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At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Enhanced oxygen transfer agent systems for oxidative dehydrogenation of hydrocarbons

Aspects of the invention relate to enhanced oxygen transfer agent systems and methods of use thereof. According to one aspect, a method for producing olefins from a hydrocarbon feed includes the step of contacting a hydrocarbon feed comprised of one or more alkanes with an oxygen transfer agent at a temperature of 350° C. to 1000° C. The oxygen transfer agent comprising an oxygen-donating chalcogen agent comprised of at least one of S, Se, or Te and a reducible metal oxide. The chalcogen having an oxidation state greater than +2. According to another aspect, a method for producing one or more olefins by partial combustion of a hydrocarbon feed includes partially combusting a hydrocarbon feed comprised of one or more alkanes by contacting the hydrocarbon feed with an oxygen transfer agent comprising CaS04 at a temperature of 350° C. to 1000° C. to produce one or more olefins comprising ethylene and coproducing water.

Sofranko, John A.↗

DOE OSTI2022

Enhanced oxygen transfer agent systems for oxidative dehydrogenation of hydrocarbons

Enhanced oxygen transfer agent systems and methods of use thereof are provided. According to one aspect, a method for producing olefins from a hydrocarbon feed includes the step of contacting a hydrocarbon feed comprised of one or more alkanes with an oxygen transfer agent at a temperature of 350° C. to 1000° C. The oxygen transfer agent includes an oxygen-donating chalcogen agent including at least one of S, Se, or Te and a reducible metal oxide. The chalcogen has an oxidation state greater than +2. A method for producing one or more olefins by partial combustion of a hydrocarbon feed is provided. The method includes partially combusting a hydrocarbon feed comprised of one or more alkanes by contacting the hydrocarbon feed with an oxygen transfer agent comprising CaSO 4 at a temperature of 350° C. to 1000° C. to produce one or more olefins comprising ethylene and coproducing water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Chemical and Foaming Stability of Foam Decontaminating Agents - 16137

This study conducted experiments to evaluate the stability of nano foaming agents by adding various types of surfactants, silica nanoparticles, and viscosifiers with the aim of enhancing the stability of foaming agents containing nanoparticles. Nonionic surfactants such as EM100 showed higher stability as compared to the Sodium Dodecyl Sulfate (SDS) anionic surfactant, and the foam stability decreased as the surfactant concentration increased. The co-surfactant addition did not significantly affect the foam stability, but the addition of Xantan gum-one of the various viscosifiers tested in this study-resulted in the highest foam stability. When silica nanoparticles were added to the surfactant EM100, the foam stability improved further. In particular, the foam stability of the nano foaming agent was observed to be the maximum when a partially hydrophobic nano-porous silica such as KAERI 1 was added, as compared to the addition of a hydrophilic dry silica such as M5. This is because the partially hydrophobic particles were distributed over the liquid film between the foams, thereby preventing drainage and increasing the stability of the foaming agent fluid. The manufactured nano foaming agent can be used not only for the effective decontamination of large-sized equipment or large facilities but also for overcoming the problem of production of a large amount of radioactive waste during chemical decontamination. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Towards Agentic AI on Particle Accelerators

As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where intelligent agents handle high-level tasks and communication and each agent is specialized control individual accelerator components. This approach raises some questions: What are the future applications of AI in particle accelerators? How can we implement an autonomous complex system such as a particle accelerator where agents gradually improve through experience and human feedback? What are the implications of integrating a human-in-the-loop component for labeling operational data and providing expert guidance? We show two examples, where we demonstrate viability of such architecture.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics

We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can be easily checked in the computational backend. We identify two complementary remedies, tool-constrained computation and targeted knowledge grounding, and pursue the first as the primary architecture. Concretely, we concentrate the agent's action distribution onto tool calls with convention-fixing semantics, in which the agent specifies a compact, human-auditable diagram specification and a trusted backend performs the symbolic or numerical manipulations exactly. The toolkit provides two complementary calculation paths consuming a shared diagram specification: Naive Dimensional Analysis (NDA) for order-of-magnitude rate estimates and Exact Diagrammatic Analysis (EDA) for tree-level symbolic calculations via automatic FeynCalc code generation, both supplemented by automatic Feynman diagram enumeration and a navigable theory knowledge base. The architecture is validated on two benchmarks: (1) an exhaustive catalog of all tree-level, single-vertex $1\to 2$ partial decay widths across scalar, fermion, and vector parents, with complete massless and threshold limits and Standard Model validation; and (2) an NDA sensitivity study of the muon decay multiplicity $μ^+ \to ν_μ\barν_e + n(e^+e^-) + e^-$, determining the maximum observable $n$ at current and planned muon experiments.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗

DOE OSTI2016

Effective methylation of phosphonic acids related to chemical warfare agents mediated by trimethyloxonium tetrafluoroborate for their qualitative detection and identification by gas chromatography-mass spectrometry

The effective methylation of phosphonic acids related to chemical warfare agents (CWAs) employing trimethyloxonium tetrafluoroborate (TMO·BF 4 ) for their qualitative detection and identification by gas chromatography-mass spectrometry (GC-MS) is presented. The methylation occurs in rapid fashion (1 h) and can be conveniently carried out at ambient temperature, thus providing a safer alternative to the universally employed diazomethane-based methylation protocols. Optimization of the methylation parameters led us to conclude that methylene chloride was the ideal solvent to carry out the derivatization, and that even though methylated products can be observed surfacing after only 1 h, additional time was not found to be detrimental but beneficial to the process particularly when dealing with analytes at low concentrations (~10 μg mL -1 ). Due to its insolubility in methylene chloride, TMO·BF 4 conveniently settles to the bottom during the reaction and does not produce additional interfering by-products that may further complicate the GC-MS analysis. We demonstrated the method to successfully methylate a variety of Schedule 2 phosphonic acids, including their half esters, resulting in derivatives that were readily detected and identified using the instrument's spectral library. Most importantly, the method was shown to simultaneously methylate a mixture of the organophosphorus-based nerve agent hydrolysis products: pinacolyl methylphosphonate (PMPA), cyclohexyl methylphosphonate (CyMPA) and ethyl methylphosphonate (EMPA) (at a 10 μg mL - 1 concentration each) in a fatty acid ester-rich organic matrix (OPCW-PT-O 3 ) featured in the 38th Organisation for the Prohibition of Chemical Weapons (OPCW) Proficiency Test. Additionally, the protocol was found to effectively methylate N,N-diethylamino ethanesulfonic acid and N,N-diisopropylamino ethanesulfonic acid that are products arising from the oxidative degradation of the V-series agents VR and VX respectively. This work described herein represents the first report on the use of TMO·BF 4 as a viable, stable and safe agent for the methylation of phosphonic acids and their half esters and within the context of an OPCW Proficiency Test sample analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach

Context: The world is experiencing frequent extreme weather events like droughts, snowstorms, and shifting of seasons due to climate change. Increased frequency and severity of these extreme weather events threaten food security because agriculture depends on climate conditions. Impacts of climate change on the agricultural system not only occur at the grower’s level, but also cascade to other levels along the supply chain. Objective: In this study, we aim to quantify a wide range of economic impacts of different extreme climate events on different stages of a food supply chain. Methods: We chose the potato supply chain in Idaho as a case study. We developed a multi-echelon supply chain simulation model using an agent-based modeling (ABM) approach with five types of agents—farmers, shippers, processors, retailers, and logistics companies. In addition to the business-as-usual (BAU) scenario, we designed two climate-related disruption events—drought and snowstorm. We quantified the heterogeneous impacts at different stages of the supply chain for both fresh and processed potatoes using key performance indicators (KPIs) including revenues, prices, lead times, traded quantities, and food waste quantity. Results and Conclusion: The impacts of the disruption events are different on different agents in the supply chain for different product categories. The price hike of fresh potatoes is far higher than processed potatoes during disruption events. This price hike makes consumers switch to processed potatoes, which require more fresh potatoes as an input that further reinforces the price hike. However, because processed potatoes have an elastic demand, once their prices go up due to higher input cost, their demand drops. Non-contracted farmers gain additional revenues from the disruption events, whereas contracted farmers incur a loss due to lock-in price and lower than usual harvest. Significance: The methodology developed in this study could be applied to other food and agricultural supply chains for understanding the vulnerabilities at agent levels due to climate change disruption events. Furthermore, the findings would help develop mitigation strategies or policies to improve the well-being of the overall supply chain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗

DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers

This paper introduces a novel physics-informed multi-agents constitutive model to propose prediction in quasi-static constitutive behavior of cross-linked elastomer and the loss of mechanical performance during environmental aging. The presented model is used to simulate the effect of single-mechanism chemical aging (i.e. thermal-inducedor hydrolytic aging) on the behavior of the material in this hybrid framework. Those environmental single-mechanism damages change the polymer matrix over time due to massive chain scission, chain formations, and changing the arrangement of molecules in the polymer matrix. Here we propose a data-driven super-constrained machine-learned engine to represent damage in the polymer matrix and capture the changes in material behavior, including its inelastic features such as Mullins effect and permanent set in the course of aging. We have simplified the 3D stress–strain tensor mapping problem into a small number of super-constrained 1D mapping problems by means of a sequential order reduction. An assembly of multiple replicated conditional neural-network learning-agents (L-agents) is trained to systematically simplify the high-dimensional mapping problem into multiple 1D problems, each represented by a different type of agent. Our hybrid framework is designed to capture the effect of deformation history, aging time, and aging temperature. The model is validated with respect to a comprehensive set of experiments specifically designed to benchmark model capabilities and also against available data in the literature. Thermodynamic consistency and frame independency have been verified. Besides acceptable predictive abilities, a significant reduction of computational cost to predict behavior at multiple states of deformation is the most significant feature of this model.

