SEARCH · Engineering Papers
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.
This white paper describes the astrobiological significance of the Enceladus plume, and makes a series of scientific and technological recommendations that would lead to a future mission that samples and analyzes plume materials, and searches for evidence of life.
There are significant gaps in knowledge regarding the water ice on Mars and future preparation for human missions. So far, six rovers have traversed Mars’ rocky surface, aiming to understand the planet’s composition and search for signs of life. A rover mission set to explore the high northern latitudes of Mars with the goal of analyzing ice cores could greatly advance scientific knowledge to better understand Mars’s ancient climate and geochemistry. As of now, little has been accomplished in the exploration of colder regions and the extraction of water ice apart from the Phoenix lander and the Viking 2 mission; however, the science potential of the Phoenix and Viking landers was limited due to their inability to explore terrain like rovers. The exploration of the near-polar region Arcadia Planitia and its water ice deposits could reveal signs of past microbial life through preserved biosignatures. Additionally, to prepare for permanent human habitation on Mars, this proposed rover could continue the characterization of Mars’s current land and mineral composition, and search for materials that future humans may need on Mars (such as water for habitation, basalt for construction, and olivine for manufacturing purposes). This would increase understanding of the potential for in-situ resource utilization (ISRU) on Mars. The use of ISRU will reduce the amount of material needed on the initial flight, and similarly will reduce fuel costs. The mission to Arcadia Planitia incorporates the use of various mineral-extracting and imaging instruments (Martian Ice Core Analyzer, ground-penetrating radar, NavCam, Mars Environmental Dynamics Analyzer, SuperCam, etc.) to collect data from water ice and geologic samples. A near-polar rover mission to Mars would illuminate Mars’ past and present, while learning more about martian resources to benefit human explorers in the future.
The search for room-temperature superconductors is a major challenge in modern physics. The discovery of copper-oxide superconductors in 1986 brought hope but also revealed complex mechanisms that are difficult to analyze and compute. In contrast, the traditional electron–phonon coupling (EPC) mechanism facilitated the practical realization of superconductivity (SC) in metallic hydrogen. Since 2015, the discovery of new hydrogen compounds has shown that EPC can enable room-temperature SC under high pressures, driving extensive research. Advances in computational capabilities, especially exascale computing, now allow for the exploration of millions of materials. This paper reviews newly predicted superconducting systems in 2023–2024, focusing on hydrides, boron–carbon systems, and compounds with nitrogen, carbon, and pure metals. Although many computationally predicted high-T c superconductors were not experimentally confirmed, some low-temperature superconductors were successfully synthesized. This paper provides a review of these developments and future research directions.
Many applications from advanced nuclear reactors to aerospace and automotive industries require materials to operate in extreme environments. In search of new materials that can operate in these extremes, the present work explores this space whereby: (1) guided by atomistic and thermodynamic calculations we utilize thin film combinatorial synthesis to rapidly explore mechanical and thermal properties in a broad range of refractory compositionally complex alloys, and (2) observe transformation induced plasticity via oscillations in the thin film nanoindentation load depth curves that are attributed to, (3) a stress-induced HCP-to-BCC phase transformation in the resulting nanogranular microstructure, which to our knowledge has not been observed before in this alloy system; and finally (4) scale to bulk materials to compare the thin film results.
In certain cases a space-borne optical instrument with a dielectric window requires a transparent conductive coating deposited on the window to remove the electrostatic charge collected due to the bombardment of ionized particles. Semiconductor and metal films are studied for use as transparent conductive coatings for the front window of far ultraviolet camera. Cr is found to be the best coating material. The theoretical search for the semiconductor and metal coating materials and experimental results for ITO and Cr films are reported.
The search for advanced measurement techniques for determining long term compatibility of materials with propellants was conducted in several parts. A comprehensive survey of the existing measurement and testing technology for determining material-propellant interactions was performed. Selections were made from those existing techniques which were determined could meet or be made to meet the requirements. Areas of refinement or changes were recommended for improvement of others. Investigations were also performed to determine the feasibility and advantages of developing and using new techniques to achieve significant improvements over existing ones. The most interesting demonstration was that of the new technique, the volatile metal chelate analysis. Rivaling the neutron activation analysis in terms of sensitivity and specificity, the volatile metal chelate technique was fully demonstrated.
