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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
NASA NTRS2012

Inspiring the Next Generation of Explorers: Scientist Involvement in the Expedition Earth and Beyond Program

Scientists, science experts, graduate and even undergraduate student researchers have a unique ability to inspire the next generation of explorers. These science, technology, engineering, and mathematics (STEM) experts can serve as role models for students and can help inspire them to consider future STEM-related careers. They have an exceptional ability to instill a sense of curiosity and fascination in the minds of students as they bring science to life in the classroom. Students and teachers are hungry for opportunities to interact with scientists. They feel honored when these experts take time out of their busy day to share their science, their expertise, and their stories. The key for teachers is to be cognizant of opportunities to connect their students with scientists. For scientists, the key is to know how to get involved, to have options for participation that involve different levels of commitment, and to work with educational specialists who can help facilitate their involvement. The Expedition Earth and Beyond (EEAB) Program, facilitated by the Astromaterials Research and Exploration Science (ARES) Directorate at the NASA Johnson Space Center, is an Earth and planetary science education program designed to inspire, engage, and educate teachers and students by getting them actively involved with NASA exploration, discovery, and the process of science. One of the main goals of the program is to facilitate student research in the classroom. The program uses astronaut photographs, provided through the ARES Crew Earth Observations (CEO) payload on the International Space Station (ISS) as the hook to help students gain an interest in a research topic. Student investigations can focus on Earth or involve comparative planetology. Student teams are encouraged to use additional imagery and data from Earth or planetary orbital spacecraft, or ground-based data collection tools, to augment the astronaut photography dataset. A second goal of the program is to provide opportunities for meaningful connections between scientists and classrooms. To do this, EEAB offers multiple opportunities for scientist involvement. One opportunity involves having scientists work as mentors for student teams conducting research. These student teams, ranging from grades 4 through 12, are able to obtain guidance, suggestions, and input from STEM experts as they conduct a research investigation. Another opportunity for scientist involvement is participation in Classroom Connection Distance Learning (DL) events. These DL events entail interactive and engaging presentations that enable STEM experts to share their expertise with students and teachers (grades 3 through 12) from all across the nation. A third opportunity for scientist involvement involves participation in virtual student team science presentations. Student teams have the opportunity to share their research and results by presenting it to science experts through the use of WebEx, an easy-to-use online conferencing tool. The impact STEM experts have on students in today s classrooms is powerful. They serve as role models to these students, and they open students eyes to a potential career path they may not have known existed otherwise. The more scientists and STEM experts we can connect with students, the greater the impact we can make as we strive to inspire and prepare our nation s next generation of explorers.

Graff, Paige↗
NASA NTRS2012

Evolved Gas Analysis of Mars Analog Samples from the Arctic Mars Analog Svalbard Expedition: Implications for Analyses by the Mars Science Laboratory

The 2011 Arctic Mars Analog Svalbard Expedition (AMASE) investigated several geologic settings on Svalbard, using methodologies and techniques being developed or considered for future Mars missions, such as the Mars Science Laboratory (MSL). The Sample Analysis at Mars (SAM) instrument suite on MSL consists of a quadrupole mass spectrometer (QMS), a gas chromatograph (GC), and a tunable laser spectrometer (TLS), which analyze gases created by pyrolysis of samples. During AMASE, a Hiden Evolved Gas Analysis-Mass Spectrometer (EGA-MS) system represented the EGA-QMS capability of SAM. Another MSL instrument, CheMin, will use x-ray diffraction (XRD) and x-ray fluorescence (XRF) to perform quantitative mineralogical characterization of samples. Field-portable versions of CheMin were used during AMASE. AMASE 2011 sites spanned a range of environments relevant to understanding martian surface materials, processes and habitability. They included the basaltic Sverrefjell volcano, which hosts carbonate globules, cements and coatings, carbonate and sulfate units at Colletth0gda, Devonian sandstone redbeds in Bockfjorden, altered basaltic lava delta deposits at Mt. Scott Keltie, and altered dolerites and volcanics at Botniahalvoya. Here we focus on SAM-like EGA-MS of a subset of the samples, with mineralogy comparisons to CheMin team results. The results allow insight into sample organic content as well as some constraints on sample mineralogy.

