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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Metamaterials as a Platform for the Development of Novel Materials for Energy Applications

To explore the fundamental properties of metamaterials (MMs) / metasurfaces and their potential for control of energy at the sub‐wavelength scale in support of the mission of the Department of Energy and the office of Basic Energy Sciences. Electromagnetic metamaterials provide a platform for the discovery and design of new materials with novel structures, functions, and properties. The PI proposes to advance the knowledge base of these materials through fundamental investigations of the experimental and theoretical properties of metamaterials for the discovery, prediction and design of new materials with novel structures, functions, and properties. The proposed research activities emphasize a complete basic research program including the conceptual / computational design, fabrication / synthesis of the materials, and the characterization and analysis of their electromagnetic properties. The proposed project explores the fundamental properties of metamaterials / metasurfaces and their potential for energy applications. There are three main topics which will be investigated: 1) Dispersion engineering with metamaterials and metasurfaces, 2) Epsilon near zero metamaterial absorbers and emitters, and 3) All dielectric metamaterials. The program implements a complete basic research program consisting of theory / design, modeling, characterization, and analysis, in order to fully characterize metamaterials and metasurfaces, while at the same time minimizing iterations necessary to achieve the proposal goals.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Building on the Cornerstone: Destinations for Nearside Sample Return

Discoveries from LRO (Lunar Reconnaissance Orbiter) have transformed our knowledge of the Moon, but LRO's instruments were originally designed to collect the measurements required to enable future lunar surface exploration. Compelling science questions and critical resources make the Moon a key destination for future human and robotic exploration. Lunar surface exploration, including rovers and other landed missions, must be part of a balanced planetary science and exploration portfolio. Among the highest planetary exploration priorities is the collection of new samples and their return to Earth for more comprehensive analysis than can be done in-situ. The Moon is the closest and most accessible location to address key science questions through targeted sample return. The Moon is the only other planet from which we have contextualized samples, yet critical issues need to be addressed: we lack important details of the Moon's early and recent geologic history, the full compositional and age ranges of its crust, and its bulk composition.

Lawrence, S. J.↗
NASA NTRS2008

CO2 Sensors Based on Nanocrystalline SnO2 Doped with CuO

Nanocrystalline tin oxide (SnO2) doped with copper oxide (CuO) has been found to be useful as an electrical-resistance sensory material for measuring the concentration of carbon dioxide in air. SnO2 is an n-type semiconductor that has been widely used as a sensing material for detecting such reducing gases as carbon monoxide, some of the nitrogen oxides, and hydrocarbons. Without doping, SnO2 usually does not respond to carbon dioxide and other stable gases. The discovery that the electrical resistance of CuO-doped SnO2 varies significantly with the concentration of CO2 creates opportunities for the development of relatively inexpensive CO2 sensors for detecting fires and monitoring atmospheric conditions. This discovery could also lead to research that could alter fundamental knowledge of SnO2 as a sensing material, perhaps leading to the development of SnO2-based sensing materials for measuring concentrations of oxidizing gases. Prototype CO2 sensors based on CuO-doped SnO2 have been fabricated by means of semiconductor-microfabrication and sol-gel nanomaterial-synthesis batch processes that are amendable to inexpensive implementation in mass production.

Xu, Jennifer C.↗
DOE OSTI2025

Charting the chemical space of Zintl phases with graph neural networks and bonding insights

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90 000 hypothetical Zintl phases and accurately discover 1810 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2× more accurate in predicting DFT stability than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Enhancing NASA Earth Science Data Discovery from Scientific Publications