The proposed application of composite materials to transport wing and fuselage structures prompted the search for tougher materials having improved resistance to impact damage and delamination. Several resin/graphite fiber composite materials were subjected to standard damage tolerance tests and the results were compared to ascertain which materials have superior toughness. In addition, test results from various company and NASA laboratories were compared for repeatability.
The search for organic materials on small bodies of the Solar System is conducted spectroscopically from Earth-based telescopes and from spacecraft. Although the carbonaceous meteorites carry a significant inventory of complex organic solids, the sources of these meteorites have not been identified. Infrared spectra of a sample of the suspected sources, the C- and D-class asteroids, including new data from the Spitzer Space Telescope, show signatures of silicates, but none diagnostic of organic compounds. In the absence of discrete spectral features, the low albedos and colors in the visible and near-IR spectral regions are the principal links between the organic-bearing meteorites and the asteroids. While Pluto and a few trans-neptunian objects show spectral signatures of frozen CH4. Solid CH3OH has been identified on two Centaur objects in the outer Solar System. In some cases the red colors of those objects suggest the presence of tholins. The VIMS instrument aboard the Cassini spacecraft in orbit around Saturn has detected near-IR spectral features on at least three of Saturn's satellites that are indicative or suggestive of organic molecules. One entire hemisphere of the satellite Iapetus is covered with low-albedo material that shows a spectral signature of aromatic hydrocarbons (3.3 microns) and the -CH2 stretching mode bands of an aliphatic component. Organics absorbing at 3.44 microns are suspected in the region of the south pole of Enceladus, and also on the surface of Phoebe. Organic material may originate on icy bodies in the current epoch by various processes of energy deposition into native material, or they may fall to the surface from an external (probably cometary) source. Some organic material may be pre-solar, having originated in the interstellar medium before the formation of the Solar System. Using the techniques of remote sensing, its detection and analysis are slow and difficult.
All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.
Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO 2 cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO 2 to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. Here we verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.
The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.
There is growing capability in the field of “Big Data” and “Data Analytics” which Mass Properties Engineers might like to take advantage of. This paper utilizes an implementation of the IBM Knowledge Analytics and Watson search capabilities to explore a corpus of material developed primarily with the interests of Mass Properties Engineers and vehicle concept developers at its forefront. The full collection of SAWE (Society of Allied Weight Engineers, Inc.) Technical Papers from 1939 through 2015 is a major portion of the knowledge content. Additional aerospace vehicle design information includes metadata from AIAA (American Institute for Aeronautics and Astronautics), and INCOSE (International Council on Systems Engineering) as well as author-provided personal search material. This data is processed with respect to certain expected content, data taxonomies and key words to become the core data in NASA Langley Research Center’s “Vehicle Analysis Analytics”, IBM Watson Content. Processed data becomes the corpus of information which is interrogated to provide examples of finding data for mass regression analysis, technology impacts on MPE (Mass Properties Engineering), mass properties control, standards, and other aspects of interest.
Safe and reliable seal materials for high-pressure oxygen systems sometimes appear to be extinct species when sought out by oxygen systems designers. Materials that seal well are easy to find, but these materials are typically incompatible with oxygen, especially in cryogenic liquid form. This incompatibility can result in seals that leak, or much worse, seals that easily ignite and burn during use. Materials that are compatible with oxygen are easy to find, such as the long list of compatible metals, but these metallic materials are limiting as seal materials. A material that seals well and is oxygen compatible has been the big game in the designer's safari. Scientists at the Materials Combustion Research Facility (MCRF), part of NASA/Marshall Space Flight Center (MSFC), are constantly searching for better materials and processes to improve the safety of oxygen systems. One focus of this effort is improving the characteristics of polymers used in the presence of an oxygen enriched environment. Very few systems can be built which contain no polymeric materials; therefore, materials which have good impact resistance, low heat of combustion, high auto-ignition temperature and that maintain good mechanical properties are essential. The scientists and engineers at the Materials Combustion Research Facility, in cooperation with seal suppliers, are currently testing a new formulation of polytetrafluoroethylene (PTFE) with Brass filler. This Brass-filled PTFE is showing great promise as a seal and seat material for high pressure oxygen systems. Early research has demonstrated very encouraging results, which could rank this material as one of the best fluorinated polymers ever tested. This paper will compare the data obtained for Brass-filled PTFE with other fluorinated polymers, such as TFE-Teflon (PTFE) , Kel-F 81, Viton A, Viton A-500, Fluorel , and Algoflon . A similar metal filled fluorinated polymer, Salox-M , was tested in comparison to Brass-filled PTFE to demonstrate the importance of the metal chosen and relative percentage of filler. General conclusions on the oxygen compatibility of this formulation are drawn, with an emphasis on comparing and contrasting the materials performance to the performance of the current state-of-the-art oxygen compatible polymers.
