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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

West Gate

West Gate is the fourth U.S. Department of Energy (DOE) Lab-Embedded Entrepreneurship Program (LEEP), which launched with its first cohort in September 2022. Selected innovators are embedded at the National Renewable Energy Laboratory (NREL) for two years to access its world-class expertise and facilities. West Gate aligns with the LEEP mission to enable the most promising cleantech entrepreneurs to develop game-changing technologies for a clean energy future. With this in mind, West Gate welcomed Cohort 1 innovators working in materials circularity, building electrification, energy storage, and energy generation. Leveraging the power of the LEEP brand and the NREL network, West Gate is a hub for innovations vital to building a clean energy economy. Working with partners at the Colorado School of Mines, LabStart, Rockies Venture Club, and countless leaders in Colorado's energy ecosystem, West Gate provides innovators with support and training as they build their businesses and make an impact in the energy community.

clean energy↗
DOE OSTI2021

Polyacrylonitrile Composites of Ag–Al–Si–O Aerogels and Xerogels as Iodine and Iodide Sorbents

Mechanically robust engineered sorbents were constructed by embedding Ag-loaded aerogels or xerogels in a polyacrylonitrile (PAN) matrix at 30 or 50 mass% loadings of gel in PAN. Here, the process for making the base gels as well as the composite materials is described herein, a technology that is very flexible for making hybrid sorbents using various inorganic or organic active sorbents. The PAN-gel composite materials were evaluated for iodine uptake in the form of gaseous iodine [I 2(g) ] at room temperature and at 150°C as well as iodide (I – ) at room temperature from simulated liquid nuclear wastes. The composites show high I 2(g) uptake and high I – uptake (= 93 mass%) in simulated liquid secondary waste spiked with 5 mg L -1 I – . These results demonstrate the utility of such a composite form for iodine removal from contaminated gas and liquid streams.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High- Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of $\mathcal{O}$(40 MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 57.1%–75.7%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 μW with an area of less than 0.2 mm 2 . The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Predictive Deep-Reinforcement-Learning-Based Connected Automated Vehicle Anticipatory Longitudinal Control in a Mixed Traffic Lane Change Condition

Maintaining safety and efficiency for mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is an arduous task due to the inherent HDVs’ stochasticity. Especially for longitudinal control, which is the basic function of vehicle automation, prevailing research primarily considers CAV’s car-following control merely the acceleration and deceleration of leading vehicles. However, this approach overlooks the potential disruptions caused by surrounding vehicles executing lane changes, which can significantly impact the control vehicle’s stability and overall safety. Hence, our study introduces a predictive deep reinforcement learning (DRL) longitudinal CAV controller. This innovative approach leverages prediction from a physics-informed neural network as well as the control capability of DRL to better anticipate and mitigate issues arising from lane-changing, enhancing the safety and efficiency of CAVs in such scenarios. Finally, validated by the numerical simulations embedded with the real-world data, the results indicate that the proposed controller significantly enhances the safety and efficiency of CAVs in situations involving lane changes by other vehicles, showcasing its potential as a valuable tool in advancing CAV technology in mixed traffic.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Automatic Fault Detection & Diagnostics: Residential Market Analysis

This report provides an overview of the market potential for automated fault detection and diagnostics (AFDD) in the residential sector, focusing on embedded AFDD in central air conditioners (CAC) and air-source heat pumps (ASHP) as well as the use of smart diagnostic tools to ensure quality installation of CAC/ASHP equipment. The contents include background on AFDD, stakeholder engagement efforts, a technology assessment of AFDD and smart diagnostic tools, residential CAC and ASHP market trends, a characterization of the CAC and ASHP installed base, a synopsis of utility provider programs, and market barriers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning

High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Evaluating Iodine Immobilization Technologies: Cermets, Polycermets, and Polyhalmets

The work in this report documents the efforts conducted to assess the feasibility of some of the ideas documented in Pacific Northwest National Laboratory invention disclosure reports (IDRs) including: 1) Iodine capture in polyacrylonitrile (PAN)-containing composite sorbents (32451-E). In this work, the composites evaluated included Ag0, Bi0, Cu0, Bi2S3, and Cu2S embedded in PAN. 2) Metal iodide removal from these sorbents through dissolution in dimethyl sulfoxide (DMSO) (32729-E). In this work, PAN dissolution was evaluated for multiple types of sorbents including Ag-Pan, Bi-PAN, Cu-PAN, Bi2S3-PAN, and Cu2S-PAN. 3) Using metal-sulfide sorbents for iodine capture (32647-E). In this work, the composites evaluated under this IDR included Ag2S, Bi2S3, and Cu2S embedded in PAN. 4) Using low-melting metals to immobilize (encapsulate) iodine-loaded and polymer-containing sorbents into polymer-ceramic-metal (called polycermet) or polymer-halide-metal (called polyhalmet) composite waste forms (32625-E). In this work, the iodine-loaded PAN composites included AgI-PAN, BiI-PAN, and CuI-PAN. 5) Ceramic-metal composite waste form synthesis of polymer-containing materials using low-melting metals like bismuth, tin, or bismuth-tin alloys (32537-E). In this work, the metals evaluated included Bi, 58Bi-42Sn eutectic. 6) Cermets for immobilizing commercial sorbents loaded with radioiodine (32806-E). In this work, AgIX (iodine-loaded silver faujasite zeolite) was evaluated in cermet form.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Ligand-Functionalized Polymer Membranes for Selective Ion Separations

Selective ion separations are central to technologies spanning water purification, resource recovery, and clean energy. Conventional polymer membranes, which rely on steric hindrance or Donnan exclusion, struggle to discriminate between chemically similar ions in high-ionic-strength environments. Ligand-functionalized membranes offer a transformative strategy by embedding molecular recognition directly into polymer matrices, enabling selective complexation and transport. Here, this Viewpoint highlights the structure–function relationships underlying ligand-mediated ion separation, emphasizing the interplay of dehydration penalties, ligand coordination, and nanoscale confinement. We discuss design principles, denticity, donor identity, rigidity, and spatial organization, alongside the permeability–selectivity trade-off, multicomponent effects, and stability challenges. Finally, we outline emerging strategies, from bioinspired ligands to computationally guided design, that chart a path toward next-generation membranes for precise and energy-efficient ion separations.

ions↗
DOE OSTI2024

Enhancing the Q-Factor of a Practical Qubit Niobium Three-Dimensional λ/4-Resonator Through Surface Treatment

Quantum computing stands as a revolutionary frontier in information technology, with the potential to solve complex problems far beyond the capacity of classical computers. At the heart of this disruptive innovation are qubits, forming the fundamental backbone of quantum computing. A leading-edge solution for constructing robust, enduring qubits involves embedding a Josephson junction within a high Q-factor, superconducting three-dimensional cavity. Further, our recent innovation lies in developing a uniquely optimized, quarter-wave resonator-based superconducting cavity, functioning at 6 GHz, specifically tailored for quantum computers. In this work, we elucidate our advancement towards elevating the Q-factor tenfold, an achievement made possible through the enhancement of machining precision, the application of rigorous postprocessing techniques—including mechanical, chemical, and surface treatments—as well as the refinement of our testing methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occurring formation water from oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this poster we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design across all periods is ensured using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗
DOE OSTI2024

Integrating the Design of Desalination Technologies into Produced Water Network Optimization

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occur-ring formation water from the oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this paper we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design is ensured across all periods using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Naik, Sakshi↗
DOE OSTI2020

Combined Plasmonic Au-nanoparticle and Conducting Metal Oxide High-temperature Optical Sensing with LSTO

<span style="color: rgba(0, 0, 0, 0.79); font-family: &quot;Source Sans Pro&quot;, source-sans-pro, museo_sans300, museo-sans, Arial, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Fiber optic sensor technology offers several advantages for harsh-environment applications. However, the development of optical gas sensing layers that are stable under harsh environmental conditions is an ongoing research challenge. In this work, electronically conducting metal oxide lanthanum-doped strontium titanate (LSTO) films embedded with gold nanoparticles are examined as a sensing layer for application in reducing gas flows at high temperature (600–800 °C). A strong localized surface plasmon resonance (LSPR) based response to hydrogen is demonstrated in the visible region of the spectrum, while a Drude free electron-based response is observed in the near-IR. Characteristics of these responses are studied both on planar glass substrates and on silica fibers. Charge transfer between the oxide film and the gold nanoparticles is explored as a possible mechanism governing the Au LSPR response and is considered in terms of the corresponding properties of the conducting metal oxide-based matrix phase. Principal component analysis is applied to the combined plasmonic and free-carrier based response over a range of temperatures and hydrogen concentrations. It is demonstrated that the combined visible and near-IR response of these films provides improved versatility for multiwavelength interrogation, as well as improved discrimination of important process parameters (concentration and temperature) through application of multivariate analysis techniques.</span>

Wuenschell, Jeffrey K.↗
DOE OSTI2020

Microbiome definition re-visited: old concepts and new challenges

The field of microbiome research has evolved rapidly over the past few decades, and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term ‘microbiome’. Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and uture outlook. We propose a definition of microbiome based on the compact, clear and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota, and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

First-Principles Predictions of Out-of-Plane Group IV and V Dimers as High-Symmetry, High-Spin Defects in Hexagonal Boron Nitride

Hexagonal boron nitride (h-BN) has been recently found to host a variety of quantum point defects, which are promising candidates as single-photon sources for solid-state quantum nanophotonic applications. Most recently, optically addressable spin qubits in h-BN have been the focus of intensive research due to their unique potential in quantum computation, communication, and sensing. However, the number of high-symmetry, high-spin defects that are desirable for developing spin qubits in h-BN is highly limited. Here, we combine density functional theory (DFT) and quantum embedding theories to show that out-of-plane X N Y i dimer defects (X, Y = C, N, P, and Si) form a new class of stable C 3v spin-triplet defects in h-BN. We find that the dimer defects have a robust 3 A 2 ground state and 3 E excited state, both of which are isolated from the h-BN bulk states. We show that 1 E and 1 A shelving states exist and they are positioned between the 3 E and 3 A 2 states for all the dimer defects considered in this study. To support future experimental identification of the X N Y i dimer defects, we provide extensive characterization of the defects in terms of their spin and optical properties. We predict that the zero-phonon line of the spin-triplet X N Y i defects lies in the visible range (800 nm to 500 nm). We compute the zero-field splitting of the dimers’ spin to range from 1.79 GHz (Si N P i o ) to 29.5 GHz (C N N i o ). Furthermore, our results broaden the scope of high-spin defect candidates that would be useful for the development of spin-based solid-state quantum technologies in two-dimensional hexagonal boron nitride.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep Koopman Neural Network for Analyzing High-Energy-Density Simulations of Electrical Wire Explosions

Megaampere-scale electrical wire experiments (EWEs) provide a platform for studying magnetohydrodynamic (MHD) instability growth in magneto-inertial fusion (MIF) devices. Even when nonlinear simulations of these experiments can digitally reproduce much of the experimentally observed instability growth, interpreting the results and understanding mode growth and evolution can be non-trivial. As a first step toward providing better interpretation of these simulation features, this work investigates the use of a deep neural network that uses Koopman operator theory to analyze the dynamics of pulsed-power-driven explosions of EWEs. This deep neural network is trained on 1-D resistive MHD simulations of EWEs. This neural network learns to transform the nonlinear data into a lower-dimensional representation where the time dynamics are linear. Layers of this neural network are shown to learn features of the simulations, including the locations of shock waves and different physical regimes of the simulation. Using the learned features, the network can compress a time state of the simulation consisting of 5120 data point into a 36-parameter lower-dimensional latent space embedding. Furthermore, these embeddings are shown to be clustered in the latent space by initial radius and time state.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Resilient Hierarchical Networked Control Systems: Secure Controls for Critical Locations and at Edge

Integration of information and communication technology (ICT) offers new opportunities in improving the management and operation of critical infrastructures such as power systems as it allows connection of different sensors and control components via a communication network, leading to the so-called networked control systems (NCS). However, the use of open and pervasive ICT such as the Internet or wireless communication technologies comes at a price of making NCS vulnerable to cyber intrusions/attacks which may cause physical damage. Here, this chapter presents control algorithms to ensure resilient and safe operation of NCS under unknown cyberattacks. Specifically, a variant of dynamic watermarking strategies is presented by embedding encoding/decoding components of chaotic signals into the NCS for secure control for critical locations where the measurement/control signals are transmitted to/from the control center via a communication network. In addition, resilient cooperative control algorithms are discussed to ensure safe operation at edge of the NCS which consists of a large number of distributed controllable devices. Several numerical examples are provided to illustrate the proposed control strategies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