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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 OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
DOE OSTI2020

Strong temporal variability in methane fluxes from natural gas well pad soils

We measured methane and carbon dioxide fluxes at natural gas well pad soils and undisturbed soils in the Rocky Mountain and Gulf Coast regions of the United States, including producing and gas storage wells. We collected both short-term (15 min) and multi-day (between 3 and 8), continuous measurements at 47 well pads and two undisturbed locations. Methane fluxes varied by more than an order of magnitude over periods as short as 30 min (e.g., 19–593 mg m -2 h -1 in one instance), and diurnal and seasonal variability was also significant (e.g., spring-to-fall change from 509 to 14174 mg m -2 h -1 ). We hypothesize that short-term flux variability was caused by pulsed flow of methane during its migration through the subsurface. Barometric pressure and well conditions likely impacted fluxes, but we found only weak evidence for this. Bacterial methanotrophy appeared to impact methane flux magnitude and variability. We injected methane into the subsurface at one well, and we found that, while fluxes of methane and carbon dioxide, and combustible soil gas concentrations, increased in response to the injection, the response was not uniform, and fluxes exhibited high hourly-scale variability, in spite of a constant injection rate. Methane fluxes tended to be higher at well pad soils compared to background soils (often much higher), and fluxes tended to be higher at well pad locations closer to the well head.

03 NATURAL GAS↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
DOE OSTI2020

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Cooperative Support of the Fermilab Site’s Natural Areas and Related Programs (CRADA Final Report)

Under the prime contract with the Department of Energy, Fermi Research Alliance, LLC (FRA) is responsible for effective stewardship of the Fermi National Accelerator Laboratory (Fermilab) 6,800-acre site, to assure that Fermilab is renewed and enhanced to meet future mission needs. As the steward of the Fermilab site, FRA is responsible for maintaining the natural areas for the site (e.g., prairie restoration sites, woodlands, etc.), which are essential for protecting the health and safety of personnel working on or visiting the site, while supporting the scientific and educational missions of Fermilab. Fermilab Natural Areas (FNA) is a not-for-profit corporation organized and existing under the laws of the State of Illinois. FNA was founded in 2006 and incorporated in 2008 for the purposes of studying, conserving, and restoring the Fermilab Site’s natural areas in a manner consistent with the science mission of Fermilab and enhancing the awareness and visibility of the natural amenities of Fermilab to neighboring communities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Researcher-driven Campaigns Engage Nature's Notebook Participants in Scientific Data Collection

One of the many benefits of citizen science projects is the capacity they hold for facilitating data collection on a grand scale and thereby enabling scientists to answer questions they would otherwise not been able to address. Nature's Notebook, the plant and animal phenology observing program of the USA National Phenology Network (USA-NPN) suitable for scientists and non-scientists alike, offers scientifically-vetted data collection protocols and infrastructure and mechanisms to quickly reach out to hundreds to thousands of potential contributors. The USA-NPN has recently partnered with several research teams to engage participants in contributing to specific studies. In one example, a team of scientists from NASA, the New Mexico Department of Health, and universities in Arizona, New Mexico, Oklahoma, and California are using juniper phenology observations submitted by Nature's Notebookparticipants to improve predictions of pollen release and inform asthma and allergy alerts. In a second effort, researchers from the University of Maryland Center for Environmental Science are engaging Nature's Notebookparticipants in tracking leafing phenophases of poplars across the U.S. These observations will be compared to information acquired via satellite imagery and used to determine geographic areas where the tree species are most and least adapted to predicted climate change. Researchers in these partnerships receive benefits primarily in the form of ground observations. Launched in 2010, the juniper pollen effort has engaged participants in several western states and has yielded thousands of observations that can play a role in model ground validation. Periodic evaluation of these observations has prompted the team to improve and enhance the materials that participants receive, in an effort to boost data quality. The poplar project is formally launching in spring of 2013 and will run for three years; preliminary findings from 2013 will be presented. Participants in these special campaigns benefit through direct engagement in science. This form of researcher partnership has now been successfully pilot-tested and implemented in several instances, and provides a template for future research project campaigns.

