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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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Ceramics and ceramic engineering - zirconium oxygen carbon system, solid state ceramics, surface phenomena, radiation effects, and processing
Effects of various energy environments on zirconium-oxygen-carbon system, solid state ceramics, surface properties, and ceramic materials processing
Radiation damage is an inherently multi-scale phenomenon, with much of the key material evolution occurring at the mesoscale. Current techniques for the direct interrogation of material property changes due to irradiation either function with very small volumes, such as in-situ TEM, cannot cover the full range of defect populations produced, such as in positron annihilation spectroscopy, or requires destruction of the test material, such as in standard or micromechanical testing. In addition, the ultimate quantities of interest to radiation materials scientists are the changes in key material properties with irradiation. These include stiffness, ductility, and thermal conductivity for structural materials, and optical properties such as index of refraction or changes in transmittance spectra for optically active materials. The latter of these are often correlated directly to stress- or thermally-induced changes, themselves a function of the specific defect populations responsible for their change. The rapid study of such a mesoscale phenomenon requires an inherently mesoscale technique, where multiple quantities of interest can be accurately measured in very short times. Determining the sensitivity of any technique with known certainly is of critical importance to qualifying a new technique to measure in-situ material property changes during irradiation. In this paper, we present the new applicability of an existing technique, known as transient grating (TG) spectroscopy, to rapidly and accurately study key problems in radiation materials science. Results quantifying the absolute sensitivity of the technique are presented, alongside molecular dynamics (MD) simulations of the TG spectroscopy process to gauge direct agreement between theory, simulation, and experiment. An example problem of interest is demonstrated as an illustration of what is possible, showing elastic and thermal changes in irradiated silicon carbide (SiC) films. Finally, future work to quantify the effects of experimentally-simulated radiation defects is proposed, in support of building a database to use TG spectroscopy to deconvolve irradiated material property changes in-situ in terms of the defect populations responsible for their evolution. The application of the TG spectroscopy technique to in-situ radiation materials science has the potential to answer long-standing questions in the field, relating to ion beam rastering, the onset of void swelling, and direct measurement of radiation-created point defect concentrations. Before it can do so, the technique must carefully be analyzed and tuned to increase its sensitivity, the certainty of its results, and the ability to predict radiation-induced changes by simulations must be developed. Making such measurements in-situ with the aid of predictive MD simulations has the potential to elucidate mechanisms of radiation damage accumulation, as well as more rapidly qualify radiation-resistant alloys for service in nuclear systems. Work is underway to build this database of experimentally-simulated radiation-produced defects, which will be combined and compared with actual irradiated materials for eventual in-situ deconvolution of the TG signal into the defect populations responsible for irradiation-induced microstructural change. (authors)
The accurate measurement of density in both liquid and solid samples is of considerable interest to Principal Investigators with materials science experiments slated for the ISS. The work to be described is an innovative application of a conventional industrial nondestructive evaluation instrument. Traditional applications of industrial computed tomography (CT) rely on reconstructing cross sections of large structures to provide two-dimensional planar views which can identify defects such as porosity, or other material anomalies. This has been done on microgravity materials science experiments to check the integrity of ampoule-cartridge assemblies for safety purposes. With a substantially monoenergetic flux, as can be obtained with a radioactive cobalt source, there will be a direct correlation between absorption and density. Under such conditions it then becomes possible to make accurate measurements of density throughout a sample, and even when the sample itself is enclosed within a furnace and a safety required cartridge. Such a system has been installed at Kennedy Space Center (KSC) and is available to PIs to examine samples before and after flight. The CT system is being used to provide density information for two purposes. Firstly, the determination of density changes from liquid to solid is vital information to the PI for purposes of modeling the solidification behavior of his sample, and to engineers who have to design containment ampoules and must allow for shrinkage and other volume changes that may occur during processing. While such information can be obtained by pycnometric measurements, the possibility of using a furnace installed on the CT system enables one to examine potentially dangerous materials having high vapor pressures, while not needing visible access to the material. In addition, uniform temperature can readily be obtained, and the system can be controlled to ramp up, hold, and ramp down while collecting data over a wide range of parameters automatically. Results of initial tests on low melting point elements such as gallium, indium and tin will be presented, and the intent is to proceed to compounds such as InSb, HgCdTe and CdTe. Alloys such as Pb-Sb (PI - Poirier, U AZ) and Cu-Al (PI - Trivedi, Ames Lab.), which are the subjects of flight experiments, will also be examined. The second application is the conversion of measured density values directly to composition. This was successfully done with the mercury cadmium telluride alloys grown on the second and fourth United States Microgravity Payload (USMP-2 and USMP-4) missions by Lehoczky. CdTe values along the length of the boules were obtained at KSC prior to cutting the sample, and could have been obtained prior to its removal from the cartridge and ampoule. Examples of the data obtained will be shown. It is anticipated that several of the materials science PIs will avail themselves of the technique described, initially for determining densities prior to flight, and then to acquire early quantitative data on the compositional variation within their samples.
