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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

pH-Sensitive Microparticles with Matrix-Dispersed Active Agent

Methods to produce pH-sensitive microparticles that have an active agent dispersed in a polymer matrix have certain advantages over microcapsules with an active agent encapsulated in an interior compartment/core inside of a polymer wall. The current invention relates to pH-sensitive microparticles that have a corrosion-detecting or corrosion-inhibiting active agent or active agents dispersed within a polymer matrix of the microparticles. The pH-sensitive microparticles can be used in various coating compositions on metal objects for corrosion detecting and/or inhibiting.

Li, Wenyan↗
NASA NTRS2020

Detection and Mitigation of Transient Instabilities in Multi-Agent Systems and Swarms

We first introduce the novel concept of transient instabilities in multi-agent systems and swarms, i.e., a small disturbance leads to increasing-amplitude oscillations throughout the swarm, which results in a large number of inter-agent collisions. This instability is dominant in the transient phase of the system and it does not appear in the steady-state behavior of the system, as each agent uses Lyapunov-stable feedback control laws. We present a rigorous definition of transient instability in swarms, and discuss its key properties. We also present a sufficient condition to check if a swarm will be transient stable. We study the behaviour of different control laws under this condition. We also show how transient instability phenomenon could impact the dynamics of deployable structures. Next, we present a novel control architecture that augments the baseline formation maintenance controller to mitigate transient instabilities. At its heart, the proposed architecture consists of a projection operator based estimator disguised as a reference model that generates collision-free trajectories for the agents to follow. We present numerical simulation results to demonstrate the effectiveness of our proposed approaches.

Quadrelli, Marco B.↗
DOE OSTI2012

A Hybrid Sensitivity Analysis Approach for Agent-based Disease Spread Models

Agent-based models (ABM) have been widely deployed in different fields for studying the collective behavior of large numbers of interacting agents. Of particular interest lately is the application of agent-based and hybrid models to epidemiology, specifically Agent-based Disease Spread Models (ABDSM). Validation (one aspect of the means to achieve dependability) of ABDSM simulation models is extremely important. It ensures that the right model has been built and lends confidence to the use of that model to inform critical decisions. In this report, we describe our preliminary efforts in ABDSM validation by using hybrid model fusion technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Method and apparatus for enhanced detection of toxic agents

A biosensor based detection of toxins includes enhancing a fluorescence signal by concentrating a plurality of photosynthetic organisms in a fluid into a concentrated region using biased AC electro-osmosis. A measured photosynthetic activity of the photosynthetic organisms is obtained in the concentrated region, where chemical, biological or radiological agents reduce a nominal photosynthetic activity of the photosynthetic organisms. A presence of the chemical, biological and/or radiological agents or precursors thereof, is determined in the fluid based on the measured photosynthetic activity of the concentrated plurality of photosynthetic organisms. A lab-on-a-chip system is used for the concentrating step. The presence of agents is determined from feature vectors, obtained from processing a time dependent signal using amplitude statistics and/or time-frequency analysis, relative to a control signal. A linear discriminant method including support vector machine classification (SVM) is used to identify the agents.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Disaggregation and Refinement of System Dynamics Models via Agent-based Modeling

System dynamics models are usually used to investigate aggregate level behavior, but these models can be decomposed into agents that have more realistic individual behaviors. Here we develop a simple model of the STEM workforce to illuminate the impacts that arise from the disaggregation and refinement of system dynamics models via agent-based modeling. Particularly, alteration of Poisson assumptions, adding heterogeneity to decision-making processes of agents, and discrete-time formulation are investigated and their impacts are illustrated. The goal is to demonstrate both the promise and danger of agent-based modeling in the context of a relatively simple model and to delineate the importance of modeling decisions that are often overlooked.

Nutaro, James J↗
DOE OSTI2018

Apparatus for generating and dispensing a powdered release agent

An apparatus 20 and a method 100 for generating and dispensing a powdered 60 release agent during an additive manufacturing build is disclosed. A solid body 28 of release agent material is ground insitu by a grinder 50 and dispensed on a surface of the part 72 to prevent adhesion of an adjacent layer of a base material 70. With the addition of the powdered 60 release agent, a support structure 76 is easily separated from the base material 70 when the part 72 is complete, saving time and preventing the part 72 from sustaining unintentional damage. Since no powdered 60 release agent is actually loaded or stored in the apparatus 20, the potential for spillage, waste, inconsistent dispensing, inadvertent dispensing, and clumping due to humidity is eliminated.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Site-specific dynamic nuclear polarization NMR agents

