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194 records · Page 9

Lithium Nucleation of Anode-Free Solid-State Batteries with a Dry Compressible Interlayer

Anode-free lithium batteries offer promising advantages, including increased energy density and the ability to address common mechanistic failures within the cell, thus increasing safety. One way that can make this possible is to control lithium nucleation. This could be achieved by reducing the overpotential required and implanting lithophilic nucleation sites in an anodic interlayer to aid in lithium ion transport and plating. The concept has been utilized in solid-state battery systems where electrolytes are solids with further improved safety and structural longevity. In this presentation, we discuss the use of a silver-holey graphene-based anodic interlayer that can be fabricated via direct dry compression without the use of solvent or binder. The addition of silver to the holey graphene matrix creates a route to lithium plating via a lower-energy intermediate. This idea is supported by the presence of a lithium-silver alloy that forms during the activation step. It is understood that the embedded silver acts as a nucleation site for lithium ions, thereby assisting in even plating. This control, combined with the added cushion of the holey graphene itself, can help reduce dendrite formation, ultimately increasing the safety and lifetime of the solid-state batteries.

Solid state batteries

Subscale Hardware-In-The-Loop Results for Hybrid Electric Turbofan Controls Use Cases

NASA is investigating hybrid electric turbine engine systems for commercial transport aircraft due to the potentially significant improvements hybrid electric technology offers in performance, fuel consumption, and operational and design flexibility. Recently, the technology has been tested at full scale in partnership with industry and advanced to Technology Readiness Level 4. This presentation will focus on a recent subscale hardware-in-the-loop test of an open source turbofan engine model developed by NASA. The Advanced Geared Turbofan 30,000 lbf – electrified (AGTF30-e) engine is used as a reference model to demonstrate control system design and use cases for an example mild hybrid electric system with no large-scale energy storage. This model is run in real-time in NASA’s Hybrid Propulsion Emulation Rig (HyPER) and is used to drive an emulation of the turbomachinery system using subscale electric machines. This dynamic scaled shaft emulation interacts with a subscale (<100 kW) hybrid system consisting of electric machines, motor controllers, and a programmable electronic load. Specific use cases demonstrated include the use of Turbine Electrified Energy Management to improve operation during transients, megawatt-scale power extraction from the AGTF30-e, and power transfer between engine spools. Results related to the effectiveness of hybrid systems are qualitatively compared to results from industry testing.

Hybrid

Ultrasonic Washer–Dryer System for Space Habitats: Design Upgrades and Parabolic Flight Readiness

Clothing makes up nearly 25% of all non-food supplies sent to the International Space Station (ISS). To support sustainable human missions in deep space, NASA’s Life Support and Habitation Systems Focus Area looks to advance technologies to support and improve logistics. Our team is creating a compact ultrasonic clothing washer/dryer system that bypasses traditional limitations and is suitable for space. Thermal drying uses a lot of energy to evaporate water, while our ultrasonic drying method offers a quicker, more efficient alternative. It uses piezoelectric transducers to vibrate textiles at the micron scale, mechanically removing water as cold mist rather than evaporating it. This speeds up drying and reduces energy use, no matter the fabric makeup. This paper details recent upgrades to a full-scale ultrasonic washer–dryer system, readying it for parabolic flight testing. Improvements include an enhanced human–machine interface, better packaging, and optimized performance and control. We also present extensive pre-flight ground tests conducted to ensure reliability and identify potential risks. Collaborating with P&G, we report preliminary cleaning tests using various detergents. These results lay a crucial foundation for the laundry system designed specifically for space. By cutting clothing-related payload and waste by over 97%, this technology supports long-term human exploration missions on the ISS, the Moon, Mars, and beyond.

Ultrasonic

Characterization of the Mechanical Trash Compactor Bag Removal Force

Managing trash accumulation is a critical challenge in human space explorations since the trash volume and mass can significantly influence launch cost and operational efficiency. This study aids the development of a Mechanical Trash Compactor (Mpactor) by testing the removal force required to remove a “trash bag” from the Mpactor. To do this, a Pneumatic Pressure Mandrel System was designed to simulate the normal force trash would exerts on the trash bag and subsequently the sidewalls of the Mpactor. For various levels of sidewall normal force, the removal force required to remove the simulated trash bag from the Mpactor compaction chamber was measured. This testing is used to identify optimal bag materials. Additionally, testing was done on both the native stainless steel walls of the compaction chamber as well as the walls after being Teflon lined. Split-plot ANOVA, simple main effects analysis, Holm’s post hoc comparisons for interaction, and sensitivity analyses were conducted to assess the influence of trash bag and sidewall materials on the required removal force. It was found that Teflon reduced the force required to remove the trash bag as well as reduced material-dependent variation in removal force. Testing showed that Teflon reduced the required removal force with a Polyvinylidene Fluoride , an unidentified Ethylene acrylate - Low Density Polyethylene copolymer, and a Polyvinyl Chloride all requiring significantly less removal force than the other materials tested.