Earth observations from space borne instruments have evolved explosively in the past decades. Following closely are reanalysis systems assimilating model and observational data, yielding even longer records and larger number of variables. Thanks to advances in internet technology, it is now easier than ever to visualize and analyze these data using web interfaces. On the other hand, it also becomes an increasingly daunting task to build upon the existing knowledge published in various peer reviewed sources, and navigate toward the most relevant data, analysis, and visualization. We present an analysis of a subset of publications that utilized a popular visualization web interface at the NASA Goddard Earth Science Data and Information Services Center. Known as "Giovanni", it allows researchers from wide backgrounds to work with hundreds of variables from space observations and assimilation systems. Since coming online more than a decade ago, Giovanni has been credited in more than 100 papers per year, and the total count now is estimated to be nearly 1,500. Many of these papers contain valuable information about when, where and how Giovanni has been used, and hence forge an opportunity to learn and share the knowledge of which variables were used for what research projects. The purpose of our work is to retrieve the information from the papers and organize it as a knowledge repository which links together datasets, variables, places, dates and phenomena all of which reflect the essence of the published research. Since the publications are unstructured texts, we use natural language processing along with machine learning methods in the retrieval process. One of the challenges is deciphering the dataset names, because in many cases researchers refer to variables, rather than the datasets containing them. To constrain the number of terms, we deploy Earth Science ontologies as dictionaries for the term extraction. We demonstrate that storing these terms and underlying ontologies, along with datasets, variables and papers in the knowledge graph database, enables various linkages between all these entities facilitating the data discovery. Thus, we are setting a qualitatively new stage in improvements of web data interfaces, where machine learning techniques are used to establish and optimize usage-based discovery of data.

Irina V Gerasimov↗
NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗
NASA NTRS1993

New materials: Fountainhead for new technologies and new science

The role of materials as the benchmark technologies which give epochs of human history their names continues into the present. The discovery of new materials has nearly always been the source of new materials science, and frequently of new technologies. This paper analyzes the actual processes by which new materials are synthesized, i.e. whether driven by serendipitous observations, new knowledge is pulled by the market, or integrated into a technological thrust. This analysis focuses on modern ceramic materials discoveries, since World War 2 and uses 45 years experience in materials synthesis in the author's own laboratory as case studies. A dozen different families of materials or processes are involved: hydrothermal reactions; sol-gel processing; clays and zeolites; electroceramics; zero expansion ceramics; diamond films; and radioactive waste host phases. Nanocomposite concepts introduced by the author a decade ago offer an entire, large, new class of materials which will dominate synthesis for the next period. The future of materials research for the next 25 years cannot be extrapolated from the past 25 years. We are near the asymptote for materials utilization in most metals. Likewise we are approaching saturation in improvement of many useful properties. Justifying much further 'basic' R/D for incremental improvement in civilian-oriented industries will not be easy. In materials synthesis, the near-term future is sure to emphasize not new phases, but tailored micro- and nanocomposites for chemical, electrical, optical, and magnetic uses. Unexpected new discoveries such as the Lanxide process may offer rarer chances for step function advances. The new structure of knowledge management will rely less on local research than on integration of worldwide inputs. Better scientific and technological opportunities will lie in designing knowledge intensive materials to meet the new environmental and conservation goals, and the human needs of the very large numbers at the bottom of the socio-economic structures of the world.

Rustum, Roy↗
NASA NTRS2016

NIAC Phase I Study Final Report on Large Ultra-Lightweight Photonic Muscle Space Structures

The research goal is to develop new tools support NASA's mission of understanding of the Cosmos by developing cost effective solutions that yield a leap in performance and science data. 'Maikalani' in Hawaiian translates to, "knowledge we gain from the cosmos." Missions like Hubble have fundamentally changed humanity's view of the cosmos. Last year's Nobel prize in physics was a result of astronomical discoveries. $9B class JWST size (6.5 meter diameter) space telescopes, when launched are anticipated to rewrite our knowledge of physics. Here we report on a neoteric meta-material telescope mirror technology designed to enable a factor of 100 or more reduction in areal density, a factor of 100 reduction in telescope production and launch costs as well as other advantages; a leap to enable missions to image the cosmos in unprecedented detail, with the associated gain in knowledge. Whether terahertz, visible or X-ray, reflectors used for high quality electromagnetic imaging require shape accuracy (surface figure) to far better than 1 wavelength (lambda) of the incident photons, more typically lambda/10 or better. Imaging visible light therefore requires mirror surfaces that approximate a desired curve (e.g. a sphere or paraboloid) with smooth shape deviation of th less than approximately 1/1000 the diameter of a human hair. This requires either thick high modulus material like glass or metal, or actuators to control mirror shape. During Phase I our team studied a novel solution to this systems level design mass/shape tradespace requirement both to advance the innovative space technology concept and also to help NASA and other agencies meet current operational and future mission requirements. Extreme and revolutionary NASA imaging missions such as Terrestrial Planet Imager (TPI) require lightweight mirrors with minimum diameters of 20 to 40 meters. For reference, NASA's great achievement; the Hubble space telescope, is only 2.4 meters in diameter. What is required is a way to make large inexpensive deployable mirrors where the cost is measured in millions, not billions like current efforts. For example we seek an interim goal within 10 years of a Hubble size (2.4m) primary mirror weighing 1 pound at a cost of 10K in materials. Described here is a technology using thin ultra lightweight materials where shape can be controlled simply with a beam of light, allowing imaging with incredibly low mass yet precisely shaped mirrors. These " Photonic Muscle" substrates will eventually make precision control of giant s p a c e apertures (mirrors) possible. OCCAM substrates make precision control of giant ultra light-weight mirror apertures possible. This technology is posed to create a revolution in remote sensing by making large ultra lightweight space telescopes a fiscal and material reality over the next decade.