McAdam, A.↗
NASA NTRS2012

Parallels Between Antarctic Travel in 1950 and Planetary Travel in 2050 (to Accompany Notes on "The Norwegian British-Swedish Antarctic Expedition 1949-52")

Objectives (Slides 2, 12, 21-22) To explore as much as possible of 1 million km2 of unexplored territory. We were the first expedition to winter in Antarctica between 95 E and 57 W - nearly half the coastline of Antarctica. It was understood that we must be self-sufficient in every respect for 2 years. There could be no firm or detailed plans for inland exploration until we found where it was possible to make a landing. Geology (Slide 20) Our two geologists traveled far from the Advance Base during both field seasons. Carrying fuel supplies (dog food) for a month, man food (dehydrated) and rock specimens acquired along the way, they covered a vast area. The surveyor drove his own dogs with the geophysicist as assistant. While the geologists were hacking away at rocks, the survey team lugged a theodolite up peaks to extend a triangulation network. Glaciology (Slides 21-22) The glaciologists each had an assistant from the support staff, so they could either travel together or divided into two parties to cover more ground. At each camp they dug a pit to determine the rate of snow accumulation, drilled (by hand) to a depth of 10 m to measure ice temperatures, and in places set up and surveyed ice-movement markers to be resurveyed the following season. Geophysics (Slides 33, 34-36, 38) The principal object was to determine the thickness of ice by seismic sounding the only means known at the time. After experiments as far as the Advance Base in the 1950-51 summer, both Weasels were devoted to a seismic sounding traverse in 1951-52 as far inland as supplies would allow. The party reached 620 km inland and found ice thicknesses of 2,500 m.

Swithinbank, Charles↗
NASA NTRS2013

A Comparison of Expedition Medical Condition List Treatment Directives with Integrated Medical Model Simulation Data Presentation and Briefing Report

This aerospace medicine clerkship project is under the direction of Dr. Sharmila Watkins and is in cooperation with Dr. Eric Kerstman and Dr. Ronak Shah. The questions of the research project are: 1. What are the main drivers of evacuation and loss of crew life (LOCL) on three Design Reference Missions (DRMs): Near Earth Asteroid (NEA), Lunar Sortie and Lunar Outpost using an inexhaustible International Space Station medical kit 2. What are the treatment designations for these driving medical conditions as listed in Expedition Medical Condition List (EMCL) 3. Do the drivers make sense in the context of the given Design Reference Mission (DRM) 4. Do any EMCL treatment designations need re-assessing.

Lewis, Robert↗
NASA NTRS2013

Underway Sampling of Marine Inherent Optical Properties on the Tara Oceans Expedition as a Novel Resource for Ocean Color Satellite Data Product Validation

Developing and validating data records from operational ocean color satellite instruments requires substantial volumes of high quality in situ data. In the absence of broad, institutionally supported field programs, organizations such as the NASA Ocean Biology Processing Group seek opportunistic datasets for use in their operational satellite calibration and validation activities. The publicly available, global biogeochemical dataset collected as part of the two and a half year Tara Oceans expedition provides one such opportunity. We showed how the inline measurements of hyperspectral absorption and attenuation coefficients collected onboard the R/V Tara can be used to evaluate near-surface estimates of chlorophyll-a, spectral particulate backscattering coefficients, particulate organic carbon, and particle size classes derived from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODISA). The predominant strength of such flow-through measurements is their sampling rate-the 375 days of measurements resulted in 165 viable MODISA-to-in situ match-ups, compared to 13 from discrete water sampling. While the need to apply bio-optical models to estimate biogeochemical quantities of interest from spectroscopy remains a weakness, we demonstrated how discrete samples can be used in combination with flow-through measurements to create data records of sufficient quality to conduct first order evaluations of satellite-derived data products. Given an emerging agency desire to rapidly evaluate new satellite missions, our results have significant implications on how calibration and validation teams for these missions will be constructed.

remote sensing↗
NASA NTRS2019

Reusable In-Space Transportation Architecture Options for Human Mars Expeditions

NASA has been investigating the use of Solar Electric Propulsion, Chemical Propulsion, and Nuclear Thermal Propulsion systems to deliver crew and cargo to Mars orbit in support of expeditions to the Martian surface. In order to evaluate the effect of the selection of in-space transportation options on a series of human Mars missions across multiple opportunities, campaign level analysis must be performed and the transportation options must be evaluated using the same set of ground rules and assumptions to provide equal comparison. In this study, a comparison of three in-space transportation options was shown from a campaign level perspective. Campaign level metrics such as total launch mass, launch vehicle launch rate, and total launch vehicle required are presented to show how the different transportation systems perform to field a series of Mars missions across multiple mission opportunities. Each transportation option was utilized to perform three crew roundtrip missions to Mars, with supporting cargo missions starting in 2029, the first crewed mission to Mars in 2035. The transportation options each have strengths and weaknesses from a campaign and strategic investment point of view, as evident by the comparison between the total launch mass and the total number of launch vehicles required. While the analysis shown in this study provides an initial comparison between the transportation options, significant challenges still exists for each options in terms of the detailed mission operations and the feasibility of all of the assumptions.