42 ENGINEERING↗

DOE OSTI2023

Determination of aquatic hazard concentrations for the oil spill response product class of surface washing agents using species sensitivity distributions

Surface washing agents (SWAs) are a diverse class of oil spill response products intended to facilitate removal of stranded oil from shorelines. This class of agents has high application rates relative to other categories of spill response products, but global toxicity data is generally limited to two standard test species: inland silverside and mysid shrimp. Here, in this study, we provide a framework to maximize the utility of limited toxicity data across a product class. To characterize species sensitivity to SWAs, the toxicity of three agents spanning a range of chemical and physical properties were tested in eight species. The relative sensitivity of mysids shrimp and inland silversides as surrogate test organisms was determined. Toxicity normalized species sensitivity distributions (SSDn) were used to estimate fifth centile hazard concentration (HC5) values for SWAs with limited toxicity data. Chemical toxicity distributions (CTD) of SWA HC5 values were used to compute a fifth centile chemical hazard distribution (HD5) to provide a more comprehensive assessment of hazard across a spill response product class with limited toxicity data than traditional single species or single agent approaches can give.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2020

A large-scale, agent-based simulation of metropolitan freight movements with passenger and freight market interactions

This study summarizes the first stage in the implementation of an agent-based freight modeling system that has a global representation of agents and detailed modeling of a large-scale transportation network. The model is used to evaluate the transportation and energy impacts of goods movement across urban and national scales. The framework is implemented within POLARIS, a C++-based Planning and Operations Language for Agent-based Regional Integrated Simulation, which consists of an activity-based modeling (ABM) and dynamic traffic assignment (DTA) system that has robust features for passenger travel. This platform provides a tool to model interactions among consumers, producers, and the transportation system. The main objective of this initial implementation is to implement a freight model within POLARIS following an agent-based paradigm with behavioral and simulation methods. This paper presents the initial framework and illustrates the application of the model. Building upon earlier works, a parcel location assignment algorithm for business establishments in the population is documented, along with a method for estimating establishment production and consumption volumes. In addition to population generation, other features of the model include push-pull supply chains, multimodal path choice, choice of transportation logistics node, and dynamic traffic assignment. A module with e-commerce supply and demand was also developed to analyze the effects of e-commerce delivery on last-mile energy use and congestion.

agent-based freight↗

DOE OSTI2019

Distributed output-feedback model predictive control for multi-agent consensus

We introduce a distributed output-feedback model predictive control approach for achieving consensus among multiple agents. Each agent computes a distributed control action based on an output-feedback measurement of a local neighborhood tracking error and communicates information only to its neighbors, according to a communication network modeled as a directed graph. Each agent computes its distributed control action by solving a local min–max optimization problem that simultaneously computes a local state estimate and control input under worst-case assumptions on unmeasured input disturbances and measurement noise. Under easily verified controllability and observability assumptions, this distributed output-feedback model predictive control approach provides an upper bound on the group consensus error, thereby ensuring practical consensus in the presence of unmeasured disturbances and noise. A numerical example with four agents connected in a directed graph is demonstrated to illustrate the results.

42 ENGINEERING↗

DOE OSTI2017

Polypyrrole-Coated Perfluorocarbon Nanoemulsions as a Sono-Photoacoustic Contrast Agent

A new contrast agent for combined photoacoustic and ultrasound imaging is presented here. It has a liquid perfluorocarbon (PFC) core of about 250 nm diameter coated by a 30 nm thin polypyrrole (PPy) doped polymer shell emulsion that represents a broadband absorber covering the visible and near-infrared ranges (peak optical extinction at 1050 nm). When exposed to a sufficiently high intensity optical or acoustic pulse, the droplets vaporize to form microbubbles providing a strong increase in imaging sensitivity and specificity. The threshold for contrast agent activation can further drastically be reduced by up to 2 orders of magnitude if simultaneously exposing them with optical and acoustic pulses. The selection of PFC core liquids with low boiling points (i.e., perfluorohexane (56 °C), perfluoropentane (29 °C), and perfluorobutane (–2 °C)) facilitates activation and reduces the activation threshold of PPy-coated emulsion contrast agents to levels well within clinical safety limits (as low as 0.2 MPa at 1 mJ/cm 2 ). Finally, the potential use of these nanoemulsions as a contrast agent is demonstrated in a series of phantom imaging studies.

