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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

In Search of Energy Landscape for Network Glasses

Quick scanning extended x-ray absorption fine-structure spectroscopy is used to obtain in situ structural information on the real-time response of network glasses at the nanoscale level of atomic organization to the temperature ramp through the glass transition range. The results testify nonlinear, real-time temperature response indicative of nanoscale dynamic heterogeneity in disordered systems with intermediate fragility, related to the intermetabasin transitions within potential energy/enthalpy landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2012

UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem

The demands of cutting-edge science are driving the need for larger and faster computing resources. With the rapidly growing scale of computing systems and the prospect of technologically disruptive architectures to meet these needs, scientists face the challenge of effectively using complex computational resources to advance scientific discovery. Multi-disciplinary collaborating networks of researchers with diverse scientific backgrounds are needed to address these complex challenges. The UNEDF SciDAC collaboration of nuclear theorists, applied mathematicians, and computer scientists is developing a comprehensive description of nuclei and their reactions that delivers maximum predictive power with quantified uncertainties. This paper describes UNEDF and identifies attributes that classify it as a successful computational collaboration. Finally, we illustrate significant milestones accomplished by UNEDF through integrative solutions using the most reliable theoretical approaches, most advanced algorithms, and leadership-class computational resources.

Nam, H.↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2024

Focus on monitoring and control of complex supply systems

The ongoing rapid transformation of our energy supply challenges the operation and stability of electric power grids and other supply networks. This focus issue comprises new ideas and concepts in the monitoring and control of complex networks to address these challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Hybrid Ultra‐Microporous Materials for Selective Xenon Adsorption and Separation

Abstract The demand for Xe/Kr separation continues to grow due to the industrial significance of high‐purity Xe gas. Current separation processes rely on energy intensive cryogenic distillation. Therefore, less energy intensive alternatives, such as physisorptive separation, using porous materials, are required. Herein we show that an underexplored class of porous materials called hybrid ultra‐microporous materials (HUMs) affords new benchmark selectivity for Xe separation from Xe/Kr mixtures. The isostructural materials, CROFOUR‐1‐Ni and CROFOUR‐2‐Ni, are coordination networks that have coordinatively saturated metal centers and two distinct types of micropores, one of which is lined by CrO 4 2− (CROFOUR) anions and the other is decorated by the functionalized organic linker. These nets offer unprecedented selectivity towards Xe. Modelling indicates that the selectivity of these nets is tailored by synergy between the pore size and the strong electrostatics afforded by the CrO 4 2− anions.

Mohamed, Mona H.↗
DOE OSTI2022

Ab initio molecular dynamics (AIMD) simulations of NaCl, UCl 3 molten salts

Ab initio molecular dynamics (AIMD) simulations are used to calculate select thermophysical and thermodynamic properties of NaCl, UCl and NaCl-UCl 3 molten salts. Following established approaches, the AIMD simulations include a model for Van der Waals interactions. The Langreth & Lundqvist (vDW-DF2), DFT-D3, and density-dependent energy correction (DFT-dDsC) dispersion models are first tested for molten NaCl in order to assess their accuracy for density and heat capacity predictions across a range of temperatures. Based on the NaCl results, the vdW-DF2 and DFT-dDsC methods are extended to the UCl 3 system and compared to available experimental data. Next, mixtures of NaCl-UCl 3 are investigated and analyzed with respect to the deviation from ideal solution behavior. For the DFT-dDsC simulations, density deviates by up to 2% from an ideal mixture, with the maximum occurring close to the eutectic composition. The mixing energy also deviates from an ideal solution and exhibits a minimum of about -0.074 to eV per formula unit, again close to the eutectic composition. Finally, the compressibility and species diffusivity of the pure and mixed salt systems are calculated. The diffusivities are slightly reduced in the mixed compared to the pure systems and the compressibilities loosely follow a linear correlation as a function of the UCl 3 composition. The trends observed for mixing properties are rationalized by correlating them to the coordination chemistry, which emphasizes the importance of maintaining the extended network formed by U and Cl ions as the NaCl concentration increases. The concentration at which breakdown of the extended network occurs, roughly coincides with the minimum of both the mixing energy and the deviation from ideal solution behavior for density.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Energy shift of collective electron excitations in highly corrugated graphitic nanostructures: Experimental and theoretical investigation

Effect of corrugation of hexagonal carbon network on the collective electron excitations has been studied using optical absorption and X-ray photoelectron spectroscopy in conjunction with density functional theory calculations. Onion-like carbon (OLC) was taken as a material, where graphitic mantle enveloping agglomerates of multi-shell fullerenes is strongly curved. Experiments showed that positions of π and π + σ plasmon modes as well as π → π* absorption peak are substantially redshifted for OLC as compared with those of highly ordered pyrolytic graphite and thermally exfoliated graphite consisted of planar sheets. This effect was reproduced in behavior of dielectric functions of rippled graphite models calculated within the random phase approximation. We conclude that the energy of electron excitations in graphitic materials could be precisely tuned by a simple bending of hexagonal network without change of topology. Moreover, our investigation suggests that in such materials optical exciton can transfer energy to plasmon non-radiatively.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Sustainable Energy Solutions Task 4.1 Intelligent Manufacturing of Hybrid Carbon-Glass Fiber-Reinforced Composite Wind Turbine Blades

