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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

One-dimensional heterocyclic carbene–Au metal–organic frameworks bridging ultra-high vacuum models and scalable liquid-phase growth

The controlled design of molecule–metal interfaces is central to the development of functional nanomaterials for catalysis, sensing, and molecular electronics. Here we show that the adsorption of a Janus-type diimidazolium precursor on gold yields one-dimensional (1D) N-heterocyclic carbene (NHC)–Au–NHC metal organic frameworks (MOFs) featuring positively charged gold nodes. Using synchrotron X-ray photoemission spectroscopy (XPS), near edge X-ray adsorption fine structure (NEXAFS) spectroscopy and scanning tunnelling microscopy (STM), we demonstrate that thermal activation promotes counterion removal and drives the formation of extended 1D arrays, characterized by ∼1.0 nm Au–Au spacing and adatom densities up to 0.6 atom nm −2 (∼4% of surface atoms). Importantly, we translate this ultra-high vacuum (UHV) benchmark into a scalable solution-phase protocol in ethanol, enabling 1D-MOF growth under mild, base-free, open-air conditions. The resulting films retain structural and electronic signatures of UHV-grown systems, bridging model studies and practical synthesis. This approach establishes NHC–metal frameworks as accessible, tunable platforms for catalysis and materials design.

Gold adatoms↗
DOE OSTI2026

Deploying MARTe2 and MDSplus for Scalable Real-Time Control Systems: A Proof-of-Concept for the SMART Tokamak

The SMART tokamak is a spherical tokamak with flexible shaping that will need a modular and high-performance real-time control system. This work presents the implementation and validation of the MARTe2 framework at the Princeton Plasma Physics Laboratory (PPPL) as a testbed for supporting SMART (University of Seville, Spain). The system integrates MARTe2, MDSplus for data archiving, and user datagram protocol (UDP)-based communication with both synthetic and physical devices. A deterministic control loop was developed using custom control algorithms, showing the system’s ability to operate under strict real-time constraints. The resulting framework is scalable, maintainable, and adaptable to experimental tokamaks like SMART.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network

As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).

Wang, Jinzhen↗
DOE OSTI2024

Directing Nanoparticle Organization in Response to Diverse Chemical Inputs

Signaling cascades are crucial for transducing stimuli in biological systems, enabling multiple stimuli to regulate a downstream target with precisely controlled timing and amplifying signals through a series of intermediary reactions. Developing a robust signaling system with such capabilities would be pivotal for programming complex behaviors in synthetic DNA-based molecular devices. However, although “software” such as nucleic acid circuits could potentially be harnessed to relay signals to DNA-based nanostructure hardware, such explorations have been limited. Here, in this study, we develop a platform for transducing a variety of stimuli via messenger-mediated reactions to regulate the release and reloading of gold nanoparticles (AuNPs) in a 3D DNA framework. In the first step, an in vitro transcription circuit is engineered to sense and amplify chemical stimuli, including arbitrary DNA sequences and proteins, producing RNA. In the second step, the RNA releases the DNA-coated AuNPs from the DNA framework via a strand displacement reaction. AuNP reloading is controlled by a separate step driven by degradation of the RNA. Our platform holds promise for applications requiring dynamic multiagent control over DNA-based devices, offering a versatile tool for advanced molecular device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning

Here, this paper describes a system for automated identification of the optimal stable cutting parameters in milling through Bayesian machine learning and closed-loop control. The closed-loop control system consists of a process monitoring architecture, an analysis framework, and a feedback mechanism. The analysis framework consists of a Bayesian machine learning algorithm that learns a stability map given test results. The learned stability map is used to select parameters for stability testing using an expected improvement in the material removal rate criterion. The test parameters are communicated to the machine controller to complete the test cut through a feedback mechanism. The test cuts were monitored using an audio signal; the stability of the test cut was determined by analyzing the frequency content of the audio signal. The test result was fed back to the Bayesian learning algorithm to complete the loop. Experimental results demonstrate that the system can identify the optimal stable parameters without information about the cutting force model or the structural dynamics. The system provides a low-cost method for optimal stable parameter identification in an industrial environment.

Chatter↗
DOE OSTI2020

An 860 MHz Wireless Surface Acoustic Wave Sensor with a Metal-Organic Framework Sensing Layer for CO2 and CH4

Wireless and passive surface acoustic wave (SAW) devices with nanoporous metal-organic framework (MOF) sensing layers are attractive gas sensors for applications in many fields such as energy industries and air pollution control. Here, we report on enhancing the sensitivity and detection limit of zeolitic imidazolate framework-8 (ZIF-8) MOF-coated SAW reflective delay line mass sensors by increasing the operating frequency for sensitive detection of carbon dioxide (CO2) and methane (CH4) at ambient conditions. In particular, we show at least four times higher sensitivity of an 860 MHz (4-μm periodicity) SAW reflective delay line coated with a 240 nm thick ZIF-8 compared to the sensitivity of a 430 MHz (8-μm periodicity) otherwise identical sensor device to the targeted gases. The detection limits of the higher frequency wireless devices for CO2 and CH4 were estimated to be 0.91 vol-% and 7.01 vol-%, respectively. The enhanced sensitivity for higher frequency devices is explained in terms of the frequency dependent acoustic wave energy confinement.