42 ENGINEERING↗

DOE OSTI2019

A Bismuth Metal–Organic Framework as a Contrast Agent for X-ray Computed Tomography

A new bismuth metal–organic framework (MOF), bismuth-NU901 (Bi-NU-901), featuring the scu topology and a pore with a diameter of ~11 Å, was solvothermally synthesized, and its use as an X-ray computed tomography (CT) contrast agent was tested. X-ray CT is a common diagnostic method used in the medical field. Inside the body, contrast media enhance the distinction between tissues and organs of similar density. Bi-NU-901 consists of eight connected Bi 6 nodes and tetratopic 1,3,5,8-( p -benzoate)pyrene linkers (TBAPy). Numerous material characterization studies including powder X-ray diffraction (PXRD), scanning transmission electron microscopy (STEM), and DFT pore size distribution support the scu structure. Additionally, given the framework’s high density of nontoxic heavy atoms, Bi-NU-901 was evaluated as an X-ray computed tomography (CT) agent. Importantly, in vitro studies revealed this new bismuth MOF demonstrates p7 times better contrast intensity compared to a zirconium MOF featuring the same topology and ~14 times better contrast than a commercially available CT contrast agent. Furthermore these results suggest bismuth MOFs may be promising CT contrast agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Multifunctional ultra-high vacuum apparatus for studies of the interactions of chemical warfare agents on complex surfaces

A fundamental understanding of the surface chemistry of chemical warfare agents is needed to fully predict the interaction of these toxic molecules with militarily relevant materials, catalysts, and environmental surfaces. For example, rules for predicting the surface chemistry of agents can be applied to the creation of next generation decontaminants, reactive coatings, and protective materials for the warfighter. Here, we describe a multifunctional ultra-high vacuum instrument for conducting comprehensive studies of the adsorption, desorption, and surface chemistry of chemical warfare agents on model and militarily relevant surfaces. The system applies reflection-absorption infrared spectroscopy, x-ray photoelectron spectroscopy, and mass spectrometry to study adsorption and surface reactions of chemical warfare agents. Several novel components have been developed to address the unique safety and sample exposure challenges that accompany the research of these toxic, often very low vapor pressure, compounds. While results of vacuum-based surface science techniques may not necessarily translate directly to environmental processes, learning about the fundamental chemistry will begin to inform scientists about the critical aspects that impact real-world applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Towards an Agent-Based Blackboard System for Reactor Design Optimization

The field of reactor design is rich with opportunities for applications of computational optimization algorithms; these applications can range from preliminary core design to reactor shuffling patterns. Many of these schemes rely on sets of previously generated solutions (sometimes referred to as “generations”) to inform future decisions. While it is important to build upon prior knowledge, this process requires a full generation of solutions to be formed before future solutions can be examined. Rather than relying on a generational scheme to perform an optimization, we propose using an agent-based approach in conjunction with a blackboard framework for performing reactor design optimizations. Utilizing an agent-based approach allows agents to perform tasks independently, while retaining the ability to build off of previous solutions. We develop an agent-based blackboard system (ABBS) for determining the Pareto front (PF) in sodium fast reactor design optimization problems and compared this with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Our goal is to evaluate the viability of the ABBS in producing a PF that is comparable with the NSGA-II algorithm. The design space consists of the fuel height, fuel smear, and plutonium fraction in the core, and we seek to minimize the reactivity swing and plutonium mass, while maximizing the burnup. The diversity, coverage, and spread of the PFs generated by the two methods are examined, and the ABBS is able to converge to the same PF as the NSGA-II algorithm. These results show that the ABBS is able to find optimal designs that are similar to those found by the NSGA-II algorithm. We conclude our study by applying the ABBS to the design of a sodium-cooled fast reactor to dispose of weapons-grade plutonium. The ABBS finds a core design that can burn upwards of 17.5 kg of weapons-grade plutonium per year and degrade an additional 195 kg of weapons-grade plutonium per year into non-weapons-grade material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs

This paper proposes attention enabled multi-agent deep reinforcement learning (MADRL) framework for active distribution network decentralized Volt-VAR control. Using the unsupervised clustering, the whole distribution system can be decomposed into several sub-networks according to the voltage and reactive power sensitivity relationships. Then, the distributed control problem of each sub-network is modeled as Markov games and solved by the improved MADRL algorithm, where each sub-network is modeled as an adaptive agent. An attention mechanism is developed to help each agent focus on specific information that is mostly related to the reward. All agents are centrally trained offline to learn the optimal coordinated Volt-VAR control strategy and executed in a decentralized manner to make online decisions with only local information. Compared with other distributed control approaches, the proposed method can effectively deal with uncertainties, achieve fast decision makings, and significantly reduce the communication requirements. Comparison results with model-based and other data-driven methods on IEEE 33-bus and 123-bus systems demonstrate the benefits of the proposed approach.

distribution network↗