In this work we perform the first high-throughput search and evaluation of materials that can serve as excellent low-mass dark matter detectors. Using properties of close to 1000 materials from the Materials Project database, we project the sensitivity in dark matter parameter space for experiments constructed from each material, including both absorption and scattering processes between dark matter and electrons. Using the anisotropic materials in the dataset, we further compute the level of daily modulation in interaction rates and the resulting directional sensitivities, highlighting materials with prospects to detect the dark matter wind. Our methods provide the basic tools for the data-driven design of dark matter detectors, and our findings lay the groundwork for the next generation of highly optimized direct searches for dark matter as light as the keV scale. This represents a major step in the application of results from condensed matter physics to dark matter search design.
Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.
Optically addressable spin defects have emerged as the leading platforms for quantum sensing and communication in solid-state systems. While traditional efforts have concentrated on a focused set of well-studied defects, recent advances in high-throughput computational methods have shown promise for large-scale exploration of defects across diverse semiconductor hosts. By cataloging key properties of quantum defects in computational databases, high-throughput screening techniques can systematically suggest and design novel candidates. In this article, we highlight recent advances in data-driven quantum defect design aimed at addressing critical materials science challenges such as host materials selection, defect stability, and desirable electronic and optical properties. Here, we emphasize the importance of electronic-structure-guided searches across various materials and illustrate how high-throughput computations contribute to our understanding of design principles for quantum defects. Additionally, we outline ongoing challenges and emerging opportunities in this rapidly developing field.
One of the challenges in exploring promising novel materials for dark matter searches is the detection of sub-eV energy excitations from light-dark matter interaction with a target material. Dark matter interaction can excite sub-eV optical phonon modes in polar materials like sapphire. We plan to utilize superconducting qubits on a sapphire substrate to study their response to energy deposition into the superconducting film and the substrate by irradiating them with photons and particles and also generating phonons in the chip. A fraction of the incident energy is expected to break Cooper pairs in the superconductor, releasing quasiparticles. As the quasiparticles tunnel across the Josephson junction, the charge parity of the qubit changes. The parity switching rate is proportional to the quasiparticle density in the qubit superconductor film and can be measured using standard qubit readout protocol. This presentation describes our ongoing attempt to build such a prototype detector.
The goal of the space flight program at the Center for Commercial Development of Space (CCDS)--Materials for Space Structures is to provide environmentally stable structural materials to support the continued humanization and commercialization of the space frontier. Information on environmental stability will be obtained through space exposure, evaluation, documentation, and subsequent return to the supplier of the candidate material for internal investigation. This program provides engineering and scientific service to space systems development firms and also exposes CCDS development candidate materials to space environments representative of in-flight conditions. The maintenance of a technological edge in space for NASA suggests the immediate search for space materials that maintain their structural integrity and remain environmentally stable. The materials being considered for long-lived space structures are complex, high strength/weight ratio composites. In order for these new candidate materials to qualify for use in space structures, they must undergo strenuous testing to determine their reliability and stability when subjected to the space environment. Ultraviolet radiation, atomic oxygen, debris/micrometeoroids, charged particles radiation, and thermal fatigue all influence the design of space structural materials. The investigation of these environmental interactions is the key purpose of this center. Some of the topics discussed with respect to the above information include: the Space Transportation System, mission planning, spaceborne experiments, and space flight payloads.