Devkota, Jagannath↗
DOE OSTI2020

Functional metal–organic frameworks as effective sensors of gases and volatile compounds

Developing efficient sensor materials with superior performance for selective, fast and sensitive detection of gases and volatile organic compounds (VOCs) is essential for human health and environmental protection, through monitoring indoor and outdoor air pollutions, managing industrial processes, controlling food quality and assisting early diagnosis of diseases. Metal–organic frameworks (MOFs) are a unique type of crystalline and porous solid material constructed from metal nodes (metal ions or clusters) and functional organic ligands. They have been investigated extensively for possible use as high performance sensors for the detection of many different gases and VOCs in recent years, due to their large surface area, tunable pore size, functionalizable sites and intriguing properties, such as electrical conductivity, magnetism, ferroelectricity, luminescence and chromism. The high porosity of MOFs allows them to interact strongly with various analytes, including gases and VOCs, thus resulting in easily measurable responses to different physicochemical parameters. Although much of the recent work on MOF-based luminescent sensors have been summarized in several excellent reviews (up to 2018), a comprehensive overview of these materials for sensing gases and VOCs based on chemiresistive, magnetic, ferroelectric, and colorimertic mechanisms is missing. In this review, we highlight the most recent progress in developing MOF sensing and switching materials with an emphasis on sensing mechanisms based on electricity, magnetism, ferroelectricity and chromism. We provide a comprehensive analysis on the MOF–analyte interactions in these processes, which play a key role in the sensing performance of the MOF-based sensors and switches. We discuss in detail possible applications of MOF-based sensing and switching materials in detecting oxygen, water vapor, toxic industrial gases (such as hydrogen sulfide, ammonia, sulfur dioxide, nitrous oxide, carbon oxides and carbon disulfide) and VOCs (such as aromatic and aliphatic hydrocarbons, ketones, alcohols, aldehydes, chlorinated hydrocarbons and N,N'-dimethylformamide). Altogether, this review serves as a timely source of information and provides insight for the future development of advanced MOF materials as next-generation gas and VOC sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Control of Excitonic Energy Transfer in RGB Quantum Dot:Polymer Composites for Tunable White Emission

Tint-controlled white light is crucial for both illumination systems and display applications. Here, in this study, we demonstrate solution-processed quantum-dot light-emitting diodes (QD-LEDs) featuring a red–green–blue (RGB) QD–poly(methyl methacrylate) (PMMA) composite emissive layer (EML) for tunable white electroluminescence (EL). In this composite EML, PMMA functions as a dispersion matrix that modulates the interdot spacing (d), thereby controlling Förster resonance energy transfer (FRET) between QDs. By adjustment of the PMMA content, the balance of R, G, and B emissions is controlled, enabling systematic and continuous tuning of the EL color from greenish to reddish white at a fixed RGB ratio and constant driving bias. Time-resolved photoluminescence measurements confirm that the variation in the exciton lifetime with the PMMA fraction is the primary factor for tuning the EL color. Notably, nearly pure white EL with CIE coordinates close to (0.33, 0.33) is achieved using a diluted PMMA matrix without significant degradation of the electrical properties. Our results demonstrate d as an independent design parameter for decoupling color tuning from RGB composition and electrical operation, providing a versatile design framework for high-quality white- or tint-controlled QD-LEDs toward advanced solid-state lighting and display technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Operator Growth in Open Quantum Systems

The spreading of quantum information in closed systems, often termed scrambling, is a hallmark of many-body quantum dynamics. In open systems, scrambling competes with noise, errors, and decoherence. Here, in this work, we provide a universal framework that describes the scrambling of quantum information in open systems: we predict that the effect of open-system dynamics is fundamentally controlled by operator size distributions and independent of the microscopic error mechanism. This framework allows us to demonstrate that open quantum systems exhibit universal classes of information dynamics that fundamentally differ from their unitary counterparts. Implications for the Loschmidt echo, nuclear magnetic resonance experiments, and the classical simulability of open quantum dynamics will be discussed.

Physics↗
DOE OSTI2022

Metal-organic framework supported single-site nickel catalysts for butene dimerization

Homotopic sites in a well-controlled environment are not only ideal systems for mechanistic studies, but also allow optimal control of catalytic transformations. Sites having only a single metal cation and sites consisting of metal oxo complexes with few nickel (Ni) cations supported on the nodes of UiO-66 metal-organic framework (Ni-UiO-66) are studied for 1-butene dimerization. Monomeric Ni sites, which bind to the Zr 6 node via two Zr-OH(µ3) linkages, are active and selective for the dimerization of 1-butene to linear and mono-branched C 8 isomers. Ni oxo complexes with few Ni cations show lower activity and promote the oligomerization of transiently formed C 8 isomers. In conclusion, Kohn-Sham density function theory calculations combined with spectroscopic measurements and kinetic analyses indicate that dimerization follows a Cossee-Arlman reaction mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2021

