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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerating countermeasure candidate discovery for A-series chemical warfare agent exposure

The recent alleged use of A-series chemical warfare agents (CWAs) highlights the urgent need to better understand their inhibition of cholinesterase enzymes and the reported shortcomings of traditional oxime countermeasures. Here, using high-throughput (HT) mass spectrometry (MS) technologies, we characterized the largely unknown inhibition kinetics of A-series CWAs on human acetylcholinesterase (hAChE) and its reactivation by oximes, achieving label-free quantitation at rates of up to 7,000 reactions per hour. Our findings indicate i) A-series agents exhibit inhibitory potencies similar to traditional CWAs like sarin and VX, and ii) bipyridinium-based oximes can reactivate A-series-adducted hAChE in vitro, challenging prior reports on oxime efficacy. These results underscore the need for continued exploration of countermeasure candidates against A-series CWAs and demonstrate the potential of HT-MS for rapidly and safely characterizing emerging toxic chemicals.

acetylcholinesterase↗
DOE OSTI2026

Adaptive Reinforcement Learning (ARL) Control of a Multi-port Resonant Converter in UAV Systems

This study presents an adaptive reinforcement learning (ARL) control framework for a multi-port resonant converter used in hybrid unmanned aerial vehicle (UAV) power systems. The converter integrates high-frequency half-bridge input ports connected to a rectified engine–generator set and a battery energy storage system, along with a semi-bridgeless active rectifier supplying the propulsion load. A deep RL agent is trained to dynamically regulate inter-port phase-shift commands in real time based on flight conditions and load power demand. The ARL controller autonomously identifies phase-shift combinations that maximize conversion efficiency while maintaining stable and coordinated power flow, even under rapidly varying operating scenarios. This data-driven approach eliminates the need for explicit system modeling or extensive manual tuning and enables coordinated control among multiple power ports without inter-port communication. Experimental results validate that the ARL based strategy achieves reliable power sharing and consistently high-efficiency operation across diverse UAV operating conditions.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2020

Predicting antimicrobial susceptibility from the bacterial genome: A new paradigm for one health resistance monitoring

The laboratory identification of antibacterial resistance is a cornerstone of infectious disease medicine. In vitro antimicrobial susceptibility testing has long been based on the growth response of organisms in pure culture to a defined concentration of antimicrobial agents. By comparing individual isolates to wild type susceptibility patterns, strains with acquired resistance can be identified. Acquired resistance can also be detected genetically. After many decades of research, the inventory of genes underlying antimicrobial resistance (AMR) is well known for several pathogenic genera including zoonotic enteric organisms such as Salmonella and Campylobacter and continues to grow substantially for others. With the decline in costs for large scale DNA sequencing, it is now practicable to characterize bacteria using whole genome sequencing (WGS), including the carriage of resistance genes in individual microorganisms and those present in complex biological samples. With genomics we can generate comprehensive, detailed information on the bacterium, the mechanisms of antibiotic resistance, clues to its source, and the nature of mobile DNA elements by which resistance spreads. These developments point to a new paradigm for antimicrobial resistance detection and tracking for both clinical and public health purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Osprey Framework v0.2.2

The Alpha Berkeley Framework is a software architecture for building agentic AI systems that coordinate multi-step workflows in scientific and industrial environments. It is based on a plan-first orchestration model, where natural language requests are translated into execution plans with explicit dependencies and optional human approval. The framework includes capability classification, which selects relevant tools on a per-task basis to keep orchestration efficient as the number of available tools grows. It incorporates task extraction methods that compress conversational context and integrate external resources such as databases, APIs, and knowledge bases into structured, machine-readable tasks. Execution is supported by modular services with checkpointing, artifact management, and error handling, allowing workflows to be paused, inspected, and resumed. The system is designed for deployment in production environments, supporting both local and containerized execution as well as integration with HPC clusters. Interfaces include command-line tools, browser-based workflows, and containerized services. The framework has been demonstrated in tutorial examples and deployed at the Advanced Light Source, where it coordinates accelerator control and analysis workflows.

Hellert, Thorsten [Lawrence Berkeley National Labo↗
DOE OSTI2026

Peptoid-Based Nanosheets Exhibiting Broad Antiviral Activity Against Enveloped RNA Viruses