Chai, Patrick R.↗
NASA NTRSDate not supplied

An Expanded Role for Python in Expediting System Simulation Development

The Python programming language has traditionally been used as a “scripting” language and is not generally recognized as a language for building system simulations, where C++, Java, and other compiled languages are typically used. While very powerful and gaining favor for a large set of programming tasks, Python is generally regarded as not having the speed to directly do the extensive numeric computations required in large-scale simulation. This paper aims to present an approach, supported by case studies, to re-think this assertion. Directly using Python in this new role has the potential to expedite an agile, spiral cycle of development of design-test-modify for systems simulation.

programming language↗
NASA NTRS2022

Overview of the MOSAiC Expedition - Snow and Sea Ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the five MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties, and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in-situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models, and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice-ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

snow and sea ice↗
DOE OSTI2022

Catalytic DNA Polymerization Can Be Expedited by Active Product Release**

Abstract The sequence‐specific hybridization of DNA facilitates its use as a building block for designer nanoscale structures and reaction networks that perform computations. However, the strong binding energy of Watson–Crick base pairing that underlies this specificity also causes the DNA dehybridization rate to depend sensitively on sequence length and temperature. This strong dependency imposes stringent constraints on the design of multi‐step DNA reactions. Here we show how an ATP‐dependent helicase, Rep‐X, can drive specific dehybridization reactions at rates independent of sequence length, removing the constraints of equilibrium on DNA hybridization and dehybridization. To illustrate how this new capacity can speed up designed DNA reaction networks, we show that Rep‐X extends the range of conditions where the primer exchange reaction, which catalytically adds a domain provided by a hairpin template to a DNA substrate, proceeds rapidly.

Moerman, Pepijn G.↗
DOE OSTI2022

Catalytic DNA Polymerization Can Be Expedited by Active Product Release**

Abstract The sequence‐specific hybridization of DNA facilitates its use as a building block for designer nanoscale structures and reaction networks that perform computations. However, the strong binding energy of Watson–Crick base pairing that underlies this specificity also causes the DNA dehybridization rate to depend sensitively on sequence length and temperature. This strong dependency imposes stringent constraints on the design of multi‐step DNA reactions. Here we show how an ATP‐dependent helicase, Rep‐X, can drive specific dehybridization reactions at rates independent of sequence length, removing the constraints of equilibrium on DNA hybridization and dehybridization. To illustrate how this new capacity can speed up designed DNA reaction networks, we show that Rep‐X extends the range of conditions where the primer exchange reaction, which catalytically adds a domain provided by a hairpin template to a DNA substrate, proceeds rapidly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2023

Evolution of LIBS technology to mobile instrumentation for expediting firearm-related investigations at the laboratory and the crime scene

Gunshot residue (GSR) is one of the few forensic disciplines that lack accurate screening techniques. This study proposes using a mobile LIBS instrument to detect inorganic GSR and compares performance to a previously validated laboratory instrument. The mobile LIBS is designed with advanced configurations specifically for on-site GSR analysis, including a CMOS detector and a sampling chamber that holds up to six typical GSR collection devices with separate gas flow ports to prevent cross-contamination. A significant novelty of the portable instrument is its image magnification, which allows quick searching and visualization of GSR particle morphology. The single-particle imaging and elemental composition capability is one of a kind and offers superior confirmatory features for GSR. The mobile LIBS performance was evaluated for residues collected from the hands of shooters (100 samples) and non-shooters (200 background samples), analyzed sequentially by the mobile instrument and then the laboratory instrument. Accuracies better than 98.8% were obtained by both instruments, demonstrating their suitability for trace IGSR detection from skin specimens. Implementation of this methodology is anticipated to drastically speed up response times (i.e., from several hours per sample by standard SEM-EDS practice to a few minutes by LIBS). The screening methods can be easily incorporated into workflows to improve decision-making processes at the crime scene and laboratory settings, reduce backlogs, and improve case management.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Exocortex Network for AI-Augmented Human-Led Scientific Expedition

AI advances in science can be viewed along two main directions with a fluid boundary: enhancing efficiency through automation and smart tools to accelerate tasks that humans can already perform; and enabling exploration into uncharted territories and potentially toward AGI. These advances manifest in the AI cognitive core through the development and explainability of foundation models; in the physical embodiment of instruments and facilities; and in the integrated agency of AI workflows exemplified by the science exocortex. To address the role of humans in this evolving landscape, in this Perspective, we suggest a third direction: the development of personalized agents that form human-centered networks, supporting both efficiency and exploration while ensuring that AI remains aligned with human vision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