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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Materials Data on GaSiCN by Materials Project

GaN(SiC) is Stannite-like structured and crystallizes in the orthorhombic Pmn2_1 space group. The structure is three-dimensional. Ga3+ is bonded to three equivalent C4- and one N3- atom to form GaC3N tetrahedra that share corners with six equivalent GaC3N tetrahedra and corners with six equivalent SiCN3 tetrahedra. There is two shorter (1.94 Å) and one longer (1.95 Å) Ga–C bond length. The Ga–N bond length is 2.26 Å. Si4+ is bonded to one C4- and three equivalent N3- atoms to form SiCN3 tetrahedra that share corners with six equivalent GaC3N tetrahedra and corners with six equivalent SiCN3 tetrahedra. The Si–C bond length is 1.77 Å. There is one shorter (1.87 Å) and two longer (1.90 Å) Si–N bond length. C4- is bonded to three equivalent Ga3+ and one Si4+ atom to form CGa3Si tetrahedra that share corners with six equivalent CGa3Si tetrahedra and corners with six equivalent NGaSi3 tetrahedra. N3- is bonded to one Ga3+ and three equivalent Si4+ atoms to form NGaSi3 tetrahedra that share corners with six equivalent CGa3Si tetrahedra and corners with six equivalent NGaSi3 tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2022

NEWTS USGS Produced Waters Database

Data from the USGS Produced Waters Database on produced water compositions from across the US. Data has been filtered for completeness of data attributes. Data has been transposed and converted for ease of input into aqueous chemistry modeling software including OLI Systems and Geochemist's Workbench. Original data from: Blondes, M. S., Gans, K. D., Engle, M. A., Kharaka, Y. K., Reidy, M. E., Saraswathula, V., Thordsen, J. J., Rowan, E. L., & Morrissey, E. A. (2019). U.S. Geological Survey National Produced Waters Geochemical Database v2.3 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7J964W8

Geochemists Workbench↗
DOE OSTI2022

NEWTS USGS Produced Waters Case Studies

Case studies from the USGS Produced Waters Database. Includes OLI Studio and Geochemist's Workbench files. Original data from: Blondes, M. S., Gans, K. D., Engle, M. A., Kharaka, Y. K., Reidy, M. E., Saraswathula, V., Thordsen, J. J., Rowan, E. L., & Morrissey, E. A. (2019). U.S. Geological Survey National Produced Waters Geochemical Database v2.3 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7J964W8

Case Studies↗
DOE OSTI2020

Quantum Theory of Semiconductor Photo-Catalysis and Solar Water Splitting / Theory and Computation for Semiconductor Catalysis of Solar Water Splitting / Static and Dynamic Thermal Effects in Solids. Final Report

This is the final report. The finished research was published in 27 journal papers listed below. The topics fall roughly into seven categories: Photochemistry of water at the GaN ($10\bar{1}0$) surface; Quasiparticles: Band electrons and Anharmonic phonons; Quasiharmonic theory; Anharmonic lattice thermal conductivity; Water and Ice; Electron-phonon effects; and, Alloy theory.

36 MATERIALS SCIENCE↗
DOE OSTI2020

AI Denoising to Accelerate Detector Simulation

Detector simulation is critical to experimental HEP; however this simulation (commonly done through toolkits such as Geant4) is computationally intensive. Performance can be improved somewhat through technical optimization, but more is needed. Using machine learning (ML) to accelerate simulation is a promising field, however efforts to use generative adversarial networks (GANs) or optimized autoencoders have faced issues. Using convolutional neural networks (CNNs) for denoising has been successful in non-HEP applications such as image processing. This poster investigates the efficacy of using CNNs to denoise Geant4 simulations. This could increase the accuracy of simulations performed under settings designed to increase computational efficiency.

