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NASA NTRS2005

Microbiological Characterization and Concerns of the International Space Station Internal Active Thermal Control System

Since January 1999, the chemical the International Space Station Thermal Control System (IATCS) and microbial state of (ISS) Internal Active fluid has been monitored by analysis of samples returned to Earth. Key chemical parameters have changed over time, including a drop in pH from the specified 9.5 +/- 0.5 ta = 58.4, an increase in the level of total inorganic carbon (TIC), total organic carbon (TOC) and dissolved nickel (Ni) in the fluid, and a decrease in the phosphate (PO,) level. In addition, silver (AS) ion levels in the fluid decreased rapidly as Ag deposited on internal metallic surfaces of the system. The lack of available Ag ions coupled with changes in the fluid chemistry has resulted in a favorable environment for microbial growth. Counts of heterotrophic bacteria have increased from less than 10 colony-forming units (CFUs)/l00 mL to l0(exp 6) to l0(exp 7) CFUs/100 mL. The increase of the microbial population is of concern because uncontrolled microbiological growth in the IATCS can contribute to deterioration in the performance of critical components within the system and potentially impact human health if opportunistic pathogens become established and escape into the cabin atmosphere. Micro-organisms can potentially degrade the coolant chemistry; attach to surfaces and form biofilms; lead to biofouling of filters, tubing, and pumps; decrease flow rates; reduce heat transfer; initiate and accelerate corrosion; and enhance mineral scale formation. The micro- biological data from the ISS IATCS fluid, and approaches to addressing the concerns, are summarized in this paper.

Monsi C Roman↗

NASA NTRSDate not supplied

Early Progress Toward the Feasibility of the Centrifugal Nuclear Thermal Rocket

The Centrifugal Nuclear Thermal Rocket (CNTR) is a Nuclear Thermal Propulsion (NTP) concept designed to heat propellant directly by the reactor fuel. The primary difference between the CNTR concept and traditional NTP systems is that rather than using traditional solid fuel elements, the CNTR uses liquid fuel with the liquid contained in rotating cylinders by centrifugal force. If the concept can be successfully realized, the CNTR would have a high specific impulse (~1800 seconds) at high thrust, which may enable (i) viable near-term human Mars exploration by reducing round-trip times to 420 days and (ii) direct injection orbits for scientific missions to the Solar System outer planets and potentially Kuiper Belt objects. The CNTR could also use storable propellants such as ammonia, methane, or hydrazine at an Isp of ~900 seconds, enabling long-term in-space storage of a dormant system. Significant engineering challenges must be addressed to establish the technical viability of the CNTR. Research is presently underway to determine resolutions for these engineering challenges. In particular, research has begun on the analytical modeling and simulation of the two-phase heat transfer between the liquid metallic uranium fuel and the gaseous propellant. A paper was presented at the 2021 IAC which described these challenges and the study plan to address them. This paper describes the analytical and experimental progress to date toward resolving these challenges and establishing the engineering feasibility of the CNTR technology.

Nuclear Thermal Propulsion↗

NASA NTRS2024

NIAC Phase I Final Report Lunar South Pole Oxygen Pipeline (LSPOP)

The Lunar South Pole Oxygen Pipeline (LSPOP) Phase I NIAC, is to analyze the feasibility of constructing a pipeline at the Moon’s South Pole for transporting gaseous oxygen from point of generation to point of use. A lunar pipeline has never been pursued and will revolutionize lunar surface operations for the Artemis program and reduce cost and risk. Our starting concept is for a 5 km pipeline to transport oxygen gas from an oxygen production source, for example from our molten regolith electrolysis (MRE, currently at TRL 5, see references[1],[2],[3],[4],[5],[6] for technical discussions of the MRE process) extraction site, or any other source, to an oxygen storage/liquification plant near a lunar base. The pipeline is designed to: 1) be constructed robotically from regolith-derived metals with minimal material transferred from Earth, 2) be repairable robotically, 3) have an oxygen flow rate of ~2 kg/hour, which is commensurate with the NASA initial projected need of 10,000 kg/year, 4) operate with minimal power over the lifetime of the pipeline, with a goal of high operational reliability and the ability to survive in the lunar environment for > 10 years. We compare this concept to a pipeline concept with pipe produced on Earth and then transported to and assembled on the Moon. Both approaches will use earth-based compressors, valves, and fittings which are integrated into the pipeline on the Moon.

