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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
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 OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2023

Improved chemical synthesis, identification and evaluation of prospective centrally active oxime antidotes for the treatment of nerve agent exposure

In this study, an improved method for the synthesis of hydroxyiminoacetamide-based oximes, compounds rhat hold great promise as centrally-active nerve agent antidotes, is described. The method involves the coupling of hydroxyiminoacetic acid to various amines in the presence of HOBT or HATU and diisopropylcarbodiimide. The optimized protocol was found to work effectively for the coupling with amines to the activated form of the hydroxyiminoacetic acid with yields ranging between 84 and 90% for primary amines, 65–75% for α-substituted and cyclic amines, while producing decent 41–48% yields for the sterically hindered α,α-substituted amines, that can not be obtained using the previously established method.

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

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making

High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents—retrieval, description refiner, classifier, validator, and feedback logger—coordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an "Item to Evidence to Decision" loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts—illustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2022

Radiolytic evaluation of select sulfur chlorides (S 2 Cl 2 and SOCl 2 ) for advanced low temperature chlorination of zirconium-based used nuclear fuel cladding

New sulfur chloride compounds—sulfur monochloride (S 2 Cl 2 ) and thionyl chloride (SOCl 2 )—have been proposed as alternative chlorinating agents for the chemical decladding of zirconium-based used nuclear fuel cladding materials. However, the radiation stability of these compounds has not been thoroughly evaluated, despite envisioned process conditions involving intense, multi-component radiation fields. Knowledge of the radiation chemistry of these sulfur chlorides is essential for the development and optimization of alternative chlorination technologies. To this end, we present preliminary findings on the gamma radiolysis of neat S 2 Cl 2 and SOCl 2 at ambient temperature up to ~ 34 MGy. In this study, Raman spectroscopy and density functional theory calculations were used to identify the Raman-active degradation products from S 2 Cl 2 and SOCl 2 radiolysis. Both sulfur chloride compounds exhibited significant radiation resistance, with respect to changes in the Raman signatures of the parent compounds and the ingrowth of Raman-active degradation products. For S 2 Cl 2 , molecular chlorine (Cl 2 ) was directly identified as the predominant degradation product, which was completely consumed at higher absorbed doses (> 9 MGy). Similarly, the main degradation product from SOCl 2 radiolysis was also Cl 2 , although in this system it continued to grow in with dose over the entire dose range. Further, an “S=O” containing degradation product(s) was also identified as a compliment to Cl 2 . Overall, the perceived radiation resistance of these sulfur chloride compounds makes them suitable for inclusion in a used nuclear fuel chemical decladding process, especially as radiolytically formed Cl 2 can be used to reform the parent compounds, thereby increasing the longevity of the solvents used.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

The ballad of LLM agents: philosophical reasoning for chemistry

Large language models (LLMs) show remarkable potential for scientific reasoning but often produce unreliable or scientifically unactionable outputs when faced with multi-step logic, domain grounding, and interpretability challenges, especially in complex fields like chemistry and materials science. Here, we introduce a framework of philosophical reasoning agents, inspired by canonical thinkers such as Socrates, Descartes, Kant, and Hume, to guide LLM behavior via structured prompt engineering. These agents embody distinct reasoning paradigms (dialectical inquiry, deductive logic, rule-based judgment, and empirical validation) and are evaluated across multiple chemistry subdomains, physical, analytical, general, inorganic, and organic chemistry, using the ChemBench benchmark. Our agentic prompting approach yields substantial accuracy gains on open-ended numerical chemistry questions, with gains of +11.5 percentage points for GPT-4o with Hume, +4.5 percentage points for GPT-5 with Kant, and +21.8 percentage points for GPT-5.1 with Socrates at the strict 1% error threshold, relative to the corresponding base models. Beyond accuracy, we observe benchmark-level model–agent performance patterns, suggesting that different prompting styles interact differently with each base model. These findings demonstrate that embedding philosophy-of-science principles into multi-agent frameworks can improve and produce interpretable, adaptive, and domain-aligned scientific LLMs.

Harb, Hassan [Argonne National Laboratory (ANL), A↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2020

Distributed Detection of Malicious Attacks on Consensus Algorithms with Applications in Power Networks

Consensus-based distributed algorithms are well suited for coordination among agents in a cyber-physical system. These distributed schemes, however, suffer from their vulnerability to cyber attacks that are aimed at manipulating data and control ow. In this article, we present a novel distributed method for detecting the presence of such intrusions for a distributed multi-agent system following ratio consensus. We employ a Max-Min protocol to develop low cost, easy to implement detection strategies where each participating node detects the intrusion independently, eliminating the need for a trusted certifying agent in the network. The effectiveness of the detection method is demonstrated by numerical simulations on a 1000 node network to demonstrate the efficacy and simplicity of implementation.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2025