pressure mandrel

Studies of Flame Structure in Microgravity

The present research endeavor is concerned with gaining fundamental understanding of the configuration, structure, and dynamics of laminar premixed and diffusion flames under conditions of negligible effects of gravity. Of particular interest is the potential to establish and hence study the properties of spherically- and cylindrically-symmetric flames and their response to external forces not related to gravity. For example, in an earlier experimental study of the burner-stabilized cylindrical premixed flames, the possibility of flame stabilization through flow divergence was established, while the resulting one-dimensional, adiabatic, stretchless flame also allowed an accurate means of determining the laminar flame speeds of combustible mixtures. We have recently extended our studies of the flame structure in microgravity along the following directions: (1) Analysis of the dynamics of spherical premixed flames; (2) Analysis of the spreading of cylindrical diffusion flames; (3) Experimental observation of an interesting dual luminous zone structure of a steady-state, microbuoyancy, spherical diffusion flame of air burning in a hydrogen/methane mixture environment, and its subsequent quantification through computational simulation with detailed chemistry and transport; (4) Experimental quantification of the unsteady growth of a spherical diffusion flame; and (5) Computational simulation of stretched, diffusionally-imbalanced premixed flames near and beyond the conventional limits of flammability, and the substantiation of the concept of extended limits of flammability. Motivation and results of these investigations are individually discussed.

C K Law

Space Shuttle Main Engine Propellant Path Leak Detection Using Sequential Image Processing

Initial research in this study using theoretical radiation transport models established that the occurrence of a leak is accompanies by a sudden but sustained change in intensity in a given region of an image. In this phase, temporal processing of video images on a frame-by-frame basis was used to detect leaks within a given field of view. The leak detection algorithm developed in this study consists of a digital highpass filter cascaded with a moving average filter. The absolute value of the resulting discrete sequence is then taken and compared to a threshold value to produce the binary leak/no leak decision at each point in the image. Alternatively, averaging over the full frame of the output image produces a single time-varying mean value estimate that is indicative of the intensity and extent of a leak. Laboratory experiments were conducted in which artificially created leaks on a simulated SSME background were produced and recorded from a visible wavelength video camera. This data was processed frame-by-frame over the time interval of interest using an image processor implementation of the leak detection algorithm. In addition, a 20 second video sequence of an actual SSME failure was analyzed using this technique. The resulting output image sequences and plots of the full frame mean value versus time verify the effectiveness of the system.

L Montgomery Smith

The Future of in-Situ Sequencing-Based Microbial Monitoring: Development of a Shelf-Stable Method for Artemis and Beyond

Microbial monitoring onboard the International Space Station (ISS) is essential for assessing the efficiency of the Environmental Control and Life Support Systems (ECLSS) and providing insight into potential risk to both crew and spacecraft. Historically, this monitoring required the need to culture organisms onboard, return these cultures to Earth, and then complete the identifications, a process that would take months. Over the past decade, and through numerous payloads, advances in molecular biology have enabled in-flight microbial identifications using nanopore sequencing. The swab-to-sequencer method resulting from these efforts was transitioned from research to operations for microbial monitoring under the Crew Health Care Systems (CHeCS) BioMole. Collectively, these accomplishments have propelled the swab-to-sequencer method to be selected as the Microbial Surface Monitor (MSM) for Gateway, as well as a payload on Artemis IV. However, the lack of cold stowage availability for Artemis requires modifications to the entire method due to the thermal instability of the reagents required for sample preparation. To achieve this, new development, optimization, and validations were undertaken. Key considerations included enzyme concentration, buffer compatibility, and equal or enhanced sensitivity and specificity. At each step, thorough side-by-side comparisons with the current ISS method were performed. The development of a robust shelf-stable method will ensure continued sequencing-based microbial monitoring for Artemis and beyond, providing data in near real-time, enhancing risk response time, and yielding clear insight into the microbiome of spacecraft.