NIAC Phase I↗

DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗

DOE OSTI2025

Boron Nitride-Driven Strengthening of Aluminum Composites via Friction Stir Processing

Friction stir welding and processing (FSW/P) has emerged as an effective solid-state joining technique for fabricating metal matrix composites (MMCs), offering improved mechanical properties through refined microstructural evolution. In this study, an aluminum-boron nitride nanoparticle (Al-BNNP) composite was synthesized via FSW, and its indentation-based mechanical properties were systematically evaluated. Microhardness mapping across the weld cross-section revealed a progressive increase in hardness toward the stir zone (SZ), attributed to severe plastic deformation, dynamic recrystallization (DRX), and the reinforcing effect of BNNPs. Profilometry-based indentation plastometry (PIP) inferred yield strength (YS) demonstrates a 47.8% increase compared to the base metal (BM) and a 75% improvement compared to FSP pure aluminum reported in literature. This enhancement is attributed to strengthening mechanisms, including grain boundary pinning, load transfer, and increased dislocation density. The strain rate sensitivity (SRS) measurements at the nanoscale demonstrated a substantial decrease in the SZ, correlated with ultrafine grain structures and strong BNNP-matrix interactions. Activation volume analysis revealed a significant reduction in the SZ, suggesting that dislocation motion is increasingly restricted by dislocation-dislocation and dislocation-particle interactions. These findings suggest that incorporating BNNPs in FSW/P enables tailoring the microstructure without thermal degradation of the secondary particles, thereby significantly enhancing the mechanical performance of aluminum composites, particularly for structural applications in aerospace and automotive industries.

Aluminum↗

DOE OSTI2026

Iron surface corrosion in supercritical CO2 at atomic scale investigated by molecular dynamics simulations

Understanding the corrosion behavior of steels in supercritical carbon dioxide (S-CO2) is essential for ensuring the safe application of S-CO2 as a heat-transfer fluid in high-temperature energy systems, including advanced nuclear reactors. In this work, molecular dynamics (MD) simulations using ReaxFF potential are performed to explore the atomic-scale corrosion mechanisms of body-centered cubic iron (BCC-Fe) in S-CO2. The results show that CO2 molecules in S-CO2 decompose at the Fe surface, generating free C and O atoms that form Fe-C and Fe-O bonds and subsequently produce oxides and carbides. Concurrently, Fe atoms dissolve from the surface and diffuse into the S-CO2 region, resulting in interdiffusion of Fe, C and O atoms at the interface. The corrosion-layer thickness calculations show that high pressure and temperature induced by S-CO2 have stronger effects than surface orientation on the corrosion process. In addition, surface Fe atoms undergo substantial displacement under S-CO2 exposure, further accelerating corrosion. When a radiation-induced void is introduced near the Fe surface, the corrosion is enhanced. The void-matrix interface expands the reaction surface area and simultaneously induces corrosion reactions inside the bulk, resulting in a deeper penetration of C and O and thicker corrosion layers. All these results indicate that high-temperature, high-pressure and radiation-induced voids can seriously affect the corrosion of Fe in S-CO2, and must be considered to better use S-CO2 in nuclear facilities.