Franklin, Lena↗
DOE OSTI2020

Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics

Machine learning (ML), including deep learning (DL), has become increasingly popular in the last few years due to its continually outstanding performance. In this context, we apply machine learning techniques to "learn" the microstructure using both supervised and unsupervised DL techniques. In particular, we focus (1) on the localization problem bridging (micro)structure (localized) property using supervised DL and (2) on the microstructure reconstruction problem in latent space using unsupervised DL. The goal of supervised and semi-supervised DL is to replace crystal plasticity finite element model (CPFEM) that maps from (micro)structure (localized) property, and implicitly the (micro)structure (homogenized) property relationships, while the goal of unsupervised DL is (1) to represent high-dimensional microstructure images in a non-linear low-dimensional manifold, and (2) to discover a way to interpolate microstructures via latent space associating with latent microstructure variables. At the heart of this report is the applications of several common DL architectures, including convolutional neural networks (CNN), autoencoder (AE), and generative adversarial network (GAN), to multiple microstructure datasets, and the quest of neural architecture search for optimal DL architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning [Slides]

We present a novel machine learning CNN architecture that can learn from limited data combined appropriately with physics and statistical priors (e.g., forward models and noise models). To address the limited availability of training data we adopt a 3D patch-based approach for our models. Patch-based learning is central to several image reconstruction methods and demands fewer training data than DL approaches, as a single data volume can be broken into several millions of overlapping 3D sub-volumes or patches. This creates a very large number of training sub-volumes from a limited number of overall image volumes. A 3D Generative Adversarial Networks (GAN) is then trained to remove artifacts at the sub-volume level. The combination of a sub-volume-based approach with DL allows us to exploit the richness of the latter in extracting and representing image features, while avoiding risks associated with overfitting due to limited training data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Compact Solid-State High-Voltage Switch

Researchers at Sandia have developed a semiconductor-based high-voltage switch, with experimental results showing potential for enhanced radiation hardness, for use in multiple power conversion applications. Gallium nitride (GaN) metal-oxide semiconductor field effect transistors (MOSFETs) were modeled using commercial and Sandia CHARON simulation software to understand their performance and for future prediction of device operation in radiation environments.

42 ENGINEERING↗
DOE OSTI2021

GANpiler

Focal Area(s): Rather than augment or replace physical models with machine learning, we instead preserve the existing models and augment the underlying code with faster surrogate models created by generative adversarial networks (GAN). To leverage the performance optimization, we also propose a runtime system and user interfaces that allow prediction and tracking of accumulated error as well as dynamic, per-process decision making as to which model (if any) to use for each iteration. Science Challenge: This proposal sits at the nexus of two hard problems. First, climate models based on machine learning will be, by their nature, difficult to trust once their predictions begin diverging from the consensus. Second, compilers and hardware have been making only incremental performance gains for decades. GPGPUs have provided a welcome performance boost for codes that can take advantage of them, but there is no similar technology on the horizon to provide the next performance leap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Remote Epitaxial Growth of Compound Semiconductors through Graphene

Epitaxial growth has been widely used for high-quality growth of crystalline materials. However, epitaxial growth techniques usually require lattice match and similar thermal expansion coefficient between growth material and substrate, which blocks this growth technique from wide applicability. Recently, van der Waals (vdW) epitaxy has been demonstrated that allows to relax the limitation of epitaxial growth. vdW epitaxy on layered or two-dimensional (2D) materials is mediated by weak vdW interactions. In addition to these interactions, a substrate below 2D materials also plays a role to intervene in the epitaxial growth process, leaving room to realize a novel epitaxial growth process: remote epitaxy. Potential fluctuation—the difference between potential energy maxima and minima along the surface of the substrate—is influenced by this remote interaction; the fluctuation is predominant over the vdW force, due to the atomic thickness of 2D materials. The remote interactions can be controlled by modulation of polarities of 2D materials and epitaxy materials. Polar-material-based heterostructure, such as GaAs, InP, GaP and GaN with their substrate sandwiching 2D materials, enables the semiconductor film to be exfoliated from the underlying substrates through the 2D materials. This process is expected to open a novel avenue for the field of non-silicon electronics and photonics, where the ability to re-use graphene-coated substrates allows savings on the high cost of non-silicon substrates.

Lee, Kyusang↗
DOE OSTI2021

Gallium Nitride Superjunction Fin Field Effect Transistor (Continued Funding Report)

The goal of this project is to develop the gallium vacancy assisted diffusion (GAID) method in GaN and apply it to demonstrate 2-terminal, 3-terminal, and large area Superjunction-enhanced modules. These devices will find use in high efficiency power conversion applications with the capability of operating at higher voltages, higher frequency, and less loss than comparable stat-of- the-art devices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Component Modeling, Co-Optimization, and Trade-Space Evaluation (FY2021 Annual Progress Report)

