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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Supervised Autonomous Assembly to Create and Evolve Persistent Assets

Supervised autonomous assembly (SAA) will create a paradigm shift in the planning and design of future persistent assets (PAs), both in near zero-g environments and on planetary surfaces. SAA refers to an autonomy approach that has the benefits of autonomous assembly as well as the benefits provided by a supervisor (operator) who is available to resolve unexpected situations. SAA provides both increased design freedom as well as reduced programmatic risk. SAA enables evolution of future PAs over decades as in-space operations transition from single purpose missions to creation of PAs, such as laboratories and experimental stations which more closely resembling terrestrial laboratories that can easily adapt and evolve to new missions leveraging repeated visits to the PA. The ability to evolve enables PAs to rapidly respond to changing objectives resulting from new questions as our understanding improves. A recently initiated National Aeronautics and Space Administration (NASA) project in the Space Technology Mission Directorate (STMD) Game Changing Development (GCD) Program called the Precision Assembled Space Structure (PASS), leverages the advantages of SAA to develop technologies that enable efficient creation and evolution of hexagonal topologies; both planar (example: fuel depots) and curved (examples: telescopes and shelters). PASS will be used to provide context for the philosophy and concepts discussed as well as the decision and selections made. PASS objectives are: a) Develop confidence in SAA and on-orbit servicing, assembly and manufacturing (OSAM) technologies by executing a test campaign that uses a path-to-flight autonomous precision assembly process directly applicable to future space telescopes. b) Test autonomous technologies including automated path planning and error recovery, to emphasize a robust approach that relies on generic robots and special purpose tools. c) Validate critical component models using a digital twin that includes the assembled primary mirror support structure and assembly process. A digital twin is a high-fidelity simulation of the asset capable of predicting the on-orbit performance. The paper concludes after identifying the critical need for a modest assembly flight experiment to validate and develop confidence in the SAA paradigm, thus accelerating adoption of the benefits described. SAA is a game changing paradigm that enhances the ability of an organization to infuse new technology through rapid evolution of PAs while leveraging OSAM technologies.

Structural Modeling↗
NASA NTRS2012

Designing Mission Operations for the Gravity Recovery and Interior Laboratory Mission

NASA's Gravity Recovery and Interior Laboratory (GRAIL) mission, to understand the internal structure and thermal evolution of the Moon, offered unique challenges to mission operations. From launch through end of mission, the twin GRAIL orbiters had to be operated in parallel. The journey to the Moon and into the low science orbit involved numerous maneuvers, planned on tight timelines, to ultimately place the orbiters into the required formation-flying configuration necessary. The baseline GRAIL mission is short, only 9 months in duration, but progressed quickly through seven very unique mission phases. Compressed into this short mission timeline, operations activities and maneuvers for both orbiters had to be planned and coordinated carefully. To prepare for these challenges, development of the GRAIL Mission Operations System began in 2008. Based on high heritage multi-mission operations developed by NASA's Jet Propulsion Laboratory and Lockheed Martin, the GRAIL mission operations system was adapted to meet the unique challenges posed by the GRAIL mission design. This paper describes GRAIL's system engineering development process for defining GRAIL's operations scenarios and generating requirements, tracing the evolution from operations concept through final design, implementation, and validation.

MOS↗
DOE OSTI2025

Hyporheic-zone Processes and Stream Oxygen Dynamics: Insights from a Multiscale Reactive Transport Model: Modeling Archive