Enveloped RNA viruses, such as Influenza A (H1N1) and Sindbis virus, pose persistent global health threats due to their high mutation rates, efficient transmission, and frequent drug resistance. By mimicking host cell membrane receptors, multivalent virus inhibitors can block viral attachment, making them promising broad-spectrum antiviral agents. However, most of existing antivirals are often limited by strain specificity, short-lived efficacy, and toxicity. Here, we introduce a broad-spectrum antiviral platform based on highly tunable and biocompatible two-dimensional nanomembranes (2DNMs) self-assembled from amphiphilic peptoids, operating via a non-genomic, mutation-insensitive mechanism. By varying peptoid sequence, we design and synthesize over twenty different 2DNMs with various surface charge and high density of viral-attachment ligands (VALs). The self-assembled architecture of these stable 2DNMs provides cooperative noncovalent multivalent binding to virus particles that result in effective inhibition of viral infection. Screening of variants identified three leads that potently suppressed Influenza A (H1N1) and Sindbis virus infection across median tissue culture infectious dose (TCID50), plaque, RT–qPCR, and immunofluorescence assays, while maintaining >90% cell viability. These nanosheets significantly reduced infectious titers, viral RNA replication, and intracellular viral protein expression, indicating inhibition at early stages of viral entry and propagation. The sequence programmability, chemical robustness, and mutation-insensitive antiviral activity distinguish 2DNMs from traditional antivirals and positions them as a versatile materials platform for antiviral coatings, protective barriers, and prophylactic biomedical applications.

Influenza A virus↗
DOE OSTI2022

Static and Dynamic Gradient Based Directional Transportation of Neutral Molecules in Swollen Polymer Films

Abstract Materials which selectively transport molecules offer powerful opportunities for concentrating and separating chemical agents. Here, utilizing static and dynamic chemical gradients, transport of molecules within swollen crosslinked polymers is demonstrated. Using an ≈200 μm static hydroxyl to hexyl gradient, the neutral ambipolar nerve agent surrogate diethyl (cyanomethyl)phosphonate (DECP) is directionally transported and concentrated 60‐fold within 4 hours. To accelerate transport kinetics, a dynamic gradient (a “travelling wave”) is utilized. Here, the non‐polar dye pyrene was transported. The dynamic gradient is generated by an ion exchange process triggered by the localized introduction of an aqueous NaCl solution, which converts the gel from hydrophobic to hydrophilic. As the hydrophilic region expands, associated water enters the gel, and pyrene is pushed ahead of the expansion front. The dynamic gradient provides about 10‐fold faster transport kinetics than the static gradient.

Ali, Mohammad A.↗
DOE OSTI2022

Static and Dynamic Gradient Based Directional Transportation of Neutral Molecules in Swollen Polymer Films

Abstract Materials which selectively transport molecules offer powerful opportunities for concentrating and separating chemical agents. Here, utilizing static and dynamic chemical gradients, transport of molecules within swollen crosslinked polymers is demonstrated. Using an ≈200 μm static hydroxyl to hexyl gradient, the neutral ambipolar nerve agent surrogate diethyl (cyanomethyl)phosphonate (DECP) is directionally transported and concentrated 60‐fold within 4 hours. To accelerate transport kinetics, a dynamic gradient (a “travelling wave”) is utilized. Here, the non‐polar dye pyrene was transported. The dynamic gradient is generated by an ion exchange process triggered by the localized introduction of an aqueous NaCl solution, which converts the gel from hydrophobic to hydrophilic. As the hydrophilic region expands, associated water enters the gel, and pyrene is pushed ahead of the expansion front. The dynamic gradient provides about 10‐fold faster transport kinetics than the static gradient.

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

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗
DOE OSTI2022

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Critical Insights into Solvent Choice for High-Voltage Organosulfur Electrolytes

Organosulfur electrolytes are promising candidates for enabling high-voltage cathodes due to their superior oxidative stability compared to conventional carbonate-based systems. However, their viscous nature and inability to passivate the anode necessitate the use of passivating agents and diluents to achieve meaningful charge/discharge rates. In this study, we investigate the use of cyclic fluorinated carbonates (CFCs) to form passivating interphases on electrode surfaces, aiming to optimize electrolyte performance in high-voltage systems. Electrolyte formulations containing 1.2 M LiPF 6 in a CFC:sulfone:1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropylether (TTE) mixture (2/5/3 V/V/V) were assessed in full-cell configurations with a LiNi 0.8 Mn 0.1 Co 0.1 O 2 (FCG-NMC) cathode and a 4.5 V upper cutoff potential. The results reveal that 3-3-3-trifluoropropylene carbonate (TFPC) combined with ethyl methyl sulfone (EMS) optimizes electrolyte performance, resulting in lower resistance buildup and reduced capacity loss compared to commercial electrolytes. We also explore the failure mechanisms of several carbonate/sulfone mixtures and identify key considerations for electrolyte compatibility in high-voltage systems. TFPC enhances cycling stability and lowers overall cell resistance, while fluoroethylene carbonate (FEC) formulations, despite higher ionic conductivity, fail to form adequate passivation layers, leading to rapid capacity loss. Additionally, EMS provides superior physical properties that avoid common failure modes seen with other sulfone solvents like methyl isopropyl sulfone and tetramethylene sulfone. Furthermore, this study underscores the importance of carefully selecting passivating carbonates and sulfone solvents to improve electrolyte performance and expand the viability of high-voltage electrolyte systems.