Li, Wenhua↗

DOE OSTI2026

Spinel high-entropy oxides (FeNiCrMnZnX) 3 O 4 (X = Al, mg) as anode materials for high-performance lithium-ion batteries

To address the high cost, cobalt dependency, and resource constraints typical of conventional high-entropy oxide (HEO) anodes, this study reports the successful synthesis of two Co-free, six-component spinel-type HEOs(FeNiCrMnZnAl) 3 O 4 (HEO-Al) and (FeNiCrMnZnMg) 3 O 4 (HEO-Mg), via a sol-gel method. The distinct effects of Al 3+ and Mg 2+ incorporation on the electrochemical performance and lithium storage kinetics were systematically investigated. XRD, Raman, and TEM characterizations confirm that both materials possess a pure spinel phase, uniform particle size, and homogeneous elemental distribution. Notably, electrochemical evaluations reveal that HEO-Al delivers a superior reversible capacity of 480.7 mAh g −1 after 100 cycles at 0.1 A g −1 , and maintains 354.5 mAh g −1 after 1000 long-term cycles at 1 A g −1 , significantly outperforming HEO-Mg. Kinetic analysis indicates that HEO-Al exhibits lower charge transfer resistance, a higher Li + diffusion coefficient, and a pseudocapacitive contribution of up to 82%. Furthermore, these findings demonstrate that Al substitution effectively optimizes the structural stability and lithium storage kinetics of Co-free HEOs, providing a viable strategy for designing low-cost, highly stable HEO anode systems.

Anode materials↗

DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗

NASA NTRS2026

Hybrid Modeling Study on Grain Evolution in the Metal Welding Process and Its Potential Lunar Application

Metal is most commonly used structural material in a wide range of spacecraft, and welding is the principal method for joining metal components into functional systems. However, conducting welding experiments under extreme environments—such as microgravity or vacuum conditions in space—is prohibitively expensive and experimentally challenging. To overcome these limitations, multi-physics computational welding models provide a cost-effective and versatile alternative. In this work, the authors have developed a coupled thermal (fluid) microstructure simulation framework to model metal welding under varying gravity conditions. The framework integrates a mixed-mode heat transfer formulation (conduction, convection, and radiation) with molten pool fluid dynamics, enabling accurate prediction of temperature fields and weld-pool geometry. A grain growth model is further incorporated to capture the spatial and temporal evolution of microstructure, including grain size distribution and morphological transitions during solidification. This approach provides detailed insight into molten pool evolution and grain-level microstructure development throughout the welding process. By explicitly parameterizing environmental conditions, the model supports extrapolation to off-Earth manufacturing scenarios such as welding on the lunar surface. Tantalum—chosen in this study due to its high melting point, oxidation resistance, and mechanical stability at elevated temperatures—serves as the material system for model demonstration. Beyond Tantalum, the integrated multi-physics framework offers broad applicability for predictive welding simulations of various structural and refractory metals or alloys used in extreme terrestrial or extraterrestrial environments.

kinetic Monte Carlo (SPPARKS)↗

DOE OSTI2026

Smart building HVAC control challenge: experience and solutions from the ADRENALIN project

A smart building HVAC control competition crowdsourced and compared algorithms on fair and equal ground using the standardized BOPTEST framework. The competition attracted 138 participants, but only 9% submitted valid solutions for the final stage, highlighting the complexity of advanced HVAC control design. The winning solutions showed significant potential to reduce energy use and cost by shifting demand, without compromising occupant comfort. Across scenarios, thermal energy cost reductions of 36–76% relative to a baseline, were achieved. In peak heat periods, the cost reduction leveraged limited energy use reduction (0–15%), but more significant energy price reduction (34–62%). This shows smart controls' ability to avoid as much as possible consumption during the morning peak hours, when spot prices are tendentially the highest. Hosting the competition has highlighted challenges in creating competitions that both are fair and promotes solutions that are transferable to real life implementation.

