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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Differentiated meteorites and the components of chondrites

Findings are summarized from research conducted to develop a detailed classification of all kinds of meteorites in an effort to determine the conditions in the solar nebula, the processes that produced chemical fractionations in chondrites and formed chondrules, as well as ascertain the processes that occurred in the parent bodies of differentiated meteorites (which preserve a partial record of the chondritic materials from which they formed). Fractionation patterns within iron meteorite groups are analyzed.

Wasson, J. T.↗
NASA NTRS2024

Bulk Elemental Composition of Aggregate Material From Asteroid Bennu

On September 24, 2023, NASA’s OSIRIS-REx mission returned pristine carbonaceous material from asteroid Bennu. One key aspect required to understand this returned material is to establish its bulk chemical composition. This is primarily due to each chondrite group having a distinct elemental composition, which is important both as a classification tool and for establishing the connections between the different chondrite groups. As the carbonaceous and non-carbonaceous chondrites are thought to have formed in the inner and outer protoplanetary disk, respectively, measuring the bulk elemental composition of Bennu material will help test the mission hypothesis that “Bennu's parent body formed beyond the snow line by accretion of material in the protoplanetary disk”. In addition, two further mission hypotheses — paraphrased as Bennu’s bulk elemental composition reflects that of its parent asteroid and is similar to the composition of the Sun and Bennu’s dominant lithologies are comparable in composition to the most aqueously altered carbonaceous chondrites — can be directly tested by bulk elemental analyses. Elemental analyses were undertaken on sample OREX-803015-0, a 20.66 mg aggregate sourced from inside the sample collector. All analyses were conducted using an iCAP Q ICP-MS. Overall, all 54 elements analyzed for the Bennu aggregate sample show similar abundances to those of the average CI chondrite, and thus the solar photosphere composition and some of the most aqueously altered carbonaceous chondrites. The elemental data also show that Bennu is indeed a carbonaceous asteroid that formed beyond the snow line by accretion of material in the protoplanetary disk. This Bennu aggregate sample appears similar in elemental composition to samples of asteroid Ryugu, yet without the small refractory element enrichments seen in Ryugu.

P Koefoed↗
NASA NTRS1988

Overview and classification of meteorites

Before interpreting properties of meteorites in terms of putative processes and conditions in the early solar system, it is necessary to understand just what sort of objects meteorites are. Such understanding begins with classification. In this chapter, the current taxonomy of meteorites is summarized, and it is shown how certain stone meteorites, the chondrites, possess chemical and petrographic features that make them potentially attractive as probes of the early solar system. The prevalence of secondary alteration effects, often capable of perturbing the primitive record even in chondrites, is also emphasized.

Sears, Derek W. G.↗
NASA NTRS2005

Thin Films and Inflatable Applications in Exploration Habitat Structures

NASA's vision to return to the Moon and then extend human exploration to Mars will benefit from human habitat technology development using new and creative applications of polymer materials and concepts. Thin Film and Inflatable (TFVI) materials are particularly interesting for exploration applications due to their light weight and low volume. Whereas TF/I materials can be launched and carried from Earth to multiple and distant exploration sites without the constraints of upmass limitations, eventually, with recycling and reclamation efforts, these materials may be developed using in-situ resources. These materials can be useful for fabricating, patching and repairing vehicles, replacement parts and even habitat structures, as well as for developing stand-alone habitat structure technologies and for nested and integrated applications. TF/Is can also be ideal environmental containment vessels within lunar or Martian regolith walls or as liners inside caves or raw regolith exterior structures for the provisions of atmosphere containment, debris protection and cleanliness. Further, TFOs can be specialized and matured for various and diverse applications. The desired range of applications will require materials specification for such properties as transparency, elasticity, thermal conductivity, mechanical strength, heat capacity, chemical resistance, and permeability. This paper will discuss Marshall Space Flight Center's plans to analyze and prioritize TF/I materials properties and classifications and to develop applications for these highly desirable materials in human habitat construction projects on the Moon and Mars.

Frazier, Donald O.↗
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2025

CSGL: chemical synthesis graph learning for molecule representation

Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
NASA NTRS1996

Chemical Characteristics of Continental Outflow Over the Tropical South Atlantic Ocean from Brazil and Africa