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.
A materials science experiment has been developed and readied for operation aboard the International Space Station (ISS). Components of this experiment are onboard ISS and area awaiting the flight of science samples. The goal of the experiment is to understand the dynamics of Ostwald ripening, also known as coarsening, a process that occurs in nearly any two-phase mixture found in nature. Attempts to obtain experimental data in ground-based laboratories are hindered due to the presence of gravity, which introduces material transport modes other than that of the coarsening phenomenon. This introduces adjustable parameters in the formulation of theory. The original Coarsening in Solid-Liquid Mixtures (CSLM) mission, which flew on the Space Shuttle in 1997, produced data from a coarsened eutectic alloy. Unfortunately, both the science matrix and the hardware, while nominally functional, did not account adequately for operations in microgravity. A significantly redesigned follow-on experiment, CSLM-2 has been developed to redress the inadequacies of the original experiment. This paper reviews the CSLM-2 project: its history, science goals, flight hardware implementation, and planned operations and analysis
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.
Helium ion microscopy (HIM), enabled by a gas field ion source (GFIS), is an emerging imaging and nanofabrication technique compatible with many applications in materials science. The scanning electron microscope (SEM) has become ubiquitous in materials science for high-resolution imaging of materials. However, due to the fundamental limitation in focusing of electron beams, ion-beam microscopy is now being developed (e.g., at 20 kV the SEM beam diameter ranges from 0.5 to 1 nm, whereas the HIM offers 0.35 nm). The key technological advantage of the HIM is in its multipurpose design that excels in a variety of disciplines. The HIM offers higher resolution than the best available SEMs as well as the traditional gallium liquid-metal ion source (LMISs) focused ion beams (FIBs), and is capable of imaging untreated biological or other insulating samples with unprecedented resolution, depth of field, materials contrast, and image quality. GFIS FIBs also offer a direct path to defect engineering via ion implantation, three-dimensional direct write using gaseous and liquid precursors, and chemical-imaging options with secondary ion mass spectrometry. HIM covers a wide range of tasks that otherwise would require multiple tools or specialized sample preparation. In this paper, we describe the underlying technology, present materials, relevant applications, and offer an outlook for the potential of FIB technology in processing materials.
Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
Facilitating the application of machine learning to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materialsspecific machine learning models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with machine learning models and how users can access those capabilities through web and programmatic interfaces.
Article to be sent to Advanced Materials and Processes, journal of ASM International, as attached. This is a news-type technical journal for a large organization of scientists, engineers, salesmen, and managers. The article is quite general, meant to be an introduction to the properties of nanotubes. This is a materials science organization, therefore the article is geared toward using nanotubes for materials uses. Pictures have not been included in this version.
Microgravity materials processing experiments provide an opportunity to perform scientific research in an environment which allows one to observe various phenomena without the masking effects of gravity-driven convective flows, buoyancy, or contaminating influences of walled containers. Even for the most experienced scientists, it is still difficult to predict beforehand, whether or not microgravity experimentation can be successfully performed in space and achieve solutions to problems which are not attainable in 1 g. Consequently, experimentation in ground based facilities which are capable of simulating, in somewhat lesser time frames and to a lesser degree of microgravity, provides a unique low-cost approach to determine the feasibility of continuing research in a particular experiment. The utilization of these facilities in developing the full requirements for a space experiment does present a very cost-effective approach to microgravity experimentation. The Drop Tube Facility at Marshall Space Flight Center (MSFC) provides an excellent test bed for containerless processing experiments such as described here. These facilities have demonstrated for a number of years the capability to develop insight into space experiments involving containerless processing, rapid solidification, and wetting phenomena through the use of lower-cost ground facilities. Once sufficient data has been obtained, then a space-based experiment can be better defined.