Cosmos↗
DOE OSTI2025

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.

Science & Technology - Other Topics↗
NASA NTRS1974

Laboratory studies on the excitation and collisional deactivation of metastable atoms and molecules in the aurora and airglow

The aeronomy group at the University of Pittsburgh is actively engaged in a series of coordinated satellite, sounding rocket, and laboratory studies designed to expand and clarify knowledge of the physics and chemistry of planetary atmospheres. Three major discoveries have been made that will lead ultimately to a complete and dramatic revision of our ideas on the ionospheres of Mars, Venus, and the Earth and on the origin of their vacuum ultraviolet airglows. The results have already suggested a new generation of ionosphere studies which probably can be carried out best by laser heterodyning techniques. Laboratory studies have also identified, for the first time, the physical mechanism responsible for the remarkable nitric oxide buildup observed in some auroral arcs. This development is an important break-through in auroral physics, and has military ramifications of considerable interest to the Department of Defense. This work may also shed some light on related NO and atomic nitrogen problems in the mesosphere.

Zipf, E. C.↗
NASA NTRS2003

Earth's earliest biosphere-a proposal to develop a collection of curated archean geologic reference materials

The discovery of evidence indicative of life in a Martian meteorite has led to an increase in interest in astrobiology. As a result of this discovery, and the ensuing controversy, it has become apparent that our knowledge of the early development of life on Earth is limited. Archean stratigraphic successions containing evidence of Earth's early biosphere are well preserved in the Pilbara Craton of Western Australia. The craton includes part of a protocontinent consisting of granitoid complexes that were emplaced into, and overlain by, a 3.51-2.94 Ga volcanigenic carapace - the Pilbara Supergroup. The craton is overlain by younger supracrustal basins that form a time series recording Earth history from approximately 2.8 Ga to approximately 1.9 Ga. It is proposed that a well-documented suite of these ancient rocks be collected as reference material for Archean and astrobiological research. All samples would be collected in a well-defined geological context in order to build a framework to test models for the early evolution of life on Earth and to develop protocols for the search for life on other planets.

Archaea↗
NASA NTRS2005

Robotic Technologies for Surveying Habitats and Seeking Evidence of Life: Results from the 2004 Field Experiments of the "Life in the Atacama" Project

The Chilean Atacama Desert is the most arid region on Earth and in several ways analogous to Mars. Evidence suggests that the interior of the Atacama is lifeless, yet where the desert meets the Pacific coastal range dessication-tolerant microorganisms are known to exist. The gradient of biodiversity and habitats in the Atacama's subregions remain unexplored and are the focus of the Life in the Atacama project. Our field investigation attempts to bring further scientific understanding of the Atacama as a habitat for life through the creation of robotic astrobiology. This involves capabilities for autonomously traversing hundreds of kilometers while deploying sensors to survey the varying geologic and biologic properties of the environment, Fig. 1. Our goal is to make genuine discoveries about the limits of life on Earth and to generate knowledge about life in extreme environments that can be applied to future planetary missions. Through these experiments we also hope to develop and practice the methods by which a rover might best be employed to survey desert terrain in search of the habitats in which life can survive, or may have in the past.

Wettergreen, D.↗
NASA NTRS2011

LISA: Opening New Horizons

The Laser Interferometer Space Antenna (LISA) is a space-borne observatory that will open the low frequency (approx.0.1-100 mHz) gravitational wave window on the universe. LISA will observe a rich variety of gravitational wave sources, including mergers of massive black holes, captures of stellar black holes by massive black holes in the centers of galaxies, and compact Galactic binaries. These sources are generally long-lived, providing unprecedented opportunities for multi-messenger astronomy in the transient sky. This talk will present an overview of these scientific arenas, highlighting how LISA will enable stunning discoveries in origins, understanding the cosmic order, and the frontiers of knowledge.

