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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials

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.

Sayed, Shehrin↗
DOE OSTI2026

Finding the perfect imperfection: Accelerated, computationally driven discovery and design of quantum defects

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.

Xiong, Yihuang [Dartmouth College, Hanover, NH (Un↗
DOE OSTI2024

Qubit-Based Detector For Dark Matter Search And Radiation Studies

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.

Khatiwada, Rakshya↗
NASA NTRS1991

The transition of ground-based space environmental effects testing to the space environment

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.

Zaat, Stephen V.↗
DOE OSTI2024

A new hybrid gadolinium nanoparticles-loaded polymeric material for neutron detection in rare event searches

Experiments aimed at direct searches for WIMP dark matter require highly effective reduction of backgrounds and control of any residual radioactive contamination. In particular, neutrons interacting with atomic nuclei represent an important class of backgrounds due to the expected similarity of a WIMP-nucleon interaction, so that such experiments often feature a dedicated neutron detector surrounding the active target volume. In the context of the development of DarkSide-20k detector at INFN Gran Sasso National Laboratory (LNGS), several R&D projects were conceived and developed for the creation of a new hybrid material rich in both hydrogen and gadolinium nuclei to be employed as an essential element of the neutron detector. Thanks to its very high cross-section for neutron capture, gadolinium is one of the most widely used elements in neutron detectors, while the hydrogen-rich material is instrumental in efficiently moderating the neutrons. In this paper results from one of the R&Ds are presented. In this effort the new hybrid material was obtained as a poly(methyl methacrylate) (PMMA) matrix, loaded with gadolinium oxide in the form of nanoparticles. We describe its realization, including all phases of design, purification, construction, characterization, and determination of mechanical properties of the new material.

Dark Matter detectors (WIMPs, axions, etc.)↗
NASA NTRS2004

Thermally Stable Heterocyclic Imines as New Potential Nonlinear Optical Materials

In the course of a search for new thermostable acentric nonlinear optical crystalline materials, several heterocyclic imine derivatives were designed, with the general structure D-pi-A(D'). Introduction of a donor amino group (D') into the acceptor moiety was expected to bring H-bonds into their crystal structures, and so to elevate their melting points and assist in an acentric molecular packing. Six heterocycle-containing compounds of this type were prepared, single crystals were grown for five of them, and these crystals were characterized by X-ray analysis. A significant melting temperature elevation was found for all of the synthesized compounds. Three of the compounds were also found to crystallize in acentric space groups. One of the acentric compounds is built as a three-dimensional H-bonded molecular network. In the other two compounds, with very similar molecular structure, the molecules form one-dimensional H-bonded head-to-head associates (chains). These chains are parallel in two different crystallographic directions and form very unusual interpenetrating chain patterns in an acentric crystal. Two of the compounds crystallized with centrosymmetric molecular packing.

Nesterov, Volodymyr V.↗
DOE OSTI2025

Charge Density Wave and Ferromagnetism in Intercalated CrSBr

In materials with 1D electronic bands, electron–electron interactions can produce intriguing quantum phenomena, including spin-charge separation and charge density waves (CDW). Most of these systems, however, are non-magnetic, motivating a search for anisotropic materials where the coupling of charge and spin may affect emergent quantum states. Here, in this study, chemical intercalation of the van der Waals magnetic semiconductor CrSBr yields Li 0.17(2) (tetrahydrofuran) 0.26(3) CrSBr, which possesses an electronically driven quasi-1D CDW with an onset temperature above room temperature. Concurrently, electron doping increases the magnetic ordering temperature from 132 to 200 K and switches its interlayer magnetic coupling from antiferromagnetic to ferromagnetic. The spin-polarized nature of the anisotropic bands that give rise to this CDW enforces an intrinsic coupling of charge and spin. The coexistence and interplay of ferromagnetism and charge modulation in this exfoliatable material provide a promising platform for studying tunable quantum phenomena across a range of temperatures and thicknesses.

2D magnets↗
DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗
DOE OSTI2025

Advanced Modeling and Process-Materials Co-Optimization Strategies for Swing Adsorption Based Gas Separations

This project devised a computational framework for simultaneously co-optimizing pressure swing adsorption process designs along with the sorbent materials (specifically, metal-organic frameworks) to be employed in the associated packed bed columns. The materials optimization aspect involved search over a design space that can describe the material’s molecular structure, while the process optimization aspect considered various process degrees of freedom for steps arising in various cycle configurations. This framework was demonstrated on the separation of nitrogen and carbon dioxide, which arises ubiquitously in a multitude of post-combustion carbon capture and “blue” hydrogen production applications. Our results led to metal-organic framework molecular descriptor choices that are predicted to outperform standard structures used in practice, providing guidance for future metal-organic framework synthesis efforts.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1981

Geological remote sensing: Identification and mapping of rock types for non-renewable resources

Efforts concentrated on developing a technique for relating laboratory spectral reflectance curves of known rocks and vegetation on LANDSAT multispectral images. The techniques involves determination of the laboratory spectral signature of a material of interest and searching a stack of spatially registered multispectral images for materials with the desired spectral signature. Changes in spectral reflectance caused by vegetation cover were also investigated in surface samples from Hawaii.