LiNi0.8Mn0.1Co0.1O2 cathode↗
DOE OSTI2020

Regeneration of critical-sized defects, in a goat model, using a dextrin-based hydrogel associated with granular synthetic bone substitute

Abstract The development of injectable bone substitutes (IBS) have obtained great importance in the bone regeneration field, as a strategy to reach hardly accessible defects using minimally invasive techniques and able to fit to irregular topographies. In this scenario, the association of injectable hydrogels and bone graft granules is emerging as a well-established trend. Particularly, in situ forming hydrogels have arisen as a new IBS generation. An in situ forming and injectable dextrin-based hydrogel (HG) was developed, aiming to act as a carrier of granular bone substitutes and bioactive agents. In this work, the HG was associated to a granular bone substitute (Bonelike®) and implanted in goat critical-sized calvarial defects (14 mm) for 3, 6 and 12 weeks. The results showed that HG improved the handling properties of the Bonelike® granules and did not affect its osteoconductive features, neither impairing the bone regeneration process. Human multipotent mesenchymal stromal cells from the umbilical cord, extracellular matrix hydrolysates and the pro-angiogenic peptide LLKKK18 were also combined with the IBS. These bioactive agents did not enhance the new bone formation significantly under the conditions tested, according to micro-computed tomography and histological analysis.

Pereira, Isabel↗
DOE OSTI2023

Trust in News as Moderated by Known Truth Assessments and Source

Disinformation has become a problem in various spaces, from social media to organizations. Given the lack of evidence and transparency surrounding organizational disinformation, we aim to understand issue of information exposure effect and source trust from the lens of traditional information consumption online. Using an online experiment and multi-level mixed-effects modeling, we find that the exposure effect exists even over only two exposures to a headline, as long as participants are not shown fact-checking. Fact-checking can be effective in reducing the impact of the exposure effect. Additionally, we find that participants trusted sources significantly more before seeing the headlines. The possible presence of disinformation reduces trust in sources, even when considered highly neutral and reputable. These findings can inform simulations of agent interactions with misinformation as well as future experimental designs focused on evaluations of belief and trust based on recognizability of a source.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗
DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Q-Learning-Based Approach for Distributed Beam Scheduling in mmWave Networks

We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks where multiple base stations (BSs) belonging to different service operators share the same unlicensed spectrum with no centralized control or explicit coordination among them. Our goal is to design efficient distributed beam scheduling and power allocation schemes such that the network-level payoff, defined as the weighted sum of the total throughput and a power penalization term, can be maximized. To this end, we propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent. As a baseline, we compare the proposed approach to the state-of-the-art non-cooperative game-based approach which was previously developed for the same problem. We conduct extensive experiments under various scenarios to verify the effect of multiple factors on the performance of both approaches. Experiment results show that the proposed approach adapts well to different interference conditions by learning from experience and thus can achieve higher payoff than the game-based approach. The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee. As a result, the weights in the payoff function can be automatically and optimally determined by the virtual queue values from the sub-problems derived from the Lyapunov optimization framework.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Pricing Strategy of Electric Vehicle Aggregators Based on Locational Marginal Price to Minimize Photovoltaic (PV) Curtailment

The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Understanding of three different polyvinylpyrrolidone (PVP) based battery binders blends on graphene surfaces from first principles via DFT simulations

Binders play a crucial role in binding electrodes, current collectors, and conductive agents during battery manufacturing. The selection of the binders and their blends considerably affect various properties such as mechanical durability, adhesion performance, ionic/electronic conductivities, and solid electrolyte interface stability for lithium-ion batteries. In this work, polyvinylidene di-fluoride (PVDF), polyacrylic acid (PAA), polyvinylpyrrolidone (PVP), and lithiated polyacrylic acid (Li-PAA) were selected as the model binders, and their blends were studied on graphene surfaces. Further, among them, PVDF is hardly soluble in water unlike the other binders, so in practice, the blend of PVP:PVDF is not thought to be a proper combination. However, the PVP:PVDF binder has also been included because the purpose of this work is to calculate several electronic properties such as binding energies, intermolecular interaction energies, bond critical points, electron density, and the Laplacian of electron density at critical bonding regions of all these blends at the molecular level via density functional theory (DFT) simulations in order to evaluate and compare how is the interaction strength and bonding type of three different lithium-ion batteries (LIBs) binder blends on graphene surfaces. Most stable binder pairs and their binding mechanisms on graphene surfaces were studied. The delocalization of lithium in Li-PAA was studied with the presence of water.

25 ENERGY STORAGE↗