BOPTEST↗

DOE OSTI2023

Nonlinear Loss Engineering in Near‐Zero‐Index Bulk Materials

Transparent conducting oxides (TCOs) show unprecedented optical nonlinearities in the near infrared wavelength range, where the real part of their linear refractive index approaches zero. More specifically, the Kerr nonlinearities of these materials have sparked widespread attention due to their magnitude and speed. However, due to the absorptive nature of these nonlinear processes, it is of fundamental interest to further investigate the imaginary component of the nonlinear index. The present work studies the nonlinear optical absorption properties of aluminium‐doped zinc oxide (AZO) thin films in their near‐zero‐index (NZI) spectral window. It is found that the imaginary part of the refractive index is reduced under optical excitation such that the field penetration depth more than doubles. An optically induced shift of the NZI bandwidth of ≈120 nm for a pump intensity of 1.3 TW cm −2 is also demonstrated. Looking into the optically induced spectral redistribution of the probe signal, local net gain is recorded, which is ascribed to a nonlinear adiabatic energy transfer. The present study adds key information about the fundamental interplay between real and imaginary nonlinear indices in NZI media, while advancing parametric amplification as viable direction for loss compensation.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Temperature-Dependent Transport Characteristics of 2D MoS2 Channel FETs Grown Using Salt-Based Precursors

D Transition Metal Dichalcogenides (TMDs), particularly MoS2, are promising candidates for sub-10 nm Gate All Around (GAA) CMOS FETs. Salt-assisted Chemical Vapor Deposition (CVD) enable lateral MoS2 growth at atmospheric pressure and low temperatures. This work analyzes salt-based precursor-driven CVD-grown MoS2 FETs at various temperatures. MoS2 was grown using Ammonium Molybdate salt, sulfurized at 750∘C, and transferred onto p−Si3/SiO2 substrates. At room temperature, threshold voltage (VT) ranged from -35 V to -25 V, with a peak drain current of 1.2μA/μm. As temperature increased above 325K, VT shifted exponentially, and carrier mobility dropped significantly. At 400 K, the gate lost channel control, though gate leakage current remained low. These results are compared with non-salt-based MoS2 growth to assess salt precursor effects.Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan).

Jones, Andrew [ORNL] (ORCID:0009000233849687)↗

DOE OSTI2026

First-principles DFT modeling of nitrobenzene adsorption on the Ag(111) surface at varying monolayer coverages

Here, the adsorption behavior of nitrobenzene on the Ag(111) surface as a function of coverage is investigated using density functional theory. Adsorption energies and optimized geometries are analyzed together with isolated intermolecular interaction calculations and electronic structure analysis, including Bader charge partitioning and projected density of states, to disentangle the roles of adsorbate–surface bonding and through-space adsorbate–adsorbate interactions. At low and intermediate coverages (θ = 1/9 and θ = 2/9), bidentate adsorption at top sites is favored due to strong adsorbate–surface interactions, with additional stabilization at θ = 2/9 arising from favorable intermolecular separations. At higher coverage (θ = 1/3), bidentate adsorption is destabilized by strong intermolecular repulsion, and monodentate adsorption becomes energetically preferred as rotational freedom allows more favorable intermolecular spacing. Charge density differences, Bader charge, and density of states analyses show that charge transfer from the Ag surface is localized primarily on the nitro group and increases with coverage and adsorption denticity, although this increase does not directly correlate with adsorption strength at high coverage due to competing intermolecular interactions. These results demonstrate that surface coverage can induce a transition in preferred adsorption denticity driven by intermolecular interactions, highlighting the importance of adsorbate packing in organic molecule adsorption at metal interfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Novel Zwitterionic Polyurethane-in-Salt Electrolytes with High Ion Conductivity, Elasticity, and Adhesion for High-Performance Solid-State Lithium Metal Batteries

This study presents a novel polymer-in-salt (PIS) zwitterionic polyurethane-based solid polymer electrolyte (zPU-SPE) that offers high ionic conductivity, strong interaction with electrodes, and excellent mechanical and electrochemical stabilities, making it promising for high-performance all solid-state lithium batteries (ASSLBs). The zPU-SPE exhibits remarkable lithium-ion (Li+) conductivity (3.7 × 10⁻⁴ S cm−1 at 25 °C), enabled by exceptionally high salt loading of up to 90 wt.% (12.6 molar ratio of Li salt to polymer unit) without phase separation. It addresses the limitations of conventional SPEs by combining high ionic conductivity with a Li+ transference number of 0.44, achieved through the incorporation of zwitterionic groups that enhance ion dissociation and transport. The high surface energy (338.4 J m−2) and elasticity ensure excellent adhesion to Li anodes, reducing interfacial resistance and ensuring uniform Li+ flux. When tested in Li||zPU||LiFePO₄ and Li||zPU||S/C cells, the zPU-SPE demonstrated remarkable cycling stability, retaining 76% capacity after 2000 cycles with the LiFePO4 cathode, and achieving 84% capacity retention after 300 cycles with the S/C cathode. Molecular simulations and a range of experimental characterizations confirm the superior structural organization of the zPU matrix, contributing to its outstanding electrochemical performance. The findings strongly suggest that zPU-SPE is a promising candidate for next-generation ASSLBs.