OpenDSS-wrapper (Distribution System Co-simulator with Distributed Energy Resource Controls)

Electric grid transformation with the proliferation of distributed energy resources (DER), such as solar photovoltaic (PV), wind, advanced energy storage technologies, and electric vehicles, and the growing use of communication technologies in both transmission and distribution systems are increasing the need to capture the interactions among these systems. Advanced modeling, control, and simulation tools that can perform co-simulation of electric power systems with other domains become indispensable to accurately model these interactions. To address this need, we have developed a codebase that integrates an electric power distribution system simulator, DER models, and DER controls. The codebase is based on OpenDSS, a distribution system simulator, and an existing open source co-simulation framework called HELICS. The contribution and uniqueness of the proposed codebase is that it tailors the generic HELICS framework specifically for distribution grid-related applications. The codebase includes an OpenDSS wrapper that controls the simulation, implements advanced DER controls, and extracts power flows, voltages, and other power system element information from the the distribution network modeled in OpenDSS. Sample HELICS federates, including a federate for OpenDSS, are provided that can communicate messages through the HELICS interface. Sample federates can be modified and additional HELICS federates can be added by the user depending on their use case requirements. SEE ALSO: https://github.com/NREL/dss-cosim

Blonsky, Michael↗
DOE OSTI2020

Goldilocks and the three glymes: How Na+ solvation controls Na–O 2 battery cycling

In this work we report a framework to understand the role of solvent-salt interactions and how they mediate the performance of sodium-air/O 2 batteries. The utilization of suitable electrolyte materials remains a point of major concern within the research community, as their stability and decomposition pathways during cycling are intimately connected with capacity and cycle life. Glyme based solvents have been widely utilized in Na–O 2 batteries, however, to date no clear correlation between solvent/electrolyte properties and battery performance has been given. Herein, we have examined the effect of glyme chain length (ethylene glycol dimethyl ether DME; diethylene glycol dimethyl ether, DEGDME; and tetraethylene glycol dimethyl ether, TEGME) on the cycling behaviour of Na–O 2 batteries and conclude that overall cell performance is highly dependent on solvent selection, salt concentration and rate of discharge/charge. We demonstrate how solvent selection helps define cell chemistry and performance by linking salt-solvent interactions to enthalpy of dissolution - and subsequently to sodium battery electrolyte properties - through the combination of both experimental and theoretical methodologies. The approaches detailed in this study could be used to predictively prepare electrolytes for Li-air batteries, other glyme-based electrochemical systems and low temperature applications.

25 ENERGY STORAGE↗
DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Framework to Demonstrate a DNP3 Interface With a CIM-Based Data Integration Platform: Preprint

The contemporary electrical grid is characterized by its complexity and abundance of data. A control-rich environment supported by information and communication technologies within an Advanced Distribution Management System (ADMS) presents a viable and cost-effective option for utility companies aiming to implement advanced real-time analytical schemes for monitoring and remotely controlling distribution feeders. Modular platform-based approaches to distribution operations require a structured framework for acquiring field device measurements, performing analytics, converting the setpoint to the correct protocol, and sending it on the appropriate communications network to the field devices. We present the development and deployment of an application service to integrate an open-source standardsbased platform with an ADMS test bed with field devices using the Distributed Network Protocol (DNP3) for data exchange. The step-by-step procedure for establishing the DNP3-Master service on an open-source distribution platform is outlined, comprehensively explaining the Master setup process. Moreover, sample use case results highlight the capabilities of the DNP3- Master service setup. Results demonstrate the scalability and configurability of the DNP3-Master service, making it adaptable for integration with other relevant applications, thus providing potential opportunities for real-world field trials and real-time assessments.

ADMS↗
DOE OSTI2023

Validation of Microgrid Algorithms using At-Scale Simulation and Emulation Real-Time (ASSERT) Framework

This paper presents a novel framework for validating microgrid algorithms using a At-Scale Simulation and Emulation Real-Time (ASSERT) setup. The proposed universal microgrid validation framework was developed at Advanced Research on Integrated Energy Systems (ARIES) platform at National Renewable Energy Laboratory in Golden, Colorado, USA. As the number of intercommunicating devices grow in power systems, along with more integration of inverter-based resources, developing and validating microgrid operation and control algorithms is becoming complex and time-consuming task. The ASSERT framework is designed to provide a comprehensive, accurate, and efficient solution for validating microgrid algorithms. It combines at-scale simulations and real-time emulation to create a virtual environment that closely mimics the behavior of a physical microgrids which are characteristic of future smart distribution systems, such as: (1) increasing variability in the physical size of new energy technologies; (2) requiring control large numbers of interconnected devices, and (3) integrating diverse technologies that have not previously worked together. The paper describes the technical details of the ASSERT framework, its validation process, and its benefits over traditional validation methods. The framework's ability to validate the performance of various algorithms is demonstrated through a case study, which shows that it can effectively identify the weaknesses of an algorithm and optimize its performance. Overall, the ASSERT framework is a promising tool that can significantly enhance the development and validation of microgrid algorithms, leading to more reliable and efficient microgrid operation.

at-scale↗