This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap devices and ceramic capacitors. This project includes: (1) a survey of available technologies; (2) the development of design tools that consider the converter volume and performance; (3) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, and select topologies; and (4) building and testing hardware prototypes to validate models and concepts. Early instantiations of the design tools enable co-optimization of the power module and passive elements and provide some design guidance; later instantiations will enable the co-optimization of inverter and machine. Prototype testing begins with evaluation of simpler conversion topologies (i.e. the half-bridge boost converter) and progresses with fabrication of prototype inverter drives.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Incorporation of Alloy Fluctuation Effects for Accurate Modeling of GaN/InGaN/GaN Light Emitting Diodes

III-Nitride light emitting diodes (LEDs) are widely used in a range of high efficiency lighting and display applications, which have enabled significant energy savings in the last decade[1]. To improve the device performance of LEDs, it is imperative to understand the carrier transport, and how it affects forward voltage, extraction efficiency, and other LED characteristics. Despite the wide application of GaN LEDs, transport mechanisms across InGaN/GaN heterostructures in these devices are not well-explained. Fixed polarization sheet charges at InGaN/GaN interfaces lead to large interface dipole charges, which creates large potential barriers to overcome. One-dimensional models for transport across such heterostructures predict turn-on voltages that are significantly higher than that found in real devices. As a result, conventional models for transport cannot be used to design more efficient devices, or to understand the underlying transport and recombination phenomena in InGaN/GaN light emitting diodes. In this work, we show that incorporating low and high Indium compositions within quantum wells at the submicron scale can provide accurate prediction of the characteristics of GaN/InGaN light emitting diodes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

HydroGEN Seedling: Monolithically Integrated Thin Film/Silicon Tandem Photoelectrodes for High-Efficiency and Stable Photoelectrochemical Water Splitting

In this project, we propose to develop monolithically integrated Si-based tandem photoelectrodes to achieve both high solar-to-hydrogen (STH) efficiency (>15%) and long-term stability (>1,000 hours) in spontaneous water splitting systems. The proposed tandem devices consist of a Ta 3 N 5 , BCTSSe, or In 0.5 Ga 0.5 N top junction, which has a bandgap of ~1.7-2.0 eV and can split the solar spectrum with the Si bottom junction. Ta 3 N 5 , BCTSSe, and InGaN will be grown/deposited on a low-resistivity nanowire tunnel junction on Si substrates using atomic layer deposition (ALD), sputtering, and molecular beam epitaxy (MBE), respectively. Both n and p-type Si solar wafers will be utilized, which can serve as the photoanode or photocathode, respectively. We will use N-rich GaN ultrathin passivation layers to protect photoelectrodes from corrosion and oxidation.

