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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Compact Monolithic Electro-Optic Package for Quantum Microwave-to-Optical Transduction

We present a novel quantum transduction package that integrates a macroscopic electro-optic crystal into a compact, monolithic assembly designed for superconducting quantum processors. The device provides an efficient and scalable interface between microwave and optical domains while preserving cryogenic compatibility. We performed full-wave and quantum dynamical simulations to assess the performance of the hybrid system, focusing on the interaction between a transmon-based microwave cavity and the crystal’s and cavity low-frequency microwave mode. The transmon operates both as an ancilla for the QPU and as a nonlinear element enabling four-wave mixing between cavity and electro-optic crystal microwave fields. Our results indicate that this architecture can coherently mediate quantum information transfer from the cavity to the electro-optic mode, offering a promising platform for on-chip quantum transduction within superconducting quantum networks.

Reineri, Alessandro [Fermilab] (ORCID:000000016175↗
DOE OSTI2021

Ontology-Enriched Specifications Enabling Findable, Accessible, Interoperable, and Reusable Marine Metagenomic Datasets in Cyberinfrastructure Systems

Marine microbial ecology requires the systematic comparison of biogeochemical and sequence data to analyze environmental influences on the distribution and variability of microbial communities. With ever-increasing quantities of metagenomic data, there is a growing need to make datasets Findable, Accessible, Interoperable, and Reusable (FAIR) across diverse ecosystems. FAIR data is essential to developing analytical frameworks that integrate microbiological, genomic, ecological, oceanographic, and computational methods. Although community standards defining the minimal metadata required to accompany sequence data exist, they haven’t been consistently used across projects, precluding interoperability. Moreover, these data are not machine-actionable or discoverable by cyberinfrastructure systems. By making ‘omic and physicochemical datasets FAIR to machine systems, we can enable sequence data discovery and reuse based on machine-readable descriptions of environments or physicochemical gradients. In this work, we developed a novel technical specification for dataset encapsulation for the FAIR reuse of marine metagenomic and physicochemical datasets within cyberinfrastructure systems. This includes using Frictionless Data Packages enriched with terminology from environmental and life-science ontologies to annotate measured variables, their units, and the measurement devices used. This approach was implemented in Planet Microbe, a cyberinfrastructure platform and marine metagenomic web-portal. Here, we discuss the data properties built into the specification to make global ocean datasets FAIR within the Planet Microbe portal. We additionally discuss the selection of, and contributions to marine-science ontologies used within the specification. Finally, we use the system to discover data by which to answer various biological questions about environments, physicochemical gradients, and microbial communities in meta-analyses. This work represents a future direction in marine metagenomic research by proposing a specification for FAIR dataset encapsulation that, if adopted within cyberinfrastructure systems, would automate the discovery, exchange, and re-use of data needed to answer broader reaching questions than originally intended.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Graphite-Embedded High-Performance Insulated Metal Substrate for Wide-Bandgap Power Modules

Emerging wide-bandgap (WBG) semiconductor devices such as silicon carbide (SiC) metal–oxide semiconductor field-effect transistors (MOSFETs) and gallium nitride high-electron-mobility transistors can handle high power in reduced semiconductor areas better than conventional Si-based devices owing to superior material properties. With increased power loss density in a WBG-based converter and reduced die size in power modules, thermal management of power devices must be optimized for high performance. Here, we present a graphite-embedded insulated metal substrate (thermally-annealed-pyrolytic-graphite-embedded insulated metal substrate—IMSwTPG) designed for WBG power modules. Theoretical thermal performance analysis of graphite-embedded metal cores is presented, with design details for IMSwTPG with embedded graphite to replace a direct-bonded copper (DBC) substrate. The proposed IMSwTPG is compared with an aluminum nitride-based DBC substrate using finite-element thermal analysis for steady-state and transient thermal performance. The solutions’ thermal performances are compared under different coolant temperature and thermal loading conditions, and the proposed substrate's electrical performance is validated with static and dynamic characterization. Using graphite-embedded substrates, junction-to-case thermal resistance of SiC MOSFETs can be reduced up to 17%, and device current density can be increased by 10%, regardless of the thermal management strategy used to cool the substrate. Reduced transient thermal impedance of up to 40% of dies owing to increased heat capacity is validated in transient thermal simulations and experiments. The half-bridge power module's electrical performance is evaluated for on -state resistance, switching performance, and switching loss at three junction temperature conditions. The proposed substrate solution has minimal impact on conduction and switching performance of SiC MOSFETs.

42 ENGINEERING↗
DOE OSTI2024

Simulating Superconducting Devices in Novel Materials Using the G4CMP Simulation Toolkit

Understanding the kinematics of phonons and charge propagation in superconducting devices is crucial for conducting low-threshold dark matter searches and minimizing correlated errors in superconducting qubits. For nearly a decade, the Geant4 Condensed Matter Physics (G4CMP) package, originally developed for the Cryogenic Dark Matter Search (CDMS) experiment, has been limited to simulating charge and phonon transport in silicon and germanium materials. In this work, we have expanded the capabilities of G4CMP to include novel substrate materials such as sapphire (Al2O3), Gallium Arsenide (GaAs), Lithium Fluoride (LiF), Calcium Tungstate (CaWO4), and Calcium Fluoride (CaF2). We demonstrate the use of this toolkit in generating phonon transport properties for these materials and compare the results with experimentally determined values where available. Additionally, we explore the performance of superconducting devices utilizing these materials.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Sensos Smart Label Performance Summary as Observed by Oak Ridge National Laboratory

The Oak Ridge National Laboratory (ORNL) team performed an evaluation of the Sensos Smart Label Gen 2.0, as shown in Figure 1, for package tracking. A long-distance round-trip shipment between Oak Ridge, Tennessee, and Seattle, Washington, was completed to assess the device’s performance in location tracking, environment sensing capabilities, alerting features, threshold options, battery life, and real-time and historical data retrieval from the “Sync” data dashboard provided by Sensos. The evaluation was conducted to gain a general understanding of the capabilities of the device. Furthermore, due to time and resource constraints, ORNL did not conduct exhaustive testing to confirm reliability, availability, or effectiveness of alerting and tracking features. On equipment arrangement, Sensos (sensos.ai) graciously agreed to provide a Sensos Smart Label Gen 2.0 device to ORNL, at no cost, for testing and evaluation purposes. ORNL conducted assessments along with other commercial off-the-shelf (COTS) tracking devices. As a courtesy, ORNL will provide Sensos with this report summarizing the observations and findings specific to the Sensos label based on the tests performed.

42 ENGINEERING↗
DOE OSTI2020

Quantum prediction of ultra-low thermal conductivity in lithium intercalation materials

Lithium-intercalated layered transition-metal oxides, Li x TMO 2, brought about a paradigm change in rechargeable batteries in recent decades and show promise for use in memristors, a type of device for future neural computing and on-chip storage. Thermal transport properties, although being a crucial element in limiting the charging/discharging rate, package density, energy efficiency, and safety of batteries as well as the controllability and energy consumption of memristors, are poorly managed or even understood yet. Here, for the first time, we employ quantum calculations including high-order lattice anharmonicity and find that the thermal conductivity κ of Li x TMO 2 materials is significantly lower than hitherto believed. More specifically, the theoretical upper limit of κ of LiCoO 2 is 6 W/m-K, 2–6 times lower than the prior theoretical predictions. Delithiation further reduces κ by 40–70% for LiCoO 2 and LiNbO 2 . Grain boundaries, strain, and porosity are yet additional causes of thermal-conductivity reduction, while Li-ion diffusion and electrical transport are found to have only a minor effect on phonon thermal transport. The results elucidate several long-standing issues regarding the thermal transport in lithium-intercalated materials and provide guidance toward designing high-energy-density batteries and controllable memristors.

25 ENERGY STORAGE↗
DOE OSTI2024

NEML2: A High Performance Library for Constitutive Modeling

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Testing Instrument Extremes in the TREAT Facility

TREAT is a transient power-shaping reactor whose primary mission is to enhance safety performance by testing nuclear fuels and materials under thermodynamic and neutronic conditions ranging from off-normal to extreme. Transient testing of nuclear fuels is analogous to car crash testing, in that the dramatic changes commonly seen when comparing the test specimens' initial and final state points necessitate in-situ measuring in order to interpret and understand the evolution of the experiment. TREAT’s experiment design strategy utilizes a highly reconfigurable and accessible reactor core that affords flexibility when installing experiment devices, thanks to the lack of a primary coolant boundary and the inclusion of multiple access points in the reactor bioshield. Within the experiment devices, the test specimens (which are integrated with their test environments), desired instrumentation diagnostics, and safety considerations are all contained in a single engineered package. The experiments encompass various types of environments (e.g., water, sodium, and gas at temperature and pressure), as well as a range of control options (e.g., static or flowing coolants). The experimental conditions are uniquely representative of advanced reactors, providing challenges to sensor performance but also affording a unique opportunity for development and qualification of sensors suited to these environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems: Preprint

We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). While many frameworks exist for training multi-agent (MA) RL policies, none exist to rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning multi-agent RL policies using both OpenAI's MADDPG and RLLib's PPO algorithms where, in both cases, at least some subset of agents incorporate elements of the power flow solution at each time step as part of their reward (negative cost) structures.

MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Implementation of a portable diagnostic system for Thomson scattering measurements on an electrothermal arc source

To fulfill the increasing needs of diagnostic support for researchers in plasma technology, a portable diagnostic package (PDP) equipped for both laser Thomson scattering (TS) and optical emission spectroscopy has been designed and constructed at Oak Ridge National Laboratory (ORNL), aiming to measure the temperature and number density of electrons and temperatures of ions in plasma devices. The PDP has been initially implemented on a high density and low temperature electrothermal arc source (ET-arc) at ORNL to test its TS capability. TS from the plasmas in the ET-arc has been obtained using the PDP. The electron temperature and number density were determined from TS spectra. These results were then compared to measurements from previous studies on the ET-arc. The TS diagnostic measured 0.8 ± 0.1, 1.3 ± 0.2, and 0.7 ± 0.1 eV and (4.4 ± 0.5) × 1021, (5.9 ± 0.7) × 1021, and (4.3 ± 0.5) x 1021 m-3, respectively, from three lines of sight that transect the plasma column.

He, Z. (ORCID:0000000183159882)↗
DOE OSTI2023

Metal oxide barrier layers for terrestrial and space perovskite photovoltaics

Perovskite photovoltaics are attractive for both terrestrial and space applications. Although terrestrial conditions require durability against stressors such as moisture and partial shading, space poses different challenges: radiation, atomic oxygen, vacuum and high-temperature operation. Here we demonstrate a silicon oxide layer that hardens perovskite photovoltaics to critical space stressors. A 1-um-thick silicon oxide layer evaporated atop the device contacts blocks 0.05 MeV protons at fluences of 10 15 cm -2 without a loss in power conversion efficiency, which results in a device lifetime increase in low Earth orbit by x20 and in highly elliptical orbit by x30. Silicon-oxide-protected Cs 0.05 (MA 0.17 FA 0.83 ) 0.95 Pb(I 0.83 Br 0.17 ) 3 ) (MA, methylammonium; FA, formamidinium cation) and CsPbI 2 Br cells survive submergence in water and N,N-dimethylformamide. Furthermore, moisture tolerance of Sn-Pb and CsPbI 2 Br devices is boosted. Devices are also found to retain power conversion efficiencies on exposure to alpha irradiation and atomic oxygen. So, this barrier technology is a step towards lightweight packaging designs for both space and terrestrial applications.

14 SOLAR ENERGY↗
DOE OSTI2024

Giant energy storage and power density negative capacitance superlattices

Dielectric electrostatic capacitors, because of their ultrafast charge-discharge, are desirable for high-power energy storage applications. Along with ultrafast operation, on-chip integration can enable miniaturized energy storage devices for emerging autonomous microelectronics and microsystems. Moreover, state-of-the-art miniaturized electrochemical energy storage systems-microsupercapacitors and microbatteries-currently face safety, packaging, materials and microfabrication challenges preventing on-chip technological readiness, leaving an opportunity for electrostatic microcapacitors. Here, in this study, we report record-high electrostatic energy storage density (ESD) and power density, to our knowledge, in HfO 2 -ZrO 2 -based thin film microcapacitors integrated into silicon, through a three-pronged approach. First, to increase intrinsic energy storage, atomic-layer-deposited antiferroelectric HfO 2 -ZrO 2 films are engineered near a field-driven ferroelectric phase transition to exhibit amplified charge storage by the negative capacitance effect, which enhances volumetric ESD beyond the best-known back-end-of-the-line-compatible dielectrics (115 J cm -3 ). Second, to increase total energy storage, antiferroelectric superlattice engineering scales the energy storage performance beyond the conventional thickness limitations of HfO 2 -ZrO 2 -based (anti)ferroelectricity (100-nm regime). Third, to increase the storage per footprint, the superlattices are conformally integrated into three-dimensional capacitors, which boosts the areal ESD nine times and the areal power density 170 times that of the best-known electrostatic capacitors: 80 mJ cm -2 and 300 kW cm -2 , respectively. This simultaneous demonstration of ultrahigh energy density and power density overcomes the traditional capacity-speed trade-off across the electrostatic-electrochemical energy storage hierarchy. Furthermore, the integration of ultrahigh-density and ultrafast-charging thin films within a back-end-of-the-line-compatible process enables monolithic integration of on-chip microcapacitors, which can unlock substantial energy storage and power delivery performance for electronic microsystems.

25 ENERGY STORAGE↗
DOE OSTI2024

Error field detection and correction studies towards ITER operation

In magnetic fusion devices, error field (EF) sources, spurious magnetic field perturbations, need to be identified and corrected for safe and stable (disruption-free) tokamak operation. Within Work Package Tokamak Exploitation RT04, a series of studies have been carried out to test the portability of the novel non-disruptive method, designed and tested in DIII-D (Paz-Soldan et al 2022 Nucl. Fusion62 126007), and to perform an assessment of model-based EF control strategies towards their applicability in ITER. In this paper, the lessons learned, the physical mechanism behind the magnetic island healing, which relies on enhanced viscous torque that acts against the static electro-magnetic torque, and the main control achievements are reported, together with the first design of the asynchronous EF correction current/density controller for ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

EmSense: A High-Resolution Emulated Sensor for Experiments with the Smart Grid and Distributed Ledger Technology

This work involves the development of a device - EmSense (“Emulated Sensor”) - that emulates a high-resolution sensor for a power grid. The device collects raw current and voltage sensor data which derive from ORNL's signature library. This library is a dataset that ORNL curates from many different sources that include power systems from various utilities. The EmSense packages the data from the library in the form of IEC 61850 Sampled Value (SV) packets and then broadcasts these SV packets on the network. In another mode, EmSense can generate artificial sinusoidal data that appears as waveforms for voltage and current signals. EmSense has an internal algorithm for determining the period of a signal based on the data so that the period can be specified as a variable in the IEC 61850 packets. The purpose of EmSense is to allow for experimentation with the Dark Net Infrastructure where a variety of power line sensors must be represented along with their typical communication traffic. The EmSense device was developed in coordination with the software for receiving and processing the packets in the Distributed Ledger Technology (DLT) framework of the DarkNet Project. This receiving software must have a methodology for dealing with information of high velocity, variety, and volume. Experimenting with EmSense facilitates the development of such software. The results showed that the DLT framework and the trust-anchoring approach managed to process a large flow of traffic even with up to six instances of EmSense device broadcasting data. This was achieved without overfilling packet queues in the memory of the actual hardware of the DLT devices or causing the Central Processing Unit (CPU) of the hardware to be overwhelmed. The DLTs were also able to store the data in a compact and useful form for later analysis and archival purposes.

Werth, Aaron↗
DOE OSTI2024

Modeling athermal phonons in novel materials using the G4CMP simulation toolkit

Understanding phonon and charge propagation in superconducting devices plays an important role in both performing low-threshold dark matter searches and limiting correlated errors in superconducting qubits. The Geant4 Condensed Matter Physics (G4CMP) package, originally developed for the Cryogenic Dark Matter Search (CDMS) experiment, models charge and phonon transport within silicon and germanium detectors and has been validated by experimental measurements of phonon caustics, mean charge-carrier drift velocities, and heat pulse propagation times. Here, in this work, we present a concise framework for expanding the capabilities for phonon transport to a number of other novel substrate materials of interest to the dark matter and quantum computing communities, including sapphire (Al 2 O 3 ), gallium arsenide (GaAs), lithium fluoride (LiF), calcium tungstate (CaWO 4 ), and calcium fluoride (CaF 2 ). We demonstrate the use of this framework in generating phonon transport properties of these materials and compare these properties with experimentally-determined values where available.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Toward ultrafast, ultra-stable imaging arrays: Superlattice doping to enhance the performance of backside-illuminated 3D-hybridized silicon photodetectors

In this paper, the authors report the latest results on their development of superlattice-doped, thinned, backside-illuminated (BSI), 3D-integrated photodiode detectors—a step toward their ultimate goal of demonstrating ultrafast, ultrastable CMOS imaging arrays. As with most silicon-based photodetectors, backside-illumination and backside surface passivation are keys to achieve the highest performance capability. The two-dimensional (2D) doping technique developed at the Jet Propulsion Laboratory (JPL) has proved to result in a highly efficient, highly stable detector response when combined with a variety of detectors. Here, JPL's 2D-doping has been combined with Sandia's BSI photodetectors hybridized with custom fanout wafer via copper Direct Bond Interconnect (DBI®), a technology that is rapidly becoming industry standard for BSI CMOS imaging arrays. The prototype detectors were packaged and evaluated with respect to their response to low energy electrons. The authors find that the responsivity of 2D-doped BSI detectors is higher than devices prepared using other surface passivation techniques (i.e., ion implantation). The success of the work described herein verifies that the 2D-doping processes previously developed for Sandia's frontside-illuminated photodetectors are generally applicable to BSI detectors and demonstrates for the first time that JPL's 2D-doping process is compatible with the Cu-DBI® technology.

Jewell, April D.↗
DOE OSTI2021

Reinforcement Learning for Distribution Grid Optimization (PyCIGAR) v0.1

PyCIGAR is a python software package that merges off-the-shelf reinforcement learning libraries (RLLib and Ray) with electric power distribution system simulation tools (OpenDSS and a custom power flow solver built by LBL). PyCIGAR enables the training of neural networks to optimize the behavior of different components in the electric distribution grid, such as control systems in photovoltaic rooftop solar inverters and electric battery storage systems. The software package has been used to train neural networks to update settings in photovoltaic rooftop solar inverter control systems to mitigate cyber attacks on other solar photovoltaic rooftop devices.

Arnold, Daniel↗