Crimmins, Theresa M.↗
NASA NTRS2013

Researcher-driven Campaigns Engage Nature's Notebook Participants in Scientific Data Collection

One of the many benefits of citizen science projects is the capacity they hold for facilitating data collection on a grand scale and thereby enabling scientists to answer questions they would otherwise not been able to address. Nature's Notebook, the plant and animal phenology observing program of the USA National Phenology Network (USA-NPN) suitable for scientists and non-scientists alike, offers scientifically-vetted data collection protocols and infrastructure and mechanisms to quickly reach out to hundreds to thousands of potential contributors. The USA-NPN has recently partnered with several research teams to engage participants in contributing to specific studies. In one example, a team of scientists from NASA, the New Mexico Department of Health, and universities in Arizona, New Mexico, Oklahoma, and California are using juniper phenology observations submitted by Nature's Notebookparticipants to improve predictions of pollen release and inform asthma and allergy alerts. In a second effort, researchers from the University of Maryland Center for Environmental Science are engaging Nature's Notebookparticipants in tracking leafing phenophases of poplars across the U.S. These observations will be compared to information acquired via satellite imagery and used to determine geographic areas where the tree species are most and least adapted to predicted climate change. Results/Conclusions Researchers in these partnerships receive benefits primarily in the form of ground observations. Launched in 2010, the juniper pollen effort has engaged participants in several western states and has yielded thousands of observations that can play a role in model ground validation. Periodic evaluation of these observations has prompted the team to improve and enhance the materials that participants receive, in an effort to boost data quality. The poplar project is formally launching in spring of 2013 and will run for three years; preliminary findings from 2013 will be presented. Participants in these special campaigns benefit through direct engagement in science. This form of researcher partnership has now been successfully pilot-tested and implemented in several instances, and provides a template for future research project campaigns.

Crimmins, Theresa M.↗
NASA NTRSDate not supplied

Search Enhancements using Natural Language Processing Techniques

NASA Goddard Earth Sciences Data and Information Services Center (GESDISC) is one of the 12 NASA Science Mission Directorate Data Centers. The main goal of GESDISC is to provide earth science data, information, and services to the earth science data community. Consequently, data discovery is at the center of our mission and our search engine is the primary tool for our users to interact, find, and access our data. Existing search approaches are largely focused on hard-matching of keywords in the search query with dataset metadata. Here we propose to expand the search by introducing a complementary natural language processing (NLP) search. At the heart of our proposed NLP search, we trained a joint embedding using scientific text corpus and a curated set of dataset metadata. The embedding learns the association between words in our dataset metadata and those of the scientific text corpus. This enables us to go beyond simple hard-matching of a query and data set metadata and have a notion of “similarity” between the search query and the datasets. We further integrated our NLP search into the Elastic Search (ES) framework leveraging similarity search capabilities offered through the “dense_vector” field type. Our preliminary evaluations show that our proposed NLP search has the potential to be utilized to complement the existing search engine and serve as a base for a dataset recommendation system.

Armin Mehrabian↗
DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗
DOE OSTI2020

Towards renewable flavors, fragrances, and beyond

Esters demand a large space of unique molecules with broad range of applications as flavors, fragrances, pharmaceuticals, cosmetics, green solvents, and advanced biofuels. Global demand of natural esters in food, household cleaner, personal care, and perfume industries is increasing while the ester supply from natural sources has been limited. Development of novel microbial cell factories for ester production from renewable feedstocks can potentially provide an alternative and sustainable source of natural esters and hence help fulfill growing demand. In this work, we highlight recent advances in microbial production of esters and provide perspectives for improving its economic feasibility. As the field matures, microbial ester production platforms will enable renewable and sustainable production of flavors, fragrances, and open new market opportunities beyond what nature can offer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

High-Precision Zinc Isotopic Measurement of Certified Reference Materials Relevant to the Environmental, Earth, Planetary and Biomedical Sciences

The zinc (Zn) stable isotope system has emerged as a powerful tool for investigating natural and anthropogenic processes of interest to disciplines extending from the Earth and planetary sciences to biomedical research. In nature, Zn isotopes typically fractionate ( 66 Zn/ 64 Zn) within the range of –1 and +1 permil (‰), and however, analytical limitations restrict the application of the Zn isotope system. Specifically, there is a lack of well-characterised matrix-matched reference materials covering most Zn isotope applications that are essential for validating laboratory performance, particularly given the contamination prone nature of Zn. Furthermore, the increasing demand for analysis of size-limited materials and sample types displaying small, sub-permil level Zn isotope variations requires improvements in measurement procedures. To address these limitations, the δ 66 Zn values of two pure Zn solutions and eighteen reference materials including the previously uncharacterised RGM-2, BCR-279, DOLT-4, DOLT-5, NASS-7, SBC-1, SGR-1b, MESS-3 and HISS-1, and the understudied QLO-1, SDC-1, BCR-414, NASS-6 and COQ-1 materials are reported. Furthermore, a 70 Zn- 67 Zn double-spike design has been implemented that routinely achieves intermediate measurement precision better than ± 0.02‰. This represents at least a two-fold improvement when compared with other double-spike designs, providing a robust platform of δ 66 Zn values required for quality control procedures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Arctic shrub expansion generates regional variation in litter decomposition by altering litter quality and the decomposition environment