Adams, J. B.↗
NASA NTRS1998

Mars Molecular and Isotopic Analysis Research Study

Recently, the Martian atmosphere and surface constituents have become of great interest. The Viking in situ gas chromatograph mass spectrometer experiment contributed greatly to our knowledge of the composition of the Martian atmosphere. However, important questions remain such as the abundance of water on Mars. The Viking experiment employed solid reagents to enhance their carbon measurements. Techniques of chemical conversion using simple solid reagents have advanced considerably in the past 20 years. In this investigation we researched the advancements in techniques to reversibly adsorb and desorb water and focused on the techniques potentially useful for the temperatures and pressures on the Martian surface. During the granting period from June 15, 1998 to August 14, 1998, a literature study of the material appropriate for use in a chemical conversion device and the availability of these materials were undertaken. The focus of this investigation was searching for methods and materials potentially useful in enhancing the measurements of water. Three different methods were considered for the means to extract water from a given gas sample. These methods included adsorption in a desiccant, adsorption on a clean metal surface, and adsorption in a carbon molecular sieve or zeolite. Each method was evaluated with feasibility and reversibility in mind. By far the simplest and perhaps cheapest way to remove water from a gaseous sample is by means of a bulk desiccant. Desiccants are commercially available from many companies including those that supply chemicals. The main feature of a desiccant is its ability to rapidly bind or absorb water from the atmosphere. Calcium chloride, for example, is frequently incorporated into drying tubes by organic chemists when reactions require the absence of water. Other desiccants include sodium hydroxide, calcium hydride, and commercial products such as Drierite, available from Aldrich Chemical. The disadvantage to most desiccants is a direct consequence of what makes them so advantageous. Desiccants rapidly and strongly bind water, often reacting chemically with it. This strong binding makes reversibly desorbing the water from the desiccant very difficult. The third method by which to absorb water from a gas sample involves the use of molecular sieves of other ziolites.

Manning, Heidi L. K.↗
NASA NTRS1993

Establishment of computerized numerical databases on thermophysical and other properties of molten as well as solid materials and data evaluation and validation for generating recommended reliable reference data

The Center for Information and Numerical Data Analysis and Synthesis, (CINDAS), measures and maintains databases on thermophysical, thermoradiative, mechanical, optical, electronic, ablation, and physical properties of materials. Emphasis is on aerospace structural materials especially composites and on infrared detector/sensor materials. Within CINDAS, the Department of Defense sponsors at Purdue several centers: the High Temperature Material Information Analysis Center (HTMIAC), the Ceramics Information Analysis Center (CIAC) and the Metals Information Analysis Center (MIAC). The responsibilities of CINDAS are extremely broad encompassing basic and applied research, measurement of the properties of thin wires and thin foils as well as bulk materials, acquisition and search of world-wide literature, critical evaluation of data, generation of estimated values to fill data voids, investigation of constitutive, structural, processing, environmental, and rapid heating and loading effects, and dissemination of data. Liquids, gases, molten materials and solids are all considered. The responsibility of maintaining widely used databases includes data evaluation, analysis, correlation, and synthesis. Material property data recorded on the literature are often conflicting, diverging, and subject to large uncertainties. It is admittedly difficult to accurately measure materials properties. Systematic and random errors both enter. Some errors result from lack of characterization of the material itself (impurity effects). In some cases assumed boundary conditions corresponding to a theoretical model are not obtained in the experiments. Stray heat flows and losses must be accounted for. Some experimental methods are inappropriate and in other cases appropriate methods are carried out with poor technique. Conflicts in data may be resolved by curve fitting of the data to theoretical or empirical models or correlation in terms of various affecting parameters. Reasons (e.g. phase transitions) must be found for unusual dependence or any anomaly. Such critical evaluation involves knowledge of theory, experience in measurement, familiarity with metallurgy (microstructural behavior) and not inconsiderable judgment. An examination of typical data compiled and analyzed by CINDAS shows that the thermal conductivity of a material reported in the literature may vary by a factor of two of more; the range of reported values increases as temperature increases reflecting the difficulty of high temperature measurements. Often only estimates of melt behavior are available, despite the importance of melt properties in modeling, welding, or other solidification processes. There may be only a few measurements available for properties such as kinematic viscosity, even for widely used materials such as stainless steel. In the face of such a paucity of existing data and in a national environment where too few new data are being generated it is nonetheless the responsibility of CINDAS to select and disseminate recommended values of a wide variety of thermophysical properties.