This archive contains the data and Python scripts required to reproduce the analyses and figures in the study: Gomez-Velez, J. D., Rathore, S. S., Cohen, M. J., & Painter, S. L. (2025). Hyporheic-zone Processes and Stream Oxygen Dynamics: Insights from a Multiscale Reactive Transport Model. Submitted to Water Resources Research. The analysis utilizes the subgrid model Advection Dispersion Equation with Lagrangian Subgrids (ADELS) implemented in the Advanced Terrestrial Simulator (ATS; https://amanzi.github.io/ats/stable/). In this case, the ATS and Amanzi versions are (1) ATS version 1.5.1_f5ba18f8 and (2) Amanzi version 1.6-dev_53444cca4. The repository includes a Jupyter Notebook and the necessary data (Pandas DataFrames stored as pickle files) to generate the figures for the manuscript. Additionally, it contains Python scripts to create ATS input files, run the ATS simulations, and post-process the results. Finally, it provides routines for parameter estimation using the Single-Station Metabolism (SSM) model with the Differential Evolution Adaptive Metropolis (DREAM) Markov Chain Monte Carlo (MCMC) algorithm with ZS enhancements (DREAM-ZS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Efficient Berry phase calculation via adaptive variational quantum computing approach

We present an adaptive variational quantum algorithm to estimate the Berry phase accumulated by a nondegenerate ground state under cyclic, adiabatic evolution of a time-dependent Hamiltonian. Our method leverages cyclic adiabatic evolution of the Hamiltonian and employs adaptive variational quantum algorithms for state preparation and evolution, optimizing circuit efficiency while maintaining high accuracy. We benchmark our approach on dimerized Fermi–Hubbard chains with four sites, demonstrating precise Berry phase simulations in both noninteracting and interacting regimes. Our results show that circuit depths reach up to 106 layers for noninteracting systems and increase to 279 layers for interacting systems due to added complexity. In addition, we demonstrate the robustness of our scheme across a wide range of parameters governing adiabatic evolution and variational algorithms. These findings highlight the potential of adaptive variational quantum algorithms for advancing quantum simulations of topological materials and computing geometric phases in strongly correlated systems.

Mootz, Martin [Ames Laboratory (AMES), Ames, IA (U↗
NASA NTRS2000

Evolution of thermotolerance in hot spring cyanobacteria of the genus Synechococcus

The extension of ecological tolerance limits may be an important mechanism by which microorganisms adapt to novel environments, but it may come at the evolutionary cost of reduced performance under ancestral conditions. We combined a comparative physiological approach with phylogenetic analyses to study the evolution of thermotolerance in hot spring cyanobacteria of the genus Synechococcus. Among the 20 laboratory clones of Synechococcus isolated from collections made along an Oregon hot spring thermal gradient, four different 16S rRNA gene sequences were identified. Phylogenies constructed by using the sequence data indicated that the clones were polyphyletic but that three of the four sequence groups formed a clade. Differences in thermotolerance were observed for clones with different 16S rRNA gene sequences, and comparison of these physiological differences within a phylogenetic framework provided evidence that more thermotolerant lineages of Synechococcus evolved from less thermotolerant ancestors. The extension of the thermal limit in these bacteria was correlated with a reduction in the breadth of the temperature range for growth, which provides evidence that enhanced thermotolerance has come at the evolutionary cost of increased thermal specialization. This study illustrates the utility of using phylogenetic comparative methods to investigate how evolutionary processes have shaped historical patterns of ecological diversification in microorganisms.

Synechococcus Group/classification/growth & develo↗
DOE OSTI2025

Recent Progress in Solid‐State Lithium Batteries through Cathode Microstructure Engineering

A high‐performance cathode is necessary to realize the great potentials of solid‐state batteries such as high energy density and long cycle life. It is also needed to validate electrolyte performance, which is lacking. Currently, cathodes for solid‐state batteries are thinner and have lower cathode active material content than their lithium‐ion battery counterpart, resulting from insufficient conductivity and limiting the battery energy density. This review article provides an overview of recent development in cathode microstructures and their impact on battery properties, including compatibility between cathode and electrolyte, cathode architecture design, interface engineering, correlation between material properties, cathode processing approaches, and performance, as well as the advanced characterization methods used to understand the above correlations . Some perspectives on future development are shared including utilizing in situ and operando characterization tools to better understand dynamic evolution of the cathode/electrolyte interface, adapting artificial intelligence and machine learning to design and optimize cathode structures. The article is aimed to promote research interest on cathode development and advance solid‐state battery technologies.