Abstract The expansion of deciduous shrubs into the graminoid‐dominated arctic tundra is expected to alter litter decomposition by changing litter quality and local abiotic and biotic conditions. However, it remains unclear how shrub expansion will affect litter decomposition at regional scales, where macroclimate is expected to be the dominant regulator of decomposition. To determine the relative influence of macroclimate and local controls on regional patterns of litter mass loss and nitrogen release, we conducted two hierarchical litter decomposition experiments across spatial scales. We decomposed leaf and root litter from a prominent graminoid ( Eriophorum vaginatum ) and three genera of deciduous shrubs ( Betula , Alnus and Salix ) for 1 year within replicated plots at five sites spanning a 160 km latitudinal gradient in northern Alaska. Using Eriophorum litter as a substrate, we found that macroclimate was the primary regulator of mass loss but had opposing effects on leaf and root litter. As summer temperature increased along the latitudinal gradient (11.9 to 13.9°C), leaf litter mass loss increased by 20% whereas root litter mass loss decreased by 33%. Leaf nitrogen release also increased with summer temperature. Conversely, root nitrogen release was controlled by the vegetation type of the decomposition environment. Using different shrub litters as substrates, we found that litter quality and its interaction with soil microclimate and macroclimate controlled decomposition. Overall, shrub root litters decomposed faster than Eriophorum root litter, losing 53% more mass and 190% more nitrogen across all sites and decomposition environments. For leaf litter, however, patterns varied by litter genus, with Salix losing more mass and Betula and Alnus losing less mass than Eriophorum . Our findings demonstrate that shrub expansion in the Arctic can regulate leaf and root litter decomposition at the regional scale through its effects on local controls, primarily litter quality. Ongoing increases in shrub cover are likely to accelerate the turnover of root litter carbon and nitrogen pools in tundra ecosystems. Therefore, including shrub‐related processes in Earth system models will improve our ability to predict regional‐scale litter decomposition and its effects on carbon and nutrient cycling in a warming Arctic. Read the free Plain Language Summary for this article on the Journal blog.

Vozzo, Justin T. [Department of Natural Resources ↗
NASA NTRS1997

School Science Comes Alive

Phase 3 of the School Science Comes Alive Program (S(sup 2)CAP) created an exciting, science - enrichment experience for third, fourth and fifth graders and their teachers and enhanced the science-teaching skills of teacher teams at each of four participating elementary schools on Virginia's Peninsula. The schools involved enroll a majority of Black students, many of whom are from economically disadvantaged households. Designed to build on the highly successful S(sup 2)CAP program of the preceding two years, this project brought college faculty together with classroom teachers and trained volunteers in a cooperative effort to make a lasting difference in the quality of science education at the four schools. In total, this program touched approximately 1000 the school children, more than half of whom are black, giving them direct and indirect exposure to the spirit of inquiry and adventure of the world-wide science community. In S(sup 2)CAP Phase 3, a large measure of responsibility was placed on the classroom teachers, thus creating a more sustainable partnership between college faculty and grade school teacher. Our college physics professors coached and supported teams of teachers from each school at intensive training workshops. A volunteer program provided each teacher with one or more trained volunteers to assist in class with the hands-on activities that have been central to the S2CAP program. Most of the equipment for these activities was constructed during the workshops by the teachers and volunteers from low cost materials provided by the program. Two types of volunteers were enlisted: science smart black college students and technically trained retirees (many of whom are ex-NASA employees). One goal of this program was to increase the numbers of minority students who see science as an interesting and exciting subject, to make the science period a time which students look forward to in the school day. Such an attitude is expected to translate naturally into a higher interest in science and engineering as a career for these students. A second goal was to create a sustainable improvement in the way science is taught at the elementary level. By the end of the program we expected that our teachers would be significantly more self reliant in using hands-on-activities as a part of their science curricula than they were prior to their involvement with S2CAP. In summary, S2CAP Phase 3 offered intensive training workshops for teachers and supporting volunteers followed by stimulating hands-on activities in the classroom for the children. These components combined to amplify the experience, enthusiasm, and ideas of our scientists in a way that complements the normal elementary school curriculum in each of the two school systems involved.