Christian G Mena

Integrated Computational Materials Engineering (ICME) Capability Maturity Levels for Ecosystems Enabling Digital Transformation

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

ICME

NASA Microgravity Combustion Science Program

Combustion is a key element of many critical technologies used by contemporary society. For example, electric power production, home heating, surface and air transportation, space propulsion, and materials synthesis all utilize combustion as a source of energy. Yet, although combustion technology is vital to our standard of living, it poses great challenges to maintaining a habitable environment. For example, pollutants, atmospheric change and global warming, unwanted fires and explosions, and the incineration of hazardous wastes are major problem areas which would benefit from improved understanding of combustion. Effects of gravitational forces impede combustion studies more than most other areas of science since combustion involves production of high-temperature gases whose low density results in buoyant motion, vastly complicating the execution and interpretation of experiments. Effects of buoyancy are so ubiquitous that their enormous negative impact on the rational development of combustion science is generally not recognized. Buoyant motion also triggers the onset of turbulence, yielding complicating unsteady effects. Finally, gravity forces cause particles and drops to settle, inhibiting deconvoluted studies of heterogeneous flames important to furnace, incineration and power generation technologies. Thus, effects of buoyancy have seriously limited our capabilities to carry out 'clean' experiments needed for fundamental understanding of flame phenomena. Combustion scientists can use microgravity to simplify the study of many combustion processes, allowing fresh insights into important problems via a deeper understanding of elemental phenomena also found in Earth-based combustion processes and to additionally provide valuable information concerning how fires behave in microgravity and how fire safety on spacecraft can be enhanced.

Merrill K King

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling

Microgravity Game Changers: Recent Accomplishments of Microgravity Assisted Materials Experiments on the ISS

This paper discusses important new materials, devices and technology produced in low-Earth orbit (LEO) that have been developed with funding from NASA’s In Space Production Applications (InSPA) Portfolio. In coordination with the International Space Station (ISS) National Laboratory, InSPA supports innovative small businesses and other research & development organizations to develop microgravity-assisted applications that make significant impacts in a wide variety of industries on Earth. These technology development efforts are using the microgravity of orbital space to overcome gravity-induced defects on Earth to create high-value products for terrestrial markets in the areas of semiconductors, pharmaceuticals, and communications. Through 24/7/365 operation on ISS for 25 years, coupled with commercial space transportation and services to, in, and from orbit, the microgravity environment has enabled pioneering science and significant commercial applications, including novel medical advances. World-leading researchers, innovators, and space payload developers provide compelling evidence through demonstrations on the ISS that materials processing in microgravity improves outcomes for a wide range of materials and products at all levels of the organization of matter, from quantum entities such as Bose-Einstein condensates, to crystals, alloys, composites, thin films, and photonics, to stem cells, medical diagnoses, medical treatments, and medical devices for humans. As of 2026, numerous examples proved by demonstration on ISS show the potential of these technologies in various fields. In this paper, a summary of four important experiments is presented.

microgravity medical devices

Stationary Testing of a Pulse Tube Cryocooler Designed to Rotate at 6800 RPM with the Superconducting Rotor of an Electric Machine

Electrified aircraft are being developed to increase the efficiency and reduce the cost of operating subsonic transport aircraft. Achieving a substantial impact necessitates focusing on single- and twin-aisle aircraft which use propulsion systems with >20 MW ratings. For these aircraft, multi-MW superconducting electric machines are being developed due to their high specific power and high efficiency. Most of these electric machines employ superconductors on their rotor, thereby requiring cryogenic cooling of the rotating system. An attractive solution is to conductively cool the rotor using a cryocooler that rotates with the rotor. NASA has been developing a 50 K pulse tube cryocooler for its High Efficiency Megawatt Motor (HEMM) that can operate while rotating at up to 6,800 rpm. The first non-rotating tests of the fully assembled HEMM cryocooler have been completed, and initial testing has yielded a no-load temperature of 112 K. A small helium leak into the vacuum system has been identified as the major bottleneck to performance, and based on measurements with the motor turned off there is an observed vacuum-side heat leak of approximately 30 W at 115 K. Immediate next steps for testing are to locate and mitigate the apparent helium leaks into the vacuum chamber. Thus far, compressor performance and the apparent heat lift after correcting for the heat leak are on par with expectations.

Electrified Aircraft Propulsion

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi

Micromechanical Design of Carbon Nanotube Ribbon Reinforced Polymer Composite Materials

Lightweight materials are an important component of the design of aerospace structures. Carbon nanotube materials have been considered for this purpose due to the strength and stiffness of individual nanotubes, and the commercial availability of bulk formats such as fibers. These fibers can have a ribbon cross section which results in a different design space for their composites relative to traditional reinforcements which have a round cross section. This work applies brick-and-mortar micromechanical models and classical lamination theory with an inverse approach to investigate the design space of these composites. Using this approach, the influences of fiber geometry and axial and transverse mechanical properties are mapped. Finally, a sensitivity study is performed and the relative impacts of ±10% variations in the constituent material and geometric properties are ranked. Lamina axial moduli were found to range from a maximum of 3x to 1x minimum relative to a target quasi-isotropic laminate modulus depending on the anisotropy and shear modulus of the lamina. The fiber targets depended strongly on the fiber volume fraction in the lamina and the fiber axial modulus target was found to range from 4.8x to 3.2x the quasi-isotropic laminate target. The sensitivity analysis found that the largest driver of performance was the volume fraction, followed by the fiber axial modulus. While bio-based brick-and-mortar composites, such as nacre, can benefit from reinforcement aspect ratios above 10, for carbon nanotube ribbon (or carbon fiber)/polymer composites the sensitivity study indicated that the optimal cross-sectional aspect ratio was relatively smaller, potentially less than three.