ADEPT: A Pedagogical Framework for Integrating Agentic AI with Deterministic Scientific Workflows

The integration of Large Language Models (LLMs) into scientific research promises to accelerate discovery, yet a significant gap remains between the dynamic reasoning of Artificial Intelligence (AI) agents and the static, deterministic nature of canonical scientific workflows. This paper introduces ADEPT (Agentic Discovery and Exploration Platform for Tools), a reference architecture and pedagogical framework explicitly designed to bridge this gap. ADEPT's primary mission is to provide a transparent, "glass-box" environment where researchers and engineers can learn to effectively wrap established scientific software (e.g., BLAST, Nextflow pipelines) and compose it into reliable, agent-driven workflows. We describe its modular, multi-server architecture, which leverages the Model Context Protocol (MCP) for tool serving, LangGraph for robust agentic orchestration, and a secure nsjail-based sandbox for safe code execution. By prioritizing architectural clarity, safety, and modularity, ADEPT serves as an extensible blueprint for building trustworthy AI-augmented systems and fosters the collaborative development necessary to responsibly employ agentic AI for science. We provide practical examples of how to adapt and extend this framework, highlighting its utility in workforce development and AI-readiness capabilities across research and development projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Design and Gas Separation Performance of Imidazolium Poly(ILs) Containing Multivalent Imidazolium Fillers and Crosslinking Agents

This work introduces a series of vinyl-imidazolium-based polyelectrolyte composites, which were structurally modified via impregnation with multivalent imidazolium-benzene ionic liquids (ILs) or crosslinked with novel cationic crosslinkers which possess internal imidazolium cations and vinylimidazolium cations at the periphery. A set of eight [C4vim][Tf2N]-based membranes were prepared via UV-initiated free radical polymerization, including four composites containing di-, tri-, tetra-, and hexa-imidazolium benzene ILs and four crosslinked derivatives which utilized tri- and tetra- vinylimidazolium benzene crosslinking agents. Structural and functional characterizations were performed, and pure gas permeation data were collected to better understand the effects of “free” ILs dispersed in the polymeric matrix versus integrated ionic crosslinks on the transport behaviors of these thin films. These imidazolium PIL:IL composites exhibited moderately high CO2 permeabilities (~20–40 Barrer), a 4–7× increase relative to corresponding neat PIL, with excellent selectivities against N2 or CH4. The addition of imidazolium-benzene fillers with increased imidazolium content were shown to correspondingly enhance CO2 solubility (di- < tri- < tetra- < hexa-), with the [C4vim][Tf2N]: [Hexa(Im+)Benz ][Tf2N] composite showing the highest CO2 permeability (PCO2 = 38.4 Barrer), while maintaining modest selectivities (αCO2/CH4 = 20.2, αCO2/N2 = 23.6). Additionally, these metrics were similarly improved with the integration of more ionic content bonded to the polymeric matrix; increased PCO2 with increased wt% of the tri- and tetra-vinylimidazolium benzene crosslinking agent was observed. This study demonstrates the intriguing interactions and effects of ionic additives or crosslinkers within a PIL matrix, revealing the potential for the tuning of the properties and transport behaviors of ionic polymers using ionic liquid-inspired small molecules.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantitative phase microscopy monitors subcellular dynamics in single cells exposed to nanosecond pulsed electric fields

A substantial body of literature exists to study the dynamics of single cells exposed to short duration (<1 μs), high peak power (~1 MV/m) transient electric fields. Much of this research is limited to traditional fluorescence-based microscopy techniques, which introduce exogenous agents to the culture and are only sensitive to a single molecular target. Quantitative phase imaging (QPI) is a coherent imaging modality which uses optical path length as a label-free contrast mechanism, and has proven highly effective for the study of single-cell dynamics. In this work, we introduce QPI as a useful imaging tool for the study of cells undergoing cytoskeletal remodeling after nanosecond pulsed electric field (nsPEF) exposure. In particular, we use cell swelling, dry mass and disorder strength measurements derived from QPI phase images to monitor the cellular response to nsPEFs. We hope this demonstration of QPI's utility will lead to a further adoption of the technique for the study

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Metabolic flux modeling of Gluconobacter oxydans enables improved production of bioleaching organic acids

Bioleaching with organic acids is an option for recovery of rare earth elements (REE) and or other valuable metals from consumer or industrial wastes. Biohydrometallurgy can play an economically and environmentally sustainable role in REE recycling, particularly if agriculture or food wastes are used as the carbon substrates for production of the bioleaching agents. A challenge for organic acid based REE bioleaching is the incomplete conversion of the carbon substrate to an optimal organic acid mixture with high REE extraction capability. To help meet this challenge a metabolic flux model was developed for Gluconobacter oxydans to identify options for improving sugar conversion to chelating organic acids. Here, model predictions were consistent with laboratory data for the microbial consumption of glucose and production of gluconic acid and suggested that providing growth conditions with relatively high carbon to nitrogen molar ratios could result in production of lower pH biolixiviant and thus improve REE bioleaching yields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries

The rapid growth of automated and autonomous instrumentation brings forth opportunities for the co-orchestration of multimodal tools that are equipped with multiple sequential detection methods or several characterization techniques to explore identical samples. This is exemplified by combinatorial libraries that can be explored in multiple locations via multiple tools simultaneously or downstream characterization in automated synthesis systems. In co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, an orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Herein, we propose and implement a co-orchestration approach for conducting measurements with complex observables, such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure and integration into an iterative workflow via multi-task Gaussian Processes (GPs). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GPs. We illustrate this method for different modes of piezoresponse force microscopy and micro-Raman spectroscopy on a combinatorial Sm-BiFeO3 library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of the measured signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

High-sensitivity in vivo contrast for ultra-low field magnetic resonance imaging using superparamagnetic iron oxide nanoparticles

Magnetic resonance imaging (MRI) scanners operating at ultra-low magnetic fields (ULF; <10 mT) are uniquely positioned to reduce the cost and expand the clinical accessibility of MRI. A fundamental challenge for ULF MRI is obtaining high-contrast images without compromising acquisition sensitivity to the point that scan times become clinically unacceptable. Here, we demonstrate that the high magnetization of superparamagnetic iron oxide nanoparticles (SPIONs) at ULF makes possible relaxivity- and susceptibility-based effects unachievable with conventional contrast agents (CAs). We leverage these effects to acquire high-contrast images of SPIONs in a rat model with ULF MRI using short scan times. This work overcomes a key limitation of ULF MRI by enabling in vivo imaging of biocompatible CAs. These results open a new clinical translation pathway for ULF MRI and have broader implications for disease detection with low-field portable MRI scanners.

42 ENGINEERING↗
DOE OSTI2023

Structure of BTK kinase domain with the second-generation inhibitors acalabrutinib and tirabrutinib

Bruton’s tyrosine kinase (BTK) is the target of the therapeutic agent, Ibrutinib, that treats chronic lymphocyte leukemia (CLL), mantle cell lymphoma (MCL) and other B cell malignancies. Ibrutinib is a first in class, covalent BTK inhibitor that limits B-cell survival and proliferation. Designing new inhibitors of BTK has been an important objective for advancing development of improved therapeutic agents against cancer and autoimmune disorders. Based on the success of Ibrutinib, several second-generation irreversible BTK inhibitors have been developed that exhibit fewer off-target effects. However, the binding-mode and their interaction with Btk have not been experimentally determined and evaluated at atomic resolution. Here we determined the first crystal structure of the BTK kinase domain in complex with acalabrutinib. In addition, we report a structure of the BTK/tirabrutinib complex and compare these structures with previously solved structures. The structures provide insight in the superior selectivity reported for acalabrutinb and guide future BTK inhibitor development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Lignin - A green material for antibacterial application — A review

Lignin's antibacterial properties have become increasingly relevant due to the rise of microbial infectious diseases and antibiotic resistance. Lignin is capable of interacting electrostatically with bacteria and contains polyphenols that cause damage to their cell walls. These features make lignin a desirable material to exhibit antibacterial behavior. Therefore, lignin in antibacterial applications offers a novel approach to address the growing need for sustainable and effective antibacterial materials. Recent research has explored the incorporation of lignin in various biomedical applications, such as wound dressings, implants, and drug delivery systems, highlighting their potential as a sustainable alternative to synthetic antibacterial agents. Furthermore, the development of lignin-based nanomaterials with enhanced antimicrobial activity is an active area of research that holds great promise for the future. Here, in this review, we have provided a summary of how lignin can be incorporated into different forms, such as composite and non-composite synthesis of antibacterial agents and their performances. The challenges and future considerations are also discussed in this review article.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Impact of Blockchain Delay on Grid-Tied Solar Inverter Performance

This paper investigates the impact of the delay resulting from a blockchain, a promising security measure, for a hierarchical control system of inverters connected to the grid. The blockchain communication network is designed at the secondary control layer for resilience against cyberattacks. To represent the latency in the communication channel, a model is developed based on the complexity of the blockchain framework. Taking this model into account, this work evaluates the plant’s performance subject to communication delays, introduced by the blockchain, among the hierarchical control agents. In addition, this article considers an optimal model-based control strategy that performs the system’s internal control loop. The work shows that the blockchain’s delay size influences the convergence of the power supplied by the inverter to the reference at the point of common coupling. In the results section, real-time simulations on OPAL-RT are performed to test the resilience of two parallel inverters with increasing blockchain complexity.

41 EE - Solar Energy Technologies Office (EE-4S)↗