cathode engineering↗
NASA NTRS1989

Ecological considerations for possible Martian biota

Current climatic and geological evidence suggests that, like early Earth, conditions on ancient Mars may also have been favorable for the origin and evolution of life. The primordial atmospheres of the two planets were quite similar, composed primarily of CO2, N2, and water vapor at a total atmospheric pressure of approximately 1 bar. Each of these gases are important for the evolution of biological systems. With the exception of nitrogen, there seems to have been a sufficient supply of the biogenic elements C, H, O, P, and S (CHOPS) on early Mars for life to have evolved. It was postulated that primordial Mars contained only 18 mb of nitrogen in the form of N2 given that only fixed nitrogen is utilized by living systems. Laboratory tests performed at a total pressure of 1 bar and various partial pressures of dinitrogen (pN2 1-780 mb) show that nitrogen fixing organisms grow at pN2's of 18 mb or less, although the biomass and growth rates are decreased. The calcualted in vivo Km's ranged from 46 mb to 130 mb. If organisms adapted on Earth to a pH2 of 780 mb are capable of growing at these low partial pressures, it is conceivable that nitrogen was not the limiting factor in the evolution of life on early Mars.

Klingler, June M.↗
NASA NTRS2018

Enabling Experimental Evolution: Multi-Parameter Sensor System Integration into a Culture/Stressor Biofluidics System

Experimental evolution (EE) exposes microbial communities to ecological stressors, simulating dynamics up to near-extinction events. Combined with comparative sequencing and other molecular tools, such data can inform the genetic and other biological mechanisms underlying extremophile adaptation, and other observed effects. Automating this type of experiment using biofluidics can mitigate many traditional obstacles, including delays in assay results and environment adjustment and the need for many replicates. A first-generation device for automating EE procedures, the Automated Adaptive Directed Evolution Chamber (AADEC), was developed at NASA Ames. UV-C radiation was the stressor, an LED-photodiode array measured optical density, magnetic agitation and peristaltic pump systems ensured nutrient availability, and Arduino microcontrollers provided control. Escherichia coli in LB kanamycin media was used for testing and performance verification. A manual laboratory procedure with timed exposure to UV-C was performed to typify tolerance acquisition. Approximately a 106 factor increase in survival ratio was recorded over multiple iterations. Currently, a second-generation device is being developed integrating more real-time sensors: redox potential (ORP), indicating available/consumed metabolic energy; dissolved oxygen (DO), indicating aerobic/anaerobic growth; pH, indicating metabolic products; and electrical conductivity (EC), another indicator of metabolic products. The EC sensor system was constructed and calibrated in-house and matched commercial sensors in the required range. A Raspberry Pi computer automated the electrical system, allowing real-time data acquisition. The fluidics card was made of CNC-milled polycarbonate for biocompatibility. Each sensor parameter can also be used as a selection pressure alone or in combination with others to create extreme microbial environments. As a proof of concept, this work demonstrated sensor operation in one pair of growth-sensor chambers. It can be expanded to a multi-chamber system to enable inter-culture comparisons and multi-population studies. The prior Arduino system will be ported to the RPi system. Future stressors to be added include thermal, reactive oxygen species, and varying nutrient availability.