Centrella, Joan M.↗
NASA NTRS1999

The NASA Discovery 5, Genesis Mission

Genesis is the NASA Discovery 5 mission to solar wind return samples to the Earth for analyses in terrestrial laboratories. This will significantly increase our knowledge of the chemical and isotopic composition of the solar system.

sample return Genesis NASA Discovery 5 solar wind↗
DOE OSTI2024

Data Science Enabled Enabled Discovery of Superconductors (Final Progress Report)

This Final Technical Report describes efforts by 4 PIs at the University of Florida (Peter Hirschfeld, Richard Hennig, Greg Stewart and James Hamlin), over the period September 2019-August 2023, to use data science and machine learning techniques to discover new conventional superconductors. The PIs constructed a discovery loop with two theorists and two experimentalists to: develop algorithms to machine learn descriptors correlating strongly with the critical temperature Tc (PI's Peter Hirschfeld, UF Physics and Richard Hennig, UF Materials Science and En), synthesize and measure properties of promising materials, and feed back the knowledge gained into the prediction algorithm. This work was motivated by the theoretical prediction and experimental discovery of high-pressure, high-pressure hydride superconductors, and to find ways to recreate the high critical temperatures in these systems at ambient pressure. Highlights from the grant include: 1) a new equation for Tc in terms of moments of the electron-phonon spectral function, improving on the so-called Allen-Dynes equation (1975); 2) study of the metastable A15 superconductor Nb3Si, formed under explosive compression at ~1000GPa to determine the kinetic barrier to the ground state structure; 3) the development of ultra-fast machine-learned atomic potentials for molecular dynamics, and 4) the discovery of superconductivity at 19K in WB2 arising from metastable defect structures in the crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1975

Application of Skylab imagery to some geological and environmental problems in Italy

Four topics are considered: regional geology of Sicily, volcanic surveillance in southern Italy, hydrogeology (with special regard given to the discovery and mapping of paleoriverbeds), and crop investigation. The discovery of unknown lineaments and structures in Sicily contributes to the geological knowledge of this region and in particular to the mechanical phenomena involving the upper part of the crust. An attempt was made to relate the status of vegetation surrounding Etna volcano to the magmatic gas escapes filtering through the soil. False-color Skylab images were used to analyze the vigor of the Etnean forestal belt vegetation canopy in order to map possible gas-vent ways as well as the 'active' microfractures. In northern Italy, buried channels were mapped in the Venetian Plain, and a tentative cost-benefit evaluation was done in the field of vegetational studies, both disease detection and species inventory were performed in the Po River Delta and in northwestern Italy.

Cassinis, R.↗
NASA NTRS2013

The Federation of Earth Science Information Partners ESIP

A broad-based, distributed community of science, data and information technology practitioners. With over 150 member organizations, the ESIP Federation brings together public, academic, commercial, and nongovernmental organizations to share knowledge, expertise, technology and best practices to improve opportunities for increasing access, discovery, integration and usability of Earth science data.

ESIP↗
DOE OSTI2026

Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis

Heterogeneous catalysis presents a distinct challenge for artificial intelligence (AI). Data sets are often small and inconsistently reported, catalyst representations are not standardized, and extracting fundamental knowledge requires integrating performance data, spectroscopic characterizations, and mechanistic models across multiple scales. Language offers a unifying representation across these modalities, making catalysis well suited for leveraging large language models (LLMs). By standardizing how catalytic data is represented, LLMs make dispersed experimental results more accessible to downstream statistical modeling. In this perspective, we focus our discussion around three opportunities where LLMs can significantly contribute to catalysis: (1) text to properties; (2) text to structure; and (3) text to mechanistic models. The discussion is followed by a perspective section on LLM-readiness of data, aligning LLM outputs with scientific correctness, and bridging lab-scale discovery to industrial deployment. Across each area, the most productive applications couple dispersed chemical knowledge with physics-grounded validation to produce verifiable hypotheses and actionable representations.

Catalysts↗