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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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NASA NTRS2026

The Operational NASA Anomaly Gas Analyzer and Exploration Follow-Ons

The NASA Anomaly Gas Analyzer (AGA) is the culmination of nearly ten years of advancement of core technology originally developed by Vista Photonics through the Small Business Innovation Research (SBIR) program and expanded using NASA program funding. The AGA is a portable, battery operated, optical gas detection instrument for continuous monitoring of H 2 O, CO 2 , O 2 , CO, NH 3 , HCN, HF, HCl in the spacecraft environment. Real time, in-situ monitored, gas concentrations are communicated at 1 Hz through a built-in display on-orbit and additionally through a serial interface on the ground. The instruments function over the typical range of temperatures and pressures required for the spacecraft environment. A ten-year operational life is targeted. Seven AGA instruments are currently deployed on the International Space Station and two Orion-specific units are manifested for Artemis 2. A variation of the operational AGA is under development for lunar Human Landing Systems. A wider calibrated operating pressure envelope is required in this application. Otherwise, the instruments straightforwardly address lessons learned from the original AGA by improving aspects of thermal control, ruggedness, and ease-of-use. A subset of AGA-developed sensed gases (H 2 O, CO 2 , O 2 ) is the subject of the Primary Constituent Monitor (PCM) development for the Habitation and Logistics Outpost (HALO) destined for lunar orbit. Two PCMs will be installed in HALO as part of the environmental control loop where they will interface with vehicle power and communications. While the basic sensor techniques are unchanged from the AGA, these instruments have been radiation hardened to survive for many years in the harsh target environment. Current development status for these instruments will be presented along with recent developments of a naval submarine variant.

Air Monitoring↗
NASA NTRS2026

Coma Physics of an Interstellar Object: JWST Spatial-Spectral Mapping of 3I/ATLAS

We report a survey of molecular emission from cometary volatiles using the James Webb Space Telescope (JWST) toward interstellar object 3I/ATLAS carried out on UT 2025 December 22 and 23 at a heliocentric distance (\rh{}) of $2.37-2.41$ au. These measurements of CO, \ce{CO2}, \ce{H2O}, \ce{CH3OH}, and \ce{CH4} sampled molecular chemistry in 3I/ATLAS as it receded from its encounter with our Sun and entered the vicinity of the \ce{H2O} ice line --- the region between \rh{} = $2-3$ au where the temperature becomes too low for H$_2$O to vigorously sublime and CO and \ce{CO2} begin to control the overall activity. CO was the most abundant molecule, followed by \ce{H2O} and \ce{CO2}, whose molecular abundances with respect to CO were $(40.5\pm3.1)\%$ and ($41.6\pm0.3)\%$, respectively. This work presents spatial-spectral maps of column density and rotational temperature as a function of distance from the nucleus for all detected species. The spatial distributions of both quantities were highly anisotropic for the apolar species in the coma of 3I/ATLAS, yet were more nearly symmetric for the polar molecules. These results demonstrate how volatiles were segregated in the nucleus ices of 3I/ATLAS and reveal heating and cooling mechanisms in its coma. Derived maps of the ortho-to-para ratio (OPR) for \ce{H2O} were flat with increasing distance from the nucleus and consistent with a coma-averaged value $\mathrm{OPR}=2.7\pm0.2$, slightly less than the expected equilibrium value of three.

Nathan X Roth↗
NASA NTRS2026

Tracing the Source of Carbon Oxides on the Large Moons of Uranus

The Uranian moons Ariel, Umbriel, Titania, and Oberon are enriched in CO 2 mixed with CO, but the origin(s) of these carbon oxides, be they primarily native or radiolytic, remain(s) uncertain. Using data collected by NIRSpec on the James Webb Space Telescope (JWST), we measured the spectral signature of CO 2 and other carbon oxides to help disentangle these hypotheses. Through comparison to laboratory data, we find that many of the detected spectral features are consistent with CO 2 ice, including 12 CO2 scattering peaks (4.15–4.26 μm), multilobe 13 CO 2 bands (4.35–4.43 μm), and CO 2 biphonon and triphonon modes (4.80–5.25 μm). Our measurements show that CO 2 and CO are concentrated on the trailing hemispheres of the inner moons Ariel and Umbriel, potentially supporting a radiolytic production hypothesis, consistent with prior ground-based results. However, many of the identified spectral features are only observed in thick crystalline ice deposits measured in the laboratory, which may be difficult to form via radiolysis of carbon-bearing material mixed in icy regoliths. Similarly, the data exhibit weak 4.02 and 4.40 μm bands, hinting at the presence of carbonate minerals and 13 CO 2 clathrates, respectively, possibly formed in the interiors of these moons. Furthermore, JWST has revealed that CO 2 is widespread at Uranus, present in its system of rings, ring moons, and irregular satellites, consistent with its largest moons accreting CO 2 and other carbon oxides from the Uranian subnebula. We conclude that exposed carbon oxides are potentially native, with their surface distributions shaped by charged particle irradiation and seasonal sublimation–condensation cycles.