08 HYDROGEN↗
DOE OSTI2023

2023 Critical Materials Strategy

The global effort to curb carbon emissions is accelerating demand for clean energy technologies and the materials they rely on. Demand for these materials will only continue to grow, especially as some nations aim to achieve net zero emissions by 2050. While some major materials like steel, copper, and aluminum are already powering the fossil fuel economy, others are more minor materials with potential supply risks. These risks could jeopardize the ability to reduce greenhouse gas emissions within the desirable timeframe to avoid significant climate change. In some cases, it may be necessary to take action to improve the resilience of material supply chains and mitigate supply risks. Understanding the importance of individual materials to clean energy and the supply risks associated with them is necessary to identify which materials may serve as potential roadblocks to a clean energy future. The U.S. Department of Energy (DOE) issued a series of 13 supply chain deep dive assessment reports on various energy technologies in 2022 in response to President Biden’s Executive Order on America’s Supply Chains (E.O. 14017). These reports emphasized that supply chain bottlenecks can occur at any stage of the value chain from mining and refining to component and even sub-system manufacturing. The bottlenecks are a combination of factors such as material availability, equipment availability, work force availability and quality, logistics, regulatory framework, and market conditions. These bottlenecks were worsened during the global Covid-19 pandemic. Its lingering impacts have hindered capacity expansion for material supply chains and prevented product lead-time recovery. One approach to reduce supply chain risks for the United States is to have a strong domestic manufacturing sector with a diverse set of producers. Boosting responsible domestic production would require leveraging the latest science not only in material extraction but also in developing substitutes, recycling, reuse, and remanufacturing. This report is an updated analysis of previous Critical Materials Strategy (CMS) reports published by the DOE in 2010, 2011, and 2019 based on national and global priorities, technology advancement, and technology adoption trends. Like the CMS reports, this analysis presents the results of a formal material criticality assessment to identify which materials are critical to the continued deployment of clean energy technologies globally. The analysis in this report leveraged the DOE supply chain deep dive assessments to develop the initial list of materials to evaluate. This DOE Critical Materials Assessment (CMA) is conducted independently of criticality assessments performed by other U.S. government agencies, such as that conducted by the U.S. Geological Survey (USGS). This analysis complements the USGS critical minerals determination in three aspects. First, the DOE assessment is performed from a global perspective, while the USGS analysis focusses on the importance of minerals to the U.S. economy. Second, this report focuses on the importance of materials to clean energy technologies, rather than to the economy in general. Lastly, this study is forward looking to 2035 based on clean energy deployment scenarios, whereas the USGS assessment is retrospective. Materials evaluated in this report that do not appear in the USGS Critical Minerals List include copper, uranium, electrical steel, and SiC. A draft version of this report received ~80 public comments related to supporting data and methodological improvement. Those comments have been incorporated as much as possible where appropriate. Highlights of findings from this 2023 CMA include: Rare earth materials (neodymium, praseodymium, dysprosium, and terbium) used in magnets in electric vehicle (EV) motors and wind turbine generators continue to be critical. While dysprosium (Dy) and terbium (Tb) are both heavy rare earth elements that serve the same function in magnets, the criticality of Tb is slightly lower than that for Dy in the short term due to the widespread use of Dy in high-grade magnets and Tb’s present role as a substitute. Similarly, praseodymium (Pr) is critical in the medium term but only near critical in the short term because it is more substitutable in magnets than neodymium (Nd); Materials used in batteries for EVs and stationary storage are now considered to be critical. While cobalt (Co) was found to be critical in this and previous reports, lithium (Li) becomes critical in the medium term due to its broader use in various battery chemistries and the rampant growth of the EV industry. Natural graphite is a new addition in this assessment and is also found to be critical; Platinum group metals used in hydrogen electrolyzers, such as platinum (Pr) and iridium (Ir), are critical due to an increased focus on hydrogen technologies to achieve net zero carbon emissions, while those used in catalytic converters, such as rhodium (Rh) and palladium (Pd), were screened out due to the decreased importance of catalytic converters in the medium term; Gallium (Ga) continues to be critical due to its use in light-emitting diodes (LEDs). In addition, the use of Ga has increased in magnet manufacturing and in semiconductor in forms such as gallium arsenide (GaAs) or gallium nitride (GaN); Major materials like Aluminum (Al), copper (Cu), nickel (Ni), and silicon (Si) move from noncritical in the short term to near critical in the medium term due to their importance in electrification; Electrical steel is near critical due to its use in transformers for the grid and electric motors in EVs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Novel Source Development for Focused Ion Beam Implantation and Irradiation [Slides]

We have demonstrated focused ion implantation for fabrication of single atom devices and nanofabrication. This is a viable solution for prototyping - fast and easy! There is on-going work in diamond, SiN, SiC, hBN, GaN, AlGan, etc. A new liquid metal alloy ion source development is on-going. There is a pathway towards deterministic defect centers in wide bandgap materials using FIB implantation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗
DOE OSTI2024

Extreme Efficiency 240 Vac to Load Data Center Power Delivery Topologies and Control

The research explored innovative new circuit topologies and power converter architectures for data center power delivery, from the 240 V ac voltage at the input of a server rack, down to the less than 1 V dc power supply of the latest microprocessors. The research pursued three parallel tracks to increase the power conversion efficiency and to reduce the overall size of the servers: 1) An innovative new ac-dc power converter topology which enables improved efficiency through use of a novel direct step-down topology, well suited for the popular 48V dc power distribution. 2) A new series-stacked power delivery approach for servers comprising a large number of similar loads, such as hard drives in storage servers. The approach greatly reduces the power conversion losses, and achieves efficiency well over 99%. 3) A new family of hybrid switched-capacitor power converters that enable improved efficiency and reduced size through the use of dense ceramic capacitors and new circuit topologies that leverage GaN and low-voltage Si transistor technology. Together, the accomplished research achieved the goal of reducing the power conversion losses in datacenters, thereby reducing greenhouse gas emissions, operating costs, and overall electricity consumption.

24 POWER TRANSMISSION AND DISTRIBUTION↗