Dynamic nuclear polarization (DNP) agents are provided for DNP nuclear magnetic resonance of analytes. The DNP agents can have the structure A-X-L-R, where A is none or an amphiphilic group; X is a coupling group capable of site-specific binding with the analyte or, when A is an amphiphilic group, capable of site-specific binding with the amphiphilic group; L is a bond or a linker group; and R is a poly-radical group. The poly-radical can be a di-radical, a tri-radical, a tetra-radical, or a combination thereof. Methods of NMR measurement of an analyte comprising an NMR-detectable nucleus are provided. The methods can include the steps of providing a frozen sample containing the analyte and a DNP agent; applying radiation having a frequency that excites electron spin transitions in the DNP agent at an intensity to polarize the NMR-detectable nucleus; and detecting a signal from nuclear spin transitions in the NMR-detectable nucleus.

Smirnov, Alexej I.↗
DOE OSTI2020

Method for generating and dispensing a powdered release agent

A method for generating and dispensing a powdered release agent during an additive manufacturing build is disclosed. A solid body of release agent material is ground insitu by a grinder and dispensed on a surface of the part to prevent adhesion of an adjacent layer of a base material. With the addition of the powdered release agent, a support structure is easily separated from the base material when the part is complete, saving time and preventing the part from sustaining unintentional damage. Since no powdered release agent is actually loaded or stored in the apparatus, the potential for spillage, waste, inconsistent dispensing, inadvertent dispensing, and clumping due to humidity is eliminated.

42 ENGINEERING↗
DOE OSTI2021

Multi-sensor agent devices

Apparatus, methods, and systems implementing multi-sensor agent devices are described herein. The agent devices can each include a plurality of sensors for measuring parameters of interest to an entity such as an electric power utility. The sensors can be organized in individually-IP-addressable sensor clusters, with each sensor cluster including an associated microcontroller. The agent devices can be controlled by a control center of the entity to operate in a coordinated manner, such as to gather and transmit data regarding parameters of interest. The agent devices can be transported to desired areas for data collection by unmanned aerial systems such as drones, and the collected data can be stored in a distributed blockchain ledger.

Fuhr, Peter L.↗
DOE OSTI2023

Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning

We consider the problem of power allocation over the 6 GHz Unlicensed National Information Infrastructure (UNII)- 5 spectrum. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm. In particular, we model the base stations (BSs) as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment, which consists of several local interference measurements and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS’s own critic which helps it to gain a more accurate perception of the overall radio environment. A centralized-training-distributed execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2011

Enzyme-linked immunosorbent assay for detection of organophosphorylated butyrylcholinesterase: A biomarker of exposure to organophosphate agents

A sandwich enzyme-linked immunosorbent assay (sELISA) is developed for detection of organophosphorylated butyrylcholinesterase (OP-BChE), a potential biomarker for human exposure to organophosphate insecticides and nerve agents. A pair of antibodies specific to OP-BChE adduct were identified through systematic screening of several anti BChE antibodies (anti-BChE) and anti-phosphoserine antibodies (anti-Pser) from different sources. The selected anti-BChE (set as capture antibody) antibodies recognize both phosphorylated and nonphosphorylated BChE. These antibodies can therefore be used to capture both BChE and OP-BChE from the sample matrices. The anti- Pser (set as detecting antibody) was used to recognize the OP moiety of OP-BChE adducts. With the combination of the selected antibody pair, several key parameters (such as the concentration of anti-BChE and anti-Pser, and the blocking agent) were optimized to enhance the sensitivity and selectivity of the sELISA. Under the optimal conditions, the sELISA has shown a wide linear range from 0.03 nM to 30 nM, with a detection limit of 0.03 nM. Furthermore, the sELISA was successfully applied to detect OP-BChE using in-vitro biological samples such as rat plasma spiked with OP-BChE with excellent adduct recovery (z>99 %). These results demonstrate that this novel approach holds great promise to develop an ELISA kit and offers a simple and cost-effective tool for screening/evaluating exposure to organophosphate insecticides and nerve agents.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗
DOE OSTI2019

Agent-Based Dynamic Traffic Assignment with Information Mixing

This study develops an approach for network assignment convergence using individualized agent-based routing and agent-specific link cost information as well as historical travel times. Each traveler is routed individually from their starting and ending network link at a specific departure time. The approach is gap-based in two ways. First, as in previous approaches, the re-assignment decision during the convergence process is based on the gap between the routed and experienced travel time from the previous iteration. Secondly, the historical time-dependent and prevailing traffic conditions are averaged into a single expected value for each agent using a weight calculated by a modified two-parameter Weibull survival function. This weight is individualized based on the relative gap of the traveler from the previous iteration, as well as the iteration number; a novel aspect of this work. The methodology is tested on a medium-scale network of Bloomington, IL. The algorithm converges after only two iterations, which is promising as computational time of a single iteration can be high for large-scale networks.