Ice spectroscopy↗
NASA NTRS1992

On the isotopic signature of recent solar-wind nitrogen

One of the most intriguing discoveries yielded by the Apollo samples was evidence pointing towards a significant long-term change in the composition of the sun. Such a change, of the size inferred from the lunar sample data, is inconsistent with present theories of solar evolution. Consequently, there is much interest in exploring this phenomenon as closely as possible, to determine exactly what compositional changes have taken place and whether those changes really did take place in the sun, or whether the cause lies elsewhere. The reason why we can use the moon to analyze the elements in the sun is that the sun emits a stream of ions, known as the solar wind, whose composition, on average, is believed to be the same as that in the surface regions of the sun. When the solar-wind ions hit the surface of the moon, many of them penetrate a short distance into the dust grains lying on the lunar surface. Thus, after a grain has sat on the lunar surface for a while, it has a rim of material that is partly lunar and partly solar in composition. For most chemical elements, the difference between lunar and solar composition is so sufficiently small that the solar elements cannot be detected, but for a handful of elements that are missing from the moon, their solar 'signature' can be observed in samples of lunar soil brought back by the astronauts. Among those elements is nitrogen, the most common element in the air we breathe, but very rare indeed on the moon. Our analytical techniques are not sophisticated enough yet to enable us to analyze individual lunar soil grains for nitrogen, much less to zero in on just the nitrogen in the surface of such a grain. Consequently we are forced to analyze samples consisting of many different grains, each of which could have experienced its own individual history. This makes it difficult to identify the nitrogen implanted in grain surfaces, and also to define the age of a sample.

Kim, Y.↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRS1984

Reports of Planetary Geology and Geophysics Program, 1984

Topics include outer planets and satellites; asteroids and comets; Venus; lunar origin and solar dynamics; cratering process; planetary interiors, petrology, and geochemistry; volcanic processes; aeolian processes and landforms; fluvial processes; geomorphology; periglacial and permafrost processes; remote sensing and regolith studies; structure, tectonics, and stratigraphy; geological mapping, cartography, and geodesy; and radar applications.

Henry E Holt↗
NASA NTRSDate not supplied

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

Xiaohua Pan↗
NASA NTRS1975

Planets and Satellites of the Outer Solar System, Asteroids, and Comets

The cosmogenic significance of outer solar system objects is evaluated with emphasis on planets and larger satellites of greatest biological interest; comets, asteroids, and the smaller satellites are also discussed. Principal physical and rudimentary photometric data for the five outer planets are presented in tables. Sufficient information on planetary motions is included for most calculations in physical planetology.

Ray L Newburn, Jr↗
NASA NTRSDate not supplied

NASA Small Spacecraft and Distributed Systems: Recent and Upcoming Technology Demonstrations and Development Efforts

NASA’s Small Spacecraft & Distributed Systems (SSDS) program strengthens U.S. ability to conduct unique missions by rapidly developing and demonstrating capabilities for SmallSat exploration, science, and commercial space. In collaboration with NASA Centers, other government agencies, commercial industry, and academia, SSDS advances next generation SmallSat technologies like power, processing, propulsion, communications, autonomous navigation, architectures (swarms), and applications (AI/ML/Edge Computing)—to extend missions beyond LEO into cislunar and planetary space. Various investment mechanisms exist for SSDS to select and fund projects that will ultimately advance NASA’s Moon to Mars Architecture. Presented here are the latest achievements and findings from recently completed SSDS projects, along with updates from ongoing efforts and planned future work. Successful missions like Starling and CAPSTONE continue to demonstrate their capability after several years on-orbit. Advancements in next generation swarm configurations are being implemented by Starling for space traffic monitoring and management applications. Findings from recent SSDS flight projects are discussed: DiskSat, a unique SmallSat platform alternative to canisterized nanosatellites, launched December 2025 and is gathering data; the PTD series of missions concluded in December 2025. Current SSDS efforts are focused on addressing NASA Shortfalls relating to rendezvous and proximity operations, neuromorphic computing, and space situational awareness.

Roger Hunter↗