Ho, C. Y.↗
NASA NTRS1988

Primitive material surviving in chondrites - Matrix

A logical place to search for surviving pristine nebular material is in the fine-grained matrices of ordinary and carbonaceous chondrites of petrographic type 3. Unfortunately, many of these chondrites have experienced brecciation, thermal metamorphism, and aqueous alteration, so that interpreting individual features in terms of specific nebular conditions and/or processes is difficult. It follows that the origin and evolutionary history of such matrix phases are controversial, and a consensus is difficult to define. In this chapter, therefore, after summarizing the salient mineralogical, petrographic, chemical, and isotopic features of matrix in apparently primitive chondrites, an attempt is made to provide an overview both of areas of agreement and of topics that are currently in dispute.

Scott, E. R. D.↗
DOE OSTI2024

In Search of Optimum Fresh-Cut Raw Material: Using Computer Vision Systems as a Sensory Screening Tool for Browning-Resistant Romaine Lettuce Accessions

The popularity of ready-to-eat (RTE) salads has prompted novel technology to prolong the shelf life of their ingredients. Fresh-cut romaine lettuce is widely used in RTE salads; however, its tendency to quickly discolor continues to be a challenge for the industry. Selecting the ideal lettuce accessions for use in RTE salads is essential to ensure maximum shelf life, and it is critical to have a practical way to assess and compare the quality of multiple lettuce accessions that are being considered for use in fresh-cut applications. Thus, in this work we aimed to determine whether a computer vision system (CVS) composed of image acquisition, processing, and analysis could be effective to detect visual quality differences among 16 accessions of fresh-cut romaine lettuce during postharvest storage. The CVS involved a post-capturing color correction, effective image segmentation, and calculation of a browning index, which was tested as a predictor of quality and shelf life of fresh-cut romaine lettuce. The results demonstrated that machine vision software can be implemented to replace or supplement the scoring of a trained panel and instrumental quality measurements. Overall visual quality, a key sensory parameter that determines food preferences and consumer behavior, was highly correlated with the browning index, with a Pearson correlation coefficient of −0.85. Other important sensory decision parameters were also strongly or moderately correlated with the browning index, with Pearson correlation coefficients of −0.84 for freshness, 0.79 for off odor, and 0.57 for browning. The ranking of the accessions according to quality acceptability from the sensory evaluation produced a similar pattern to those obtained with the CVS. This study revealed that multiple lettuce accessions can be effectively benchmarked for their performance as fresh-cut sources via a CVS-based method. Future opportunities and challenges in using machine vision image processing to predict consumer preferences for RTE salad greens is also discussed.

Agriculture↗
NASA NTRS1973

Moessbauer search for ferric oxide phases in lunar materials and simulated lunar materials

Moessbauer studies were carried out on lunar fines and on simulated lunar glasses containing magnetic-like precipitates with the primary objective of determining how much, if any, ferric oxide is present in the lunar soils. Although unambiguous evidence of lunar Fe(3+) phases was not obtained, an upper limit was estimated from different portions of the Moessbauer spectra to be between 0.1 and 0.4 wt.% (as Fe3O4). A smaller than 62 microns fraction of 15021,118 showed 0.5 wt.% ferromagnetic iron at 300 K in as-returned condition. After heating to 650 C in an evacuated, sealed quartz tube for 1400 hours, the same sample exhibited 1 wt.% ferromagnetic iron at room temperature. An accompanying decrease in excess absorption area near zero velocity was noted. Thus, the result of the vacuum heat treatment was to convert fine grained iron to larger particles, apparently without the oxidation effects commonly reported.

Forester, D. W.↗
NASA NTRS1975

Grown organic matter as a fuel raw material resource

An extensive search was made on biomass production from the standpoint of climatic zones, water, nutrients, costs and energy requirements for many species. No exotic species were uncovered that gave hope for a bonanza of biomass production under culture, location, and management markedly different from those of existing agricultural concepts. A simulation analysis of biomass production was carried out for six species using conventional production methods, including their production costs and energy requirements. These estimates were compared with data on food, fiber, and feed production. The alternative possibility of using residues from food, feed, or lumber was evaluated. It was concluded that great doubt must be cast on the feasibility of producing grown organic matter for fuel, in competition with food, feed, or fiber. The feasibility of collecting residues may be nearer, but the competition for the residues for return to the soil or cellulosic production is formidable.

Roller, W. L.↗
DOE OSTI2026

Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to Ba⁡(Co,Fe,Zr,Y)⁢O 3−𝛿 combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