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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Epitaxial Growth of 2D Core‐Crown SnS 2 /SnSe 2 Heterostructure Through Interfacial Modification with Polyvinylpyrrolidone

Abstract Developing generalized strategies for controlled synthesis of 2D heterostructures remains a significant challenge because the existing approaches often suffer from poor reproducibility and scalability. In this study, a solution synthesis approach for epitaxial core‐crown heterostructures with controlled band alignment, that overcomes these challenges is reported. Polyvinylpyrrolidone (PVP) is used as a structure‐directing agent to reduce lattice mismatch between SnS 2 and SnSe 2 (10‐10) surfaces and direct epitaxial growth of SnSe 2 crown on SnS 2 seed. Additionally, PVP adsorption to the basal plane prevents van der Waals stacking and stabilizes 2D heterostructures during synthesis. Driven by interfacial thermodynamics, the formation of the core‐crown heterostructure is highly reproducible and the size of the 2D heterostructure and relative areas of the core and the crown can be precisely controlled in a two‐step process by varying synthesis times for the seed and the crown. The identified growth pathway for 2D heterostructures can be generalized to other combinations of van der Waals materials to provide a platform for synthesizing micron‐size epitaxial heterostructures with a desired electronic structure for catalysis and microelectronics.

Liu, Lili [Physical and Computational Sciences Dir↗
DOE OSTI2022

Atomic Layer Deposition of Ruthenium Films Using Ruthenium Diketonates and O 2 , H 2 , or N 2 O: The Role of Ruthenium Etching

In this work, the evolution of the surface during the steps that comprise the atomic layer deposition (ALD) of ruthenium films on a nickel substrate using tris(2,2,6,6-tetramethyl-3,5-heptanedionato)ruthenium(III) (Ru(tmhd) 3 ) and molecular oxygen was characterized using a combination of X-ray photoelectron (XPS) and reflection–absorption infrared (RAIRS) spectroscopies. The uptake of the Ru metalorganic precursor was determined to be activated, involving the average loss of two out of the three ligands (and the retention of the third, in molecular form, at the surface–vacuum interface), and self-limited, as required in ALD. The reaction of the resulting layer from that first half of the ALD cycle with O 2 proved to be more complex: in addition to the desired removal of the carbon-containing material, the Ni substrate becomes oxidized, and some Ru is etched away in the form of the volatile RuO 4 gas. By testing different combinations of exposures and temperatures it was determined that Ru film growth was possible, but tuning and optimizing the ALD or atomic layer etching (ALE) process conditions for maximum film growth or removal rate per cycle proved difficult. Replacing O 2 with H 2 was shown not to be viable either, as such an agent can reduce the nickel oxide formed after O 2 treatments (in fact, Ru(tmhd) 3 can do this as well) but not remove the carbonaceous material deposited on the surface by the Ru precursor. The use of N 2 O, on the other hand, showed much promise, being capable of removing most of the surface carbon without affecting the Ru film or the Ni substrate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Spatially resolved photoluminescence brightening in individual single-walled carbon nanotubes

Single-molecule microscopy was used to image photoluminescence (PL) brightening of individual sodium-dodecyl-sulfate (SDS)-wrapped single-walled carbon nanotubes (SWCNTs) upon the addition of dithiothreitol (DTT). PL enhancement varied for each nanotube (NT), with some brightening by 16% and others by a factor of about 7. Interestingly, NTs that displayed lower initial QY values showed the largest increases in PL enhancement. SDS-SWCNTs longer than the diffraction limit were studied in order to spatially resolve the brightening phenomenon. Quite unexpectedly, a uniform, single-step PL brightening along the NT was consistently observed, suggesting that the PL enhancement is the result of a non-localized process. Furthermore, the even PL brightening seen over SWCNTs that are micrometers long implies that single point defect sites, which are known to be largely responsible for exciton nonradiative decay, play no significant role in the brightening process. Interestingly, affixing the SWCNT strongly to the substrate surface mitigated the PL brightening response, consistent with a hypothesis that surfactant reorganization upon the addition of DTT is responsible for exciton PL brightening.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Distributed fiber-optic sensing in a subscale high-temperature superconducting dipole magnet

High-temperature superconductors, such as REBa2Cu3O7−x (REBCO, RE = rare earth), are becoming pivotal for high-field magnet technology for future circular colliders and compact fusion reactors. The U.S. Magnet Development Program, in collaboration with industry, is developing REBCO magnet technology using round conductors consisting of multiple REBCO tapes. For these multi-tape cables, traditional instrumentation, such as voltage taps and resistive strain gauges, become insufficient to help measure and understand the performance-limiting factors in these model magnets. Distributed fiber-optic sensing (DFOS) is a potential solution to address this challenge. Although DFOS is well established for various applications, measuring temperature and strain in high-temperature superconducting magnets is in its infancy. Here we report the detailed implementation and test results of DFOS based on Rayleigh scattering in a subscale canted cosθ (CCT) dipole magnet using high-temperature superconducting CORC® wires. We co-wound optical fibers in each layer of the CCT magnet and compared different types of commercial fibers and mold-release agents to reduce the power attenuation in the fibers. The DFOS allowed us to measure mechanical deformation and temperature along the conductor during tests at 77 and 4.2 K. The measured strain agreed quantitively with a finite-element mechanical model of the subscale magnet. Our results indicate that DFOS can effectively identify locations of strain and temperature changes, offering unique insight into magnet performance that can advance our understanding and development of the REBCO magnet technology for high-energy physics and fusion applications.

Luo, Linqing↗
DOE OSTI2022

Sustainable Conversion of Carbon Dioxide and Shale Gas to Green Acetic Acid via a Thermochemical Cyclic Redox Scheme (Final Report)

The large-scale production of commodity chemicals relies heavily on the combustion of fossil fuels. As a result, enormous amounts of carbon dioxide (CO 2 ) are emitted which severely affects the global climate. The challenges for CO 2 utilization reside in the high stability of CO 2 molecules relative to the products, which requires the addition of significant external energy and overcoming slow and/or unfavorable reaction kinetics/thermodynamics. Chemical looping dry reforming of methane (CLDRM), also known as a hybrid redox process (HRP), is a promising alternative that allows the utilization of CO 2 and domestic shale gas resources to produce commodity chemicals. HRP works in two steps: In the first step, a redox catalyst reacts with methane to yield synthesis gas with a H 2 /CO ratio near 2:1, which is suitable for methanol and Fischer–Tropsch synthesis. The reduced redox catalyst then reacts with an oxidizing agent, such as CO 2 , to yield CO. In comparison to conventional thermochemical CO 2 splitting approaches, the use of methane as the reducing agent in HRP can significantly lower the operating temperature for CO 2 splitting.

03 NATURAL GAS↗
DOE OSTI2022

Systems, methods and computer program products for collaborative agent control

Systems, methods and unmanned agents for collaboratively controlling agents in a collaborative network by one or more agents continuously simulating numeric models of one or more other agents in the network to dramatically reduce the computational bandwidth required between agents, and improve the quality of shared estimates of the agent locations as well as the locations and characteristics of other objects of interest, e.g. targets. Bandwidth is reduced by using the models to intelligently filter data before communicating.

Buerger, Stephen↗
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↗
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]↗
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↗
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↗
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.↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