Govinda Raj, Chinmayee↗
NASA NTRS2017

The Evolution of Planetary Protection Implementation on Mars Landed Missions

NASA has developed requirements dedicated to the prevention of forward and backward contamination during space exploration. Historically, international agreements provided guidelines to prevent contamination of the Moon and other celestial bodies, as well as the Earth (e.g., sample return missions). The UN Outer Space Treaty was established in 1967 and the Committee on Space Research (COSPAR) maintains a planetary protection policy complying with Article IX of this treaty. By avoiding forward contamination, the integrity of scientific exploration is preserved. Planetary Protection mission requirements are levied on missions to control contamination. These requirements are dependent on the science of the mission and on the celestial bodies encountered or targeted along the way. Consequently, categories are assigned to missions, and specific implementation plans are developed to meet the planetary protection requirements. NASA missions have evolved over time with increasingly more demanding scientific objectives and more complex flight systems to achieve those objectives and, thus, planetary protection methods and processes used for implementation have become much more intricate, complicated, and challenging. Here, we will portray the evolution of planetary protection implementation at JPL in several important areas throughout the course of NASA sponsored robotic Mars lander or rover missions, starting from Mars Pathfinder through the beginning of Mars 2020. Highlighted in the discussion will be process changes in planetary protection requirements development and flow down. Development and implementation of new and improved methods used in the reduction of spacecraft bioburden will be discussed as well as approaches and challenges that come along with setting up remote laboratories to perform bioassays. The consequences and forward planning of delays on missions will be highlighted as well as lessons learned on the impact of communication and training in achieving planetary protection requirements. The evolution of methods used for the detection of microbial bioburden on spacecraft hardware will be considered. These methods use standard microbiology as well as the adaptation of advances in biotechnology, molecular biology, and bioinformatics. Technical approaches developed for the prevention of contamination and recontamination of hardware during Assembly, Test, and Launch Operations will be discussed.

Kazarians, Gayane A.↗
DOE OSTI2025

Krylov Subspace Methods for Quantum Dynamics with Time-Dependent Generators

Krylov subspace methods in quantum dynamics identify the minimal subspace in which a process unfolds. To date, their use is restricted to time evolutions governed by time-independent generators. Here, we introduce a generalization valid for driven quantum systems governed by a time-dependent Hamiltonian that maps the evolution to a diffusion problem in a one-dimensional lattice with nearest-neighbor hopping probabilities that are inhomogeneous and time dependent. This representation is used to establish a novel class of fundamental limits to the quantum speed of evolution and operator growth. We also discuss generalizations of the algorithm, adapted to discretized time evolutions and periodic Hamiltonians, with applications to many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Functional Nano-to-Microstructures by Jet Printing and Direct Ink Writing

Jet-based printing techniques and direct ink writing have emerged as complementary, convergent technologies serving as key platforms in additive manufacturing for functional nano- to microscale architectures. This review highlights how these approaches enable fine feature resolution and three-dimensional structures in advanced electronics and biointerfacing applications. The interplay of fluid mechanics, viscoelastic ink rheology, droplet–substrate interactions, and drying dynamics is examined as a critical determinant of printing fidelity. Application-focused case studies, from flexible thin-film transistors to bioprinted artificial tissues, demonstrate how precise structural control via printing translates to enhanced device performance and new functionality in electronic and biological systems. Finally, we discuss the challenges and future opportunities driving the evolution of these printing platforms toward autonomous, adaptive, and intelligent manufacturing systems.

Luo, Junchen [Sichuan Univ. of Arts and Science (C↗
DOE OSTI2026

Climate change drives convergent evolution of root traits on Sky Island climate relicts

Roots are essential to the strategies plants use to survive in variable environments, yet we know little of how they vary within species. Experimental conditions demonstrate that intraspecific plant root traits respond strongly to variation in the environment; however, it is unclear when these responses can be characterized as evolution in response to selective pressures of climate change over many generations. Sky Islands are model, natural climate relict ecosystems to examine climate-change driven evolution. Utilizing a common garden with replicate genotypes of Populus angustifolia (Narrowleaf cottonwood) from six Sky Island (SI) populations and nine adjacent Mountain Chain (MC) populations across three genetic provenances, we hypothesized that SI root traits have diverged due to historical isolation in warmer, drier climates. When grown in common conditions, populations originating on SI’s showed convergent evolution across three distinct genetic provenances, which was characterized by 44.16% decreased total root length, 42.64% decreased average root volume, 43.31% decreased root surface area, and significantly less root trait variation, relative to adjacent mountain chains. Convergent evolution of root traits from trees originating on SI’s is correlated with changes in mean annual precipitation and potential evapotranspiration in the field over the past ~ 125 years. These results demonstrate a consistent pattern in root trait evolution at the landscape scale and the role of climate on the evolution of root traits in a genetic and geographic context relevant to climate change.

Convergent evolution↗
DOE OSTI2025

Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation.