The microgravity materials program was nearly eliminated in the middle of the aughts due to budget constraints. Hardware developments were eliminated. Some investigators with experiments that could be performed using ISS partner hardware received continued funding. Partnerships were established between US investigators and ESA science teams for several investigations. ESA conducted peer reviews on the proposals of various science teams as part of an ESA AO process. Assuming he or she was part of a science team that was selected by the ESA process, a US investigator would submit a proposal to NASA for grant funding to support their part of the science team effort. In a similar manner, a US materials investigator (Dr. Rohit Trivedi) is working as a part of a CNES selected science team. As funding began to increase another seven materials investigators were selected in 2010 through an NRA mechanism to perform research related to development of Materials Science Research Rack investigations. One of these has since been converted to a Glovebox investigation.
On January 14, 2004, the President of the United States announced a new vision for the United States civil space program. The Administrator of the National Aeronautics and Space Administration (NASA) has the responsibility to implement this new vision. The President also created a Presidential Commission 'to obtain recommendations concerning implementation of the new vision for space exploration.' The President's Commission recognized that achieving the exploration objectives would require significant technical innovation, research, and development in focal areas defined as 'enabling technologies.' Among the 17 enabling technologies identified for initial focus were advanced structures; advanced power and propulsion; closed-loop life support and habitability; extravehicular activity system; autonomous systems and robotics; scientific data collection and analysis; biomedical risk mitigation; and planetary in situ resource utilization. The Commission also recommended realignment of NASA Headquarters organizations to support the vision for space exploration. NASA has aggressively responded in its planning to support the vision for space exploration and with the current considerations of the findings and recommendations from the Presidential Commission. This presentation will examine the transformation and realignment activities to support the vision for space exploration that are underway in the microgravity materials science program. The heritage of the microgravity materials science program, in the context of residence within the organizational structure of the Office of Biological and Physical Research, and thematic and sub-discipline based research content areas, will be briefly examined as the starting point for the ongoing transformation. Overviews of future research directions will be presented and the status of organizational restructuring at NASA Headquarters, with respect to influences on the microgravity materials science program, will be discussed. Additional information is included in the original extended abstract.
Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.
Project MAUS is a part of the German material sciences program and provides autonomous payloads for the Space Shuttle. These payloads are housed in canisters which are identical with those of NASA's Get-Away-Special program. The main components of the hardware are: a standard system consisting of power supply, experiment control, data acquisition and the experiment modules containing experiment specific hardware. So far, three MAUS modules with experiments from the area of material sciences have been flown as GAS payloads.
Materials are generally classified by a phase diagram which displays their properties as a function of external state variables, typically temperature and pressure. A new dimension that is relatively unexplored is time: a rich variety of new materials can become accessible in the transient period following laser excitation from the ground state. The timescale of nanoseconds to femtoseconds, is ripe for investigation using x-ray free-electron laser (XFEL) methods. There is no shortage of materials suitable for time-resolved materials-science exploration. Oxides alone represent most of the minerals making up the Earth's crust, catalysts, ferroelectrics, corrosion products and electronically ordered materials such as superconductors, to name a few. Some of the elements have metastable phase diagrams with predicted new phases. There are some examples known already: an oxide 'hidden phase' living only nanoseconds and an electronically ordered excited phase of fullerene C 60 , lasting only femtoseconds. In a completely general way, optically excited states of materials can be probed with Bragg coherent diffraction imaging, both below the damage threshold and in the destructive regime. Lastly, prospective methods for carrying out such XFEL experiments are discussed.
Changes to the LANL / BNL photocathode programs are a strategic response to evolving needs and opportunities within NNSA. The Materials by Design strategy engages the broader material science communities at both LANL and BNL. Progress has been non-linear with time and investment. Techniques honed for one application can be rapidly applied to many others. For example, Cs 2 Te following successes of smooth K 2 CsSb. We enter a cycle of using accelerator-based tools to improve accelerators. Providers of advanced tools of material science (accelerator based x-ray light sources) are now availing themselves of the same capabilities to improve the next-generation machines. We are looking to define future collaborations.