The chemical characteristics of air parcels over the tropical South Atlantic during September - October 1992 are summarized by analysis of aged marine and continental outflow classifications. Positive correlations between CO and CH3CL and minimal enhancements of C2CL40, and various ChloroFluoroCarbon (CFC) species in air parcels recently advected over the South Atlantic basin strongly suggest an impact on tropospheric chemistry from biomass burning on adjacent continental areas of Brazil and Africa. Comparison of the composition of aged Pacific air with aged marine air over the South Atlantic basin from 0.3 to 12.5 km altitude indicates potential accumulation of long-lived species during the local dry season. This may amount to enhancements of up to two-fold for C2H6, 30% for CO, and 10% for CH3Cl. Nitric oxide and NO(x) were significantly enhanced (up to approx. 1 part per billion by volume (ppbv)) above 10 km altitude and poorly correlated with CO and CH3Cl. In addition, median mixing ratios of NO and NO(x) were essentially identical in aged marine and continental outflow air masses. It appears that in addition to biomass burning, lightning or recycled reactive nitrogen may be an important source of NO(x) to the upper troposphere. Methane exhibited a monotonic increase with altitude from approx. 1690 to 1720 ppbv in both aged marine and continental outflow air masses. The largest mixing ratios in the upper troposphere were often anticorrelated with CO, CH3Cl, and CO2, suggesting CH, contributions from natural sources. We also argue, based on CH4/CO ratios and relationships with various hydrocarbon and CFC species, that inputs from biomass burning and the northern hemisphere are unlikely to be the dominant sources of CO, CH4 and C2H6 in aged marine air. Emissions from urban areas would seem to be necessary to account for the distribution of at least CH4 and C2H6. Over the African and South American continents an efficient mechanism of convective vertical transport coupled with large-scale circulations conveys biomass burning, urban, and natural emissions to the upper troposphere over the South Atlantic basin. Slow subsidence over the eastern South Atlantic basin may play an important role in establishing and maintaining the rather uniform vertical distribution of long-lived species over this region. The common occurrence of values greater than 1 for the ratio CH3OOH/H2O2 in the upper troposphere suggests that precipitation scavenging effectively removed highly water soluble gases (H2O2, HNO3, HCOOH, and CH3COOH) and aerosols during vertical convective transport over the continents. However, horizontal injection of biomass burning products over the South Atlantic, particularly water soluble species and aerosol particles, was frequent below 6 km altitude.

Talbot, R. W.↗
DOE OSTI2025

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado

Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Size and Time-resolved Aerosol Collector↗
NASA NTRS1991

Abstracts for the International Conference on Asteroids, Comets, Meteors 1991

Topics addressed include: chemical abundances; asteroidal belt evolution; sources of meteors and meteorites; cometary spectroscopy; gas diffusion; mathematical models; cometary nuclei; cratering records; imaging techniques; cometary composition; asteroid classification; radio telescopes and spectroscopy; magnetic fields; cosmogony; IUE observations; orbital distribution of asteroids, comets, and meteors; solar wind effects; computerized simulation; infrared remote sensing; optical properties; and orbital evolution.

Source record↗
NASA NTRSDate not supplied

Aqueous alteration of Cb-type asteroid Ryugu

The samples from the asteroid Ryugu, which returned to Earth in the winter of 2020 [1], were stored under pure Nitrogen conditions at the Extraterrestrial Materials Curation Center of ISAS. After basic description [2-3], they were distributed to six initial analysis teams in Japan in June 2021. The initial analysis "Stone" team, which was responsible for the analysis of coarse particles (>1 mm in size), consisted of about 150 scientists from Japan and abroad, and conducted a series of analyses We analyzed sixteen coarse Ryugu particles 1 ~ 8 mm in size: six from the 1st touch-down site and ten from the 2nd touch-down site. Individual coarse samples were cut by Xe-FIB or wire-saw to expose particular objects or textures to be exposed on the surface based on 3D structure and element distribution obtained by synchrotron X-ray CT analysis and XRF analysis, respectively. FE-SEM/EDS and FE-EPMA/WDS analysis were made on all polished sections. TEM observation of many FIB sections was made to see microstructures and to compare with carbonaceous chondrites. Many other analyses were carried out to characterize mineralogical and compositional properties and summary of the analysis procedures is described in [4]. X-ray CT analysis showed that all sixteen Ryugu particles are composed of fine-grained material, with no chondrules and CAIs larger than 100 μm in size. FE-EPMA observation showed that Ryugu samples are breccias, consisting of many small rock fragments of different compositions, lithologies, and histories. The most common lithology includes Mg-rich saponite and serpentine, dolomite, magnesite, hydroxyapatite, pyrrhotite, and magnetite as main constituents. The mineralogy of this major lithology supports the classification of Ryugu samples as CI chondrites, which experienced extensive aqueous alteration in Ryugu’s parent asteroid. Based on the obtained mineralogical properties, we performed chemical modeling of aqueous alteration of Ryugu’s parent asteroid [5]. Numerical simulations [6-8] to reproduce thermal history and impact-induced destruction of the Ryugu’s parent asteroid were also carried out using mineralogical and physical properties of the Ryugu samples. In the talk, I would like to explain the conditions of aqueous alteration occurred in the parent asteroid of Ryugu in order to show how the water in the asteroid changed the mineralogy and chemistry of the nebular dust.