Hartline, Frederick F.↗
DOE OSTI2024

Unlocking saponin biosynthesis in soapwort

Abstract Soapwort ( Saponaria officinalis ) is a flowering plant from the Caryophyllaceae family with a long history of human use as a traditional source of soap. Its detergent properties are because of the production of polar compounds (saponins), of which the oleanane-based triterpenoid saponins, saponariosides A and B, are the major components. Soapwort saponins have anticancer properties and are also of interest as endosomal escape enhancers for targeted tumor therapies. Intriguingly, these saponins share common structural features with the vaccine adjuvant QS-21 and, thus, represent a potential alternative supply of saponin adjuvant precursors. Here, we sequence the S . officinalis genome and, through genome mining and combinatorial expression, identify 14 enzymes that complete the biosynthetic pathway to saponarioside B. These enzymes include a noncanonical cytosolic GH1 (glycoside hydrolase family 1) transglycosidase required for the addition of d- quinovose. Our results open avenues for accessing and engineering natural and new-to-nature pharmaceuticals, drug delivery agents and potential immunostimulants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Embracing the Intersections of Environmental Science, Engineering, and Geosciences to Solve Grand Challenges of the 21st Century

The U.S. National Academies report on Environmental Engineering for the 21st Century identified five grand challenges of sustainably supplying food, water, and energy; curbing climate change and adapting to its impacts; designing a future without pollution and waste; creating efficient, healthy, resilient cities; and fostering informed decisions and actions. (1) Addressing many of the grand challenges will require embracing the intersections of environmental science, engineering, and geosciences. These three fields are inherently interdisciplinary, and they naturally intersect with each other (Figure 1). Geosciences, or Earth sciences, study the dynamics of different spheres of Earth. One of its missions is to detect the availability of mineral, water, and fuel resources, for sustainable development, and new research opportunities were identified in “coevolution of life, environment, and climate” and “biogeochemical and water cycles in terrestrial environments and impacts of global change” by the U.S. National Research Council. (2) Environmental science and engineering focus on the spheres that intersect with human activities. Environmental science puts an emphasis on understanding the migration of naturally occurring and anthropogenic contaminants in the environment and their impact on ecosystem and human health, and environmental engineering centers on developing technologies for water supply, mineral exploration, and environmental remediation to mitigate adverse impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Energy demand science for a decarbonized society in the context of the residential sector

To develop a decarbonized society, two contradictory requirements must be met: (1) reducing energy demand and (2) creating flexibility in energy demand in order to respond to fluctuations in renewable electricity generation. To help meet these requirements, conventional energy efficiency studies should be extended to incorporate “energy demand science.” This paper presents a definition of “energy demand science” and then reviews the related history and research questions of energy demand science in the context of the residential sector. It then examines three key areas that must be integrated into the next-generation energy demand science: (1) energy demand measurement with detailed granularity and analysis using cutting-edge technology, (2) energy demand modeling that helps clarify the formation mechanism of energy demand, and (3) identification of the factors that influence people's decision making, which represents typical human-dimension research. Energy demand science consists of technical, human, natural environment, demographic, and land-use dimensions, and their integration is key for the establishment of a decarbonized society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Science of the Bomb

Uranium is a naturally occurring element with wo isotopes of interest – U238 and U235; U235 is useful for its ability to sustain a chain reaction; Chain reactions are the fundamental way that nuclear reactors work, as well as how bombs work; Enrichment of U235 is important for both nuclear energy as well as nuclear weapons; A safety focus is at the center of all nuclear operations in the United States

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS1998

The Consortium for Advancing Renewable Energy Technology (CARET)

The Consortium for Advancing Renewable Energy (CARET) is a research and education program which uses the theme of renewable energy to build a minority scientist pipeline. CARET is also a consortium of four universities and NASA Lewis Research Center working together to promote science education and research to minority students using the theme of renewable energy. The consortium membership includes the HBCUs (Historically Black Colleges and Universities), Fisk, Wilberforce and Central State Universities as well as Kent State University and NASA Lewis Research Center. The various stages of this pipeline provide participating students experiences with a different emphasis. Some emphasize building enthusiasm for the classroom study of science and technology while others emphasize the nature of research in these disciplines. Still others focus on relating a practical application to science and technology. And, of great importance to the success of the program are the interfaces between the various stages. Successfully managing these transitions is a requirement for producing trained scientists, engineers and technologists. Presentations describing the CARET program have been given at this year's HBCU Research Conference at the Ohio Aerospace Institute and as a seminar in the Solar Circle Seminar series of the Photovoltaic and Space Environments Branch at NASA Lewis Research Center. In this report, we will describe the many positive achievements toward the fulfillment of the goals and outcomes of our program. We will begin with a description of the interactions among the consortium members and end with a description of the activities of each of the member institutions .

Gordon, E. M.↗