Wang, Kun↗

NASA NTRS2024

Artemis I Orion ESM Propulsion System Engine Performance

NASA's Orion spacecraft transports humans and cargo into cislunar space for the Artemis program. The European Service Module (ESM), supplied by ESA and its European industry partners, provides Orion with power and in-space propulsion. The Orion-ESM propulsion system is a bipropellant hypergolic propulsion system using monomethyl hydrazine (MMH) and nitrogen tetroxide (MON-3). Primary translational propulsion is provided by the Orbital Maneuvering System Engine(OMS-E), with backup translational propulsion provided by eight Auxiliary thrusters (AUX). Attitude control and small translational maneuvers are provided by twenty four Reaction Control System (RCS) engines. The 2022 Artemis I mission was the first integrated flight test of the Orion-ESM spacecraft and its propulsion system. The OMS-E used on Artemis I was a refurbished Space Shuttle OMS-E that previously flew on nineteen missions ranging from STS-41G in 1984 to STS-112 in 2002. The Auxiliary engines are modified Aerojet Rocketdyne R4D-11 engines produced specifically for the Orion program. The RCS engines are Ariane Group engines originally used for the Automated Transfer Vehicle (ATV) program. This paper will discuss the unique operational requirements for each engine on Orion and the development and qualification effort sat both the engine and system-level that were completed to enable a successful Artemis I mission. Next the paper will evaluate the in-flight performance of the engines during the Artemis I mission showing nominal performance as expected. Additionally, comparisons to models will be presented showing very good correlation. Finally, the paper will address the plan for the engines on future Orion missions and the evolution of the system operation.

Liquid Propulsion Systems↗

NASA NTRS2024

Artemis I Orion ESM Propulsion System Engine Performance

Reuse↗

NASA NTRS2026

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↗

NASA NTRS2026

Model Correlation and Thermal Analysis of xEMU Boot at Lunar South Pole Temperatures

The National Aeronautics and Space Administration (NASA) Artemis program plans to send astronauts to the lunar south pole, a region of the moon that is colder than previous lunar missions and low earth orbit operations. The spacesuit boots that will be used on these missions will be directly impacted by these extremely cold temperatures (down to ~50 K). To assess the performance of the Exploration Extravehicular Mobility Unit (xEMU) lunar boot in these extreme temperatures, the boot was tested at the Jet Propulsion Lab (JPL) in the Cryogenic Ice Transfer, Acquisition Development, and Excavation Laboratory (CITADEL) thermal vacuum (TVAC) chamber. This data was used to correlate thermal models to predict operational performance of the boots on the lunar south pole. This paper documents the data analysis, thermal boot model correlation, and operational predictions conducted using data from the xEMU TVAC test. Data from the test series was used to determine expected thermal conductances within the boot and between the boot and environment. These conductances were used to correlate a Thermal Desktop model of the xEMU boot across 10 different test points which varied external temperature, internal boot ventilation flowrate, and contact pressure. The correlated model was then used to predict operational performance in the lunar south pole. While the predictions indicate promising evidence for performance of the boots at the 100K test point, there is still substantial uncertainty in performance, particularly at the 48K test point. The results of this test series and model correlation stress the importance of improved testing for characterizing the expected thermal resistance between the outside of the boot and the lunar surface.

xEMU↗