Micromechanics

The Relevance of the VIPER Mission to NASA’s Artemis Human Exploration of the Moon

NASA has ambitious plans to send astronaut crews to the south polar region of the Moon and explore this lunar terrain with humans for the first time through a series of Artemis missions. Artemis will provide economic benefits, drive technological advancement, and inspire the next generation of explorers. Simultaneously, Artemis presents a unique opportunity for humans to conduct high-priority planetary science in situ in the south polar region of the Moon. Prior to human exploration, landed robotic precursor investigations can provide valuable information to reduce risk and maximize discovery and productivity of subsequent crewed missions. NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) is a robotic mission designed to explore and characterize the lunar south polar region prior to crewed Artemis landed missions. VIPER is designed to explore multiple thermal regimes to characterize the lunar polar environment and regolith properties, and to explore for surface and subsurface ices. Each of these objectives are key for both scientific investigation and to detect and assess volatile deposits to support in situ resource utilization (ISRU) and a sustained human presence on the Moon. VIPER would also operate with a real-time mission operations architecture with relevance and feed-forward to Artemis crewed operations on the lunar surface. VIPER would provide key information regarding the lunar environment, scientific exploration, technologies, and real-time operations to optimize the valuable surface exploration time of Artemis crew members while simultaneously reducing risk to crew and increasing health and human safety on the lunar surface.

Jennifer L Heldmann

Review of European Microgravity Measurements

AA In a French/Russion cooperation, CNES developed a microgravity detection system for analyzing the Mir space station micro-g-environment for the first time. European efforts to characterize the microgravity (1/9) environment within a space laboratory began in the late seventies with the design of the First Spacelab Mission SL-1. Its Material Science Double Rack was the first payload element to carry its own tri-axial acceleration package. Even though incapable for any frequency analysis, the data provided a wealth of novel information for optimal experiment and hardware design and operations for missions to come. Theoretical investigations under ESA contract demonstrated the significance of the detailed knowledge of micro-g data for a thorough experiment analysis. They especially revealed the high sensitivity of numerous phenomena to low frequency acceleration. Accordingly, the payloads of the Spacelab missions D-1 and D-2 were furnished with state-of-the-art detection systems to ensure frequency analysis between 0.1 and 100 Hz. The Microgravity Measurement Assembly (MMA) of D-2 was a centralized system comprising fixed installed as well as mobile tri-axial packages showing real-time data processing and transmission to ground. ESA's free flyer EURECA carried a system for continuous measurement over the entire mission. All EURECA subsystems and experimental facilities had to meet tough requirements defining the upper acceleration limits. In a French/Russion cooperation, CNES developed a mi crogravity detection system for analyzing the Mir space station micro-g-environment for the first time. An approach to get access to low frequency acceleration between 0 and 0.02 Hz will be realized by QSAM (Quasi-steady Acceleration Measurement) on IML-2, complementary to the NASA system Spacelab Acceleration Measurement System SAMS. A second flight of QSAM is planned for the Russian free flyer FOTON.

H Hamacher

A Parametric Battery Model for the Conceptual Design of Electric Aircraft

Electric aircraft conceptual designs usually have an assumed specific power and specific energy for the aircraft’s energy storage system that dictates the total capacity and the peak power available. The specific energy and power are inherently linked to the state-of-charge and discharge rate from which they were derived; therefore, they will not directly correspond to the diverse circumstances encountered in various aircraft missions and flight segments. These parameters turn the battery system into a black box, disregard potential electrical restrictions, and disallow the aircraft and battery to be optimized as a system. Peering into this box, this study highlights the importance of incorporating a parametric battery model into the conceptual design workflow by splitting high-level terms such as power into voltage and current and investigating their variability during the discharge process. Through the modeling of these more detailed parameters, this methodology shows the feasibility of using low states-of-charge for contingency operations, including the end of the reserve mission, expanding the amount of usable capacity for electric aircraft. Stark differences in aircraft capabilities can arise between varying fidelity battery models due to late-mission, high-power flight operations. This parametric battery model effectively captures these differences by evaluating the limitations that arise within the individual battery cells and the aircraft powertrain. This paper shows that the unusable charge of a battery is set by the balked landing power requirement and can realistically range from 5% to 44% based on assumptions. This sets the analog to unusable fuel capacity in aircraft with liquid fuel systems. Needing only aircraft- and mission-level inputs and only seconds of run time, this model is a prime fit for the fast, accurate exploration of the electric aircraft conceptual design space.

Battery

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization