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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Redox Homeostasis as a Key Regulator of Intramolecular Cyclization in Fungal Perylenequinones

Perylenequinones (PQs) such as hypocrellins and hypomycins are fungal-derived redox-active metabolites with known roles as photosensitizers in the oxidative stress response and applications in photodynamic therapy (PDT). Here, we report that Shiraia sp., a filamentous fungus, can survive and grow under strictly anaerobic (argon) conditions─an unexpected finding for a multicellular eukaryote. Modulating redox homeostasis through chemical reduction and oxygen limitation promotes the intramolecular cyclization of hypocrellins, enhancing hypomycin biosynthesis. Moisture content further influences these transformations, with high water levels favoring keto–enol tautomerization and dry, reducing environments promoting hydride substitution at the peripheral positions. These findings highlight redox modulation as a key driver of perylenequinone metabolism and suggest that PQs may contribute to maintaining redox balance under anaerobic stress, hinting at a broader role in oxygen-independent adaptation in filamentous fungi. This work offers new insights at the interface of redox biology, chemical signaling, and fungal metabolism, with potential implications for the stability and function of PQ-based PDT agents in hypoxic, reducing conditions such as tumor microenvironments.

cyclization↗
NASA NTRS2003

Abstract communication for coordinated planning

work offers evidence that distributed planning agents can greatly reduce communication costs by reasoning at abstract levels. While it is intuitive that improved search can reduce communication in such cases, there are other decisions about how to communicate plan information that greatly affect communication costs. This paper identifies cases independent of search where communicating at multiple levels of abstraction can exponentially decrease costs and where it can exponentially add costs. We conclude with a process for determining appropriate levels of communication based on characteristics of the domain.

coordination↗
DOE OSTI2020

Selective Lanthanide Sensing with Gold Nanoparticles and Hydroxypyridinone Chelators

The octadentate hydroxypyridinone chelator 3,4,3-LI(1,2-HOPO) is a promising therapeutic agent because of its high affinity for f-block elements and noncytotoxicity at medical dosages. The interaction between 3,4,3-LI(1,2-HOPO) and other biomedically relevant metals such as gold, however, has not been explored. Gold nanoparticles functionalized with chelators have demonstrated great potential in theranostics, yet thus far, no protocol that combines 3,4,3-LI(1,2-HOPO) and colloidal gold has been developed. Here, we characterize the solution thermodynamic properties of the complexes formed between 3,4,3-LI(1,2-HOPO) and Au 3+ ions and demonstrate how under specific pH conditions the chelator promotes the growth of gold nanoparticles, acting as both reducing and stabilizing agent. 3,4,3-LI(1,2-HOPO) ligands on the nanoparticle surface remain active and selective toward f-block elements, as evidenced by gold nanoparticle selective aggregation. Finally, a new colorimetric assay capable of reaching the detection levels necessary for the quantification of lanthanides in waste from industrial processes is developed based on the inhibition of particle growth by lanthanides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Accelerating lattice gauge theory studies with Agentic AI

Lattice gauge theory research, with its computationally intensive simulations and complex multi‑stage workflows, is well positioned to benefit from agentic AI systems. We demonstrate how such tools can support key components of lattice gauge theory research, including novel simulation code development using standard LQCD frameworks, HPC job orchestration, simulation data analysis, and expert‑guided tuning of algorithmic parameters such as Hasenbusch mass preconditioning and multigrid solvers. Our results show that agentic AI can reduce manual effort, improve productivity, and accelerate the research cycle while maintaining essential human oversight.

Ayyar, Venkitesh [Fermilab]↗
NASA NTRS1980

Effect of certain psychopharmacological preparations on adaptation under stress conditions

Experiments staged on rats demonstrated that the formation of pathological states caused by stress and accompanied by the development of ulcerative lesion of the gastric mucosa are associated with the degree of the catecholamines level drop in the mesencephalon and hypothalamus. The application of seduxen and also of combinations consisting of L-DOPA with seduxen, or with an L-adrenoblocking agent pyroxan tends to reduce the frequency of developing alcerative lesions of the stomach. The protective effect produced by the combination of L-DOPA with an L-adrenoblocking agent pyroxan is barred by an additional administration of an B-adrenoblocking agent, inderal.

Stanishevskaya, A. V.↗
DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2004

Validating the autonomous EO-1 science agent

This paper describes the validation process for the Autonomous Science Agent, a software agent that is currently flying onboard NASA EO-1 spacecraft. The agent autonomously collects, analyzes, and reacts to onboard science data. The Autonomous Science Agent has been designed using a layered architectural approach with specific redundant safeguards to reduce the risk of an agent malfunction to the EO-1 spacecraft. This safe design has been thoroughly validated by informal validation methods supplemented by sub-system and system-level testing. This paper describes the analysis used to define agent safety, elements of the design that increase the safety of the agent, and the process used to validate agent safety.

software↗
DOE OSTI2022

Trichoderma species isolated from grapevine with tolerance towards common copper fungicides used in viticulture for plant protection

Abstract BACKGROUND Copper‐containing fungicides are applied broadly in organic viticulture against downy mildew caused by Plasmopara viticola . Although long‐term application of copper‐based fungicides is associated with ecotoxic effects on the environment, their use in viticulture is required until sustainable alternatives are available. Trichoderma spp. might be a promising approach to fungicide reduction while promoting plant growth and development and displaying biocontrol activity. This study aims to examine the tolerance and compatibility of Trichoderma spp. to copper fungicides. This work contributes to the development of a spray application consisting of a copper‐tolerant Trichoderma sp. combined with a downscaled copper fungicide rate against P. viticola . RESULTS Trichoderma spp. isolated from grapevine wood in vineyards were identified and used for tolerance screening in various concentrations of copper fungicides. Copper hydroxide was identified as being highly compatible with Trichoderma . Two Trichoderma candidates, T. koningiopsis and T. harzianum , showed high copper tolerance in mycelial growth and germination tests, and were adapted to 2.85 g Cu L −1 of the selected fungicide. Microscopic investigations showed the attachment of copper compounds to fungal cell walls and copper uptake within the cytoplasm. In the case of high tolerance, large‐scale copper uptake was prevented. CONCLUSION Our findings identified two highly copper‐tolerant Trichoderma isolates with natural adaptation to the vineyard ecosystem, which could be further tested as biostimulants and biocontrol agents, combined with a reduced fungicide rate for sustainable plant protection. © 2022 Society of Chemical Industry.

Küpper, Verena↗
DOE OSTI2020

Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy

The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.

Mckee, Evan↗
DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Use of X-Ray Fluorescence Microscopy for Studies on Research Models of Hepatocellular Carcinoma

Introduction: TheraSphere ® microspheres containing yttrium 90 Y are among many radioembolization agents used clinically to reduce liver tumor burden, and their effects on cancer volume reduction are well-established. At the same time, concerns about off target tissue injury often limit their use. Deeper investigation into tissue distribution and long-term impact of these microspheres could inform us about additional ways to use them in practice. Methods: Healthy rat liver and rabbit liver tumor samples from animals treated with TheraSpheres were sectioned and their elemental maps were generated by X-ray fluorescence microscopy (XFM) at the Advanced Photon Source (APS) synchrotron at Argonne National Laboratory (ANL). Results: Elemental imaging allowed us to identify the presence and distribution of TheraSpheres in animal tissues without the need for additional sample manipulation or staining. Ionizing radiation produced by 90 Y radioactive contaminants present in these microspheres makes processing TheraSphere treated samples complex. Accumulation of microspheres in macrophages was observed. Conclusions: This is the first study that used XFM to evaluate the location of microspheres and radionuclides in animal liver and tumor samples introduced through radioembolization. XFM has shown promise in expanding our understanding of radioembolization and could be used for investigation of human patient samples in the future.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1967

Degreasing of titanium to minimize stress corrosion

Stress corrosion of titanium and its alloys at elevated temperatures is minimized by replacing trichloroethylene with methanol or methyl ethyl ketone as a degreasing agent. Wearing cotton gloves reduces stress corrosion from perspiration before the metal components are processed.

Carpenter, S. R.↗
NASA NTRS1990

Ozone Treatment For Cooling Towers

Report presents results of study of cooling tower in which water treated with ozone instead of usual chemical agents. Bacteria and scale reduced without pollution and at low cost. Operating and maintenance costs with treatment about 30 percent of those of treatment by other chemicals. Corrosion rates no greater than with other chemicals. Advantage of ozone, even though poisonous, quickly detected by smell in very low concentrations.

Blackwelder, Rick↗
NASA NTRS2004

Suppression of Low Strain Rate Nonpremixed Flames by an Agent

The extinction and structure of non-premixed methane/air flames were investigated in normal gravity and microgravity through the comparison of experiments and calculations using a counterflow configuration. From a fire safety perspective, low strain rate conditions are important for several reasons. In normal gravity, many fires start from small ignition sources where the convective flow and strain rates are weak. Fires in microgravity conditions, such as a manned spacecraft, may also occur in near quiescent conditions where strain rates are very low. When designing a fire suppression system, worst-case conditions should be considered. Most diffusion flames become more robust as the strain rate is decreased. The goal of this project is to investigate the extinction limits of non-premixed flames using various agents and to compare reduced gravity and normal gravity conditions. Experiments at the NASA Glenn Research Center's 2.2-second drop tower were conducted to attain extinction and temperature measurements in low-strain non-premixed flames. Extinction measurements using nitrogen added to the fuel stream were performed for global strain rates from 7/s to 50/s. The results confirmed the "turning point" behavior observed previously by Maruta et al. in a 10 s drop tower. The maximum nitrogen volume fraction in the fuel stream needed to assure extinction for all strain rates was measured to be 0.855+/-0.016, associated with the turning point determined to occur at a strain rate of 15/s. The critical nitrogen volume fraction in the fuel stream needed for extinction of 0-g flames was measured to be higher than that of 1-g flames.

Olson, Sandra L.↗
DOE OSTI2021

Method and composition for targeted delivery of therapeutic agents

Functionalized single walled or multi-walled carbon nanotubes (f-CNTs) can be delivered into mammals to targeted organs, such as the kidney and the liver. These f-CNTs may be non-covalently linked or covalently linked to therapeutic agents. In particular, the application delivers carbon nanotube-therapeutic agent conjugates to a target organ, thereby preventing or reducing damages to the organ caused by other agents or procedure.

McDevitt, Michael R.↗
NASA NTRS2002

Smart Aerospace eCommerce: Using Intelligent Agents in a NASA Mission Services Ordering Application

This paper describes how intelligent agent technology was successfully prototyped and then deployed in a smart eCommerce application for NASA. An intelligent software agent called the Intelligent Service Validation Agent (ISVA) was added to an existing web-based ordering application to validate complex orders for spacecraft mission services. This integration of intelligent agent technology with conventional web technology satisfies an immediate NASA need to reduce manual order processing costs. The ISVA agent checks orders for completeness, consistency, and correctness, and notifies users of detected problems. ISVA uses NASA business rules and a knowledge base of NASA services, and is implemented using the Java Expert System Shell (Jess), a fast rule-based inference engine. The paper discusses the design of the agent and knowledge base, and the prototyping and deployment approach. It also discusses future directions and other applications, and discusses lessons-learned that may help other projects make their aerospace eCommerce applications smarter.

Moleski, Walt↗