structural materials, wetting and adherence of nickel alloys to sapphire, boron carbide filaments, and ceramic and graphite fibers

MATERIALS SCIENCE↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning approaches for intentional materials engineering

In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
NASA NTRS1988

Electronic materials high-T(sub c) superconductivity polymers and composites structural materials surface science and catalysts industry participation

The fifth year of the Center for Advanced Materials was marked primarily by the significant scientific accomplishments of the research programs. The Electronics Materials program continued its work on the growth and characterization of gallium arsenide crystals, and the development of theories to understand the nature and distribution of defects in the crystals. The High Tc Superconductivity Program continued to make significant contributions to the field in theoretical and experimental work on both bulk materials and thin films and devices. The Ceramic Processing group developed a new technique for cladding YBCO superconductors for high current applications in work with the Electric Power Research Institute. The Polymers and Composites program published a number of important studies involving atomistic simulations of polymer surfaces with excellent correlations to experimental results. The new Enzymatic Synthesis of Materials project produced its first fluorinated polymers and successfully began engineering enzymes designed for materials synthesis. The structural Materials Program continued work on novel alloys, development of processing methods for advanced ceramics, and characterization of mechanical properties of these materials, including the newly documented characterization of cyclic fatigue crack propagation behavior in toughened ceramics. Finally, the Surface Science and Catalysis program made significant contributions to the understanding of microporous catalysts and the nature of surface structures and interface compounds.

Source record↗
NASA NTRSDate not supplied

PPW 2022 Short Course Introduction to Aerocapture, Entry, Descent, and Landing: Computational Materials

Computational materials science encompasses modeling and simulation techniques that are used to improve our understanding of materials from the atomic to macroscopic scales. This talk will focus on recent applications of computational materials science to ablative thermal protection materials, including heritage PICA and state-of-the-art woven materials like HEEET. Development of advanced molecular, microscopic, and mesoscopic models will be discussed.

phenolic↗