T. Nakamura↗
NASA NTRS1970

Pyrotechnic hazards classification and evaluation program. Phase 2, segment 1: Records and experience analysis

A comprehensive search review and analysis was made of various technical documents relating to both pyrotechnics and high explosives testing, handling, storage, manufacturing, physical and chemical characteristics and accidents and incidents. Of approximately 5000 technical abstracts reviewed, 300 applicable documents were analyzed in detail. These 300 documents were then converted to a subject matrix so that they may be readily referenced for application to the current programs. It was generally concluded that information in several important categories was lacking. Two of the more important categories were in pyrotechnics sensitivity testing and TNT equivalency testing. A general recommendation resulting from this study was that this activity continue and a comprehensive data bank be generated that would allow immediate access to a large volume of pertinent information in a relatively short period of time.

Source record↗
NASA NTRS1987

Discrimination and supervised classification of volcanic flows of the Puna-Altiplano, Central Andes Mountains using Landsat TM data

Landsat Thematic Mapper (TM) images of portions of the Central Andean Puna-Altiplano volcanic belt have been tested for the feasibility of discriminating individual volcanic flows using supervised classifications. This technique distinguishes volcanic rock classes as well as individual phases (i.e., relative age groups) within each class. The spectral signature of a volcanic rock class appears to depend on original texture and composition and on the degree of erosion, weathering, and chemical alteration. Basalts and basaltic andesite stand out as a clearly distinguishable class. The age dependent degree of weathering of these generally dark volcanic rocks can be correlated with reflectance: older rocks have a higher reflectance. On the basis of this relationship, basaltaic lava flows can be separated into several subclasses. These individual subclasses would correspond to mappable geologic units on the ground at a reconnaissance scale. The supervised classification maps are therefore useful for establishing a general stratigraphic framework for later detailed surface mapping of volcanic sequences.

Mcbride, J. H.↗
DOE OSTI2025

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗
DOE OSTI2024

Assessment of fine-tuned large language models for real-world chemistry and material science applications

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens doors to a wider chemical audience, as field-specific featurization techniques can be omitted. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning three open-source LLMs (GPT-J-6B, Llama-3.1-8B, and Mistral-7B) for a range of different chemical questions. We benchmark their performances against “traditional” machine learning models and find that, in most cases, the fine-tuning approach is superior for a simple classification problem. Depending on the size of the dataset and the type of questions, we also successfully address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations.

Van Herck, Joren↗
NASA NTRS1973

Fused Silica Surface Coating for a Flexible Silica Mat Insulation System

Fused silica insulation coatings have been developed for application to a flexible mat insulation system. Based on crystalline phase nucleation and growth kinetics, a 99+% SiO2 glass was selected as the base composition. A coating was developed that incorporated the high emissivity phase NiCr2O4 as a two phase coating with goals of high emittance and minimum change in thermal expansion. A second major coating classification has a plasma sprayed emittance coating over a sealed pure amorphous SiO2 layer. A third area of development centered on extremely thin amorphous SiO2 coatings deposited by chemical vapor deposition. The coating characterization studies presented are mechanical testing of thin specimens extracted from the coatings, cyclic arc exposures, and emittance measurements before and after arc exposures.

Rhodes, W. H.↗
NASA NTRS1989

Fundamentals of tribology at the atomic level

Tribology, the science and engineering of solid surfaces in moving contact, is a field that encompasses many disciplines: solid state physics, chemistry, materials science, and mechanical engineering. In spite of the practical importance and maturity of the field, the fundamental understanding of basic phenomena has only recently been attacked. An attempt to define some of these problems and indicate some profitable directions for future research is presented. There are three broad classifications: (1) fluid properties (compression, rheology, additives and particulates); (2) material properties of the solids (deformation, defect formation and energy loss mechanisms); and (3) interfacial properties (adhesion, friction chemical reactions, and boundary films). Research in the categories has traditionally been approached by considering macroscopic material properties. Recent activity has shown that some issues can be approached at the atomic level: the atoms in the materials can be manipulated both experimentally and theoretically, and can produce results related to macroscopic phenomena.

Ferrante, John↗
NASA NTRS2012

Observing Changing Ecological Diversity in the Anthropocene

As the world enters the Anthropocene, the planet's environment is changing rapidly, putting critical ecosystem services at risk. Understanding and forecasting how ecosystems will change over the coming decades requires understanding the sensitivity of species to environmental change. The extant distribution of species and functional groups contains valuable information about the performance of different species in different environments. However, with high rates of environmental change, information inherent in ranges of many species will disappear, since that information exists only under quasi-equilibrium conditions. The information content of distributional data obtained now is greater than data obtained in the future. New remote sensing technologies can map chemical and structural traits of plant canopies and allow inference of trait and in many cases, species ranges. Current satellite remote sensing data can only produce relatively simple classifications, but new techniques have dramatically higher biological information content.

environmental changes↗