02 PETROLEUM↗
NASA NTRSDate not supplied

Automating the Study of Microbial Adaptation Dynamics on and off the ISS

The International Space Station (ISS) not only serves as a unique environment for humans, but also the microorganisms that join alongside. Many microbes present on the spacecraft arrive via humans, and as they interact with different surfaces they begin to inhabit those locations. Much like how human health has shown to be impacted by these extreme environments, microbial viability and response to stress also changes. Experimental evolution (EE) can aid in studying how microbes’ growth and activity changes within the ISS environments by applying controlled stressors to microbial cultures and monitoring their response over generations. EE studies are commonly done manually in laboratories, but, with multiple environmental variables to measure and adjust, it becomes highly labor-intensive, prone to human error, and challenging to scale. A multipurpose automated EE system named the AADEC has been developed to address these problems. This system integrates multiple sensors into a single fluidic chamber using UV-C flux, temperature, and media composition as stressors. AADEC contains five sensors: oxidation-reduction potential, electrical conductivity, pH, dissolved oxygen, and optical density. On their own, each is able to provide certain information on growth rate or metabolism; together, they show in detail how stressors affect life. AADEC studies can be conducted on Earth and repeated aboard the ISS to see how behavior changes when exposed to space mission stressors such as microgravity and radiation. AADEC’s auxiliary systems include peristaltic pumps for media exchange, magnetic rods for agitation, and a Raspberry Pi microprocessor to monitor, store, and adjust stressor levels real-time. This allows researchers to gather information within rapid generations, data and accuracy which is challenging to achieve through manual studies. With further miniaturization and automation, such as a more robust single-piece fluidics card, AADEC has the potential to be developed as a spacecraft payload. Support: NASA Ames CIF Award

Automating↗
DOE OSTI2025

Resilient Entanglement Distribution in a Multihop Quantum Network

The evolution of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions. We introduce multihop quantum networks to improve network reach and resilience by enabling quantum communications across intermediate nodes, thus broadening network connectivity and increasing scalability. We present multihop two-qubit polarization-entanglement distribution within a quantum network at the Oak Ridge National Laboratory campus. Our system uses wavelength-selective switches for adaptive bandwidth management on a software-defined quantum network that integrates a quantum data plane with classical data and control planes, creating a flexible, reconfigurable mesh. Our network distributes entanglement across six nodes within three subnetworks, each located in a separate building, optimizing quantum state fidelity and transmission rate through adaptive resource management. Additionally, we demonstrate the network's resilience by implementing a link recovery approach that monitors and reroutes quantum resources to maintain service continuity despite link failures—paving the way for scalable and reliable quantum networking infrastructures.

Alshowkan, Muneer [Oak Ridge National Laboratory (↗
DOE OSTI2025

Development of Automated Atom Probe Tomography capability to study the influence of applied voltage and laser power on the final apparent composition of the analyzed specimen

This study presents the development and implementation of an autonomous Bayesian optimization (BO) framework for controlling and optimizing experimental parameters in Atom Probe Tomography (APT). Using commercial silicon needle samples as a benchmark system, we demonstrate that BO can efficiently navigate the complex parameter space of voltage and laser power to achieve target charge state ratios (specifically Si + /(Si + +Si 2+ )) with minimal experimental evaluations. Our implementation integrates Gaussian Process modeling with the CAMECA atom probe control framework, enabling autonomous adjustment of experimental conditions in real-time. Results show that the algorithm successfully converges to target ratios under different scenarios: maintaining a reference ratio, increasing the ratio (favoring Si 1+ ), and decreasing the ratio (favoring Si 2+ ). The system adapts to specimen evolution during analysis, compensating for changes in apex geometry while maintaining optimization targets. This work establishes a proof of concept for AI-driven optimization in APT, addressing the traditional challenges of manual parameter tuning and paving the way for applications to more complex materials where compositional accuracy is critical.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Coupling Microstructural Evolution Simulations to Material Property Degradation Predictions for Plasma-Facing Materials

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500 C to 1500 C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 D/m^2-s. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