agent-based modeling↗
DOE OSTI2016

Crystal Structure of Carboxyltransferase from Staphylococcus aureus Bound to the Antibacterial Agent Moiramide B

The dramatic increase in the prevalence of antibiotic-resistant bacteria has necessitated a search for new antibacterial agents against novel targets. Moiramide B is a natural product, broad-spectrum antibiotic that inhibits the carboxyltransferase component of acetyl-CoA carboxylase, which catalyzes the first committed step in fatty acid synthesis. Herein, we report the 2.6 Å resolution crystal structure of moiramide B bound to carboxyltransferase. An unanticipated but significant finding was that moiramide B bound as the enol/enolate. Crystallographic studies demonstrate that the (4S)-methyl succinimide moiety interacts with the oxyanion holes of the enzyme, supporting the notion that an anionic enolate is the active form of the antibacterial agent. Structure–activity studies demonstrate that the unsaturated fatty acid tail of moiramide B is needed only for entry into the bacterial cell. Furthermore, these results will allow the design of new antibacterial agents against the bacterial form of carboxyltransferase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Robust Processing of Small-Molecule:Fullerene Organic Solar Cells via Use of Nucleating Agents

The power conversion efficiency (PCE) of small-molecule bulk heterojunction solar cells is highly sensitive to the 'ink' formulation used to produce the photoactive layer. Here we demonstrate that the addition of nucleating agents renders device fabrication notably less susceptible to the ink composition, promising a route toward more robust processing of efficient devices over large areas and enabling more facile materials screening. We selected as a model system blends of 7,7-[4,4-bis(2-ethylhexyl)-4H-silolo[3,2-b:4,5-b]dithiophene-2,6-diyl]bis[6-fluoro-4-(5-hexyl-[2,2-bithiophen]-5-yl)benzo[c][1,2,5]thiadiazole](p-DTS(FBTTh2)2) as the donor and [6,6]-phenyl C71-butyric acid methyl ester (PC71BM) as the acceptor because this is one of the small-molecule OPV blends with a device performance that is most sensitive to ink formulation, especially when used with the processing aid diiodooctane (DIO). Addition of DIO is essential to obtain high device performances; however, a notable increase in device performance is only achieved over a very narrow DIO content regime. Use of nucleating agents drastically changes this situation and leads to well-performing devices even at extreme levels of DIO. We thus start to address here one of the great challenges in organic solar cell research: the fact that, too often, only a very limited composition range leads to high efficiency devices. This means that for every new donor or acceptor a multitude of formulations have to be tested, including in combination with processing aids, to ensure that promising materials are not overlooked. The use of nucleating agents, thus, promises to render materials discovery more straightforward as this dependency of device performance with composition can be reduced.

14 SOLAR ENERGY↗
DOE OSTI2026

MCP-enabled agentic AI workflow for building energy modelling: framework and use cases

Traditional building energy modelling workflows remain labor-intensive and error-prone, requiring specialized expertise that limits broader adoption. This paper introduces a novel Model Context Protocol (MCP)-enabled framework that connects AI assistants to EnergyPlus through MCP, a standardized interface for tool invocation and context management. Two complementary integration paradigms are presented and compared: conversational integration, where users interact through natural language while an AI assistant orchestrates MCP tools on demand, and agentic workflow integration, where specialized agents coordinate autonomously to complete multi-step tasks. Using an experimental testbed for residential buildings, the end-to-end workflows are demonstrated. The conversational approach reduced typical inspection and modification tasks from 1-2 h to under 15 min, while maintaining full transparency through visible tool invocations. The agentic approach automated parametric analysis. These demonstrations establish MCP as a foundational layer for AI-assisted building energy modelling, enabling natural language interactions with simulation tools while preserving professional oversight and decision-making authority.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Agent-Based Distributed Energy Resources for Supporting Intelligence at the Grid Edge

This article proposes a novel multi-agent framework that can link various forms of resources and power electronic systems into distributed energy resources (DER). The proposed multiagent architecture can also integrate DERs to a central controller for optimization and control to support the grid. To demonstrate the flexibility of this novel framework, the developed agent system is applied to a set of end-use systems. Furthermore, the agent framework is validated in hardware using controller-hardware-in-the-loop simulation platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