In this subtask, the manufacturability of hybrid carbon-glass fiber-reinforced composite wind turbine blades using Vacuum-Assisted Resin Transfer Molding (VARTM) was investigated. The objective of this investigation was to study the VARTM process and its parameters to manufacture cost-effective wind turbine blades with no defects (mainly eliminate dry spots and reduce manufacturing time). A 2.5-dimensional model and a 3-dimensional model were developed to simulate mold filling and part curing under different conditions. These conditions included isothermal and non-isothermal filling, curing of the part during and after filling, and placement of injection gates at different locations. Results from this investigation reveal that the process can be simulated and also that manufacturing parameters can be optimized to eliminate dry spot formation and reduce the manufacturing time. Using computer-based models is a cost-effective way to simulate manufacturing of wind turbine blades. The approach taken herein allows the design of the wind blade manufacturing processes without physically running trial-and-error experiments that are expensive and time-consuming; especially for larger blades needed for more demanding environmental conditions. This will benefit the wind energy industry by reducing initial design and manufacturing costs which can later be passed down to consumers and consequently make the wind energy industry more competitive.

17 WIND ENERGY↗
DOE OSTI2023

Small-Molecule Catalysis by Transition-Metal Isocyanide Monomers, Clusters and Materials

DOE Catalysis Science Award DE-SC0018243 focused on the development and mechanistic understanding of small-molecule-activation and catalysis by monomeric and multinuclear transition-metal complexes supported by encumbering m-terphenyl isocyanide ligands. An additional goal of this award was the development of solid-state network materials featuring low-valent metal centers and ditopic m-terphenyl isocyanide ligands into functional platforms for catalytic chemical transformations. The emphasis on m-terphenyl isocyanides stems from a sustained effort by our research team that has shown these ancillary ligands to be particularly adept at stabilizing electronically or coordinately unsaturated metal centers. These metal centers in turn have been shown to effect catalytic and/or stoichiometric chemical transformations of substrates relevant to high-priority energy science goals. In addition, the stabilizing environment provided by these encumbering ligands has in several instances aided the detailed mechanistic elucidation of substrate activation process and/or have enabled important electronic structure insights to be uncovered.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A three-dimensional self-assembled SnS 2 -nano-dots@graphene hybrid aerogel as an efficient polysulfide reservoir for high-performance lithium–sulfur batteries

Reliable sulfur cathodes hold the key to realizing high-performance lithium–sulfur (Li–S) batteries, yet the electrochemical inefficiency and instability arising from the poor conductivity of sulfur and lithium sulfide together with polysulfide diffusion present challenges. We present here a new three-dimensional graphene aerogel embedded with in situ grown SnS 2 nano-dots (SnS 2 -ND@G) as an efficient sulfur host. First, benefiting from a highly conductive, hierarchically porous, and mechanically self-supported architecture, the SnS 2 -ND@G aerogel enables the cathode to hold high sulfur content (75 wt%) and loading (up to 10 mg cm –2 ). Both values exceed most of the reported metal-compound-related cathode work (<60 wt% sulfur content and <3 mg cm –2 sulfur loading) in the literature. Second, this work takes advantage of a facile one-pot self-assembly fabrication, effectively guaranteeing a homogeneous deposition of SnS 2 nano-dots in the graphene aerogel with a small amount of SnS 2 (16 wt%). It greatly overcomes the shortcomings of physical incorporation methods to make metal-compound/carbon substrates reported in previous studies. More importantly, by rationally combining the physical entrapment from graphene and chemical adsorptivity from SnS 2 nano-dots towards polysulfides, the SnS 2 -ND@G aerogel demonstrates remarkably improved polysulfide-trapping capability and electrochemical stability. As a result, a high peak capacity of 1234 mA h g –1 , a high reversible capacity of 1016 mA h g –1 after 300 cycles, exceptional rate capability (C/10 – 3C rates), and impressive areal capacity (up to 11 mA h cm –2 ) are achieved. Furthermore, this work provides a viable path to integrate a conductive graphene network and nano-sized SnS 2 as a promising cathode substrate for developing advanced Li–S batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Advanced Solar and Load Forecasting Incorporating HD Sky Imaging (Phase III)

Due to rapidly changing sky conditions, the available solar irradiance for energy generation is subject to wide swings in amplitude. As the market penetration of solar energy continues to increase rapidly, these variations in solar power generation are beginning to have an impact on grid stability and increased wear on power switches. Solar power forecasting plays a critical role in operations for Independent System Operators (ISOs) and utilities. Accurate forecasts help maintain grid reliability, optimize generation from renewables, and reduce operating costs. Of particular interest to the ISOs and utilities are sudden changes in solar irradiance, termed “ramp events,” due to the movement of clouds. One significant impact of ramp events on the grid is additional ancillary service requirements necessary to manage such variability. Ramp events can also cause voltage fluctuations in the distribution grids and trigger actions of automated line equipment (e.g., tap changers), leading to additional maintenance costs. In high penetration solar regions, forecasts must be made for both transmission-connected and distribution-connected resources – either behind the meter or in front of it. Particularly for distributed solar energy resources, forecasting can be a challenge due to the lack of visibility of the energy resource. Brookhaven National Laboratory (BNL) has been working towards nowcasting Global Horizontal Irradiance (GHI) using low-cost technologies for several years now. The Solar NowCasting technology being developed by BNL is a 0 – 30 min solar “nowcasting” technology applicable to a scale covering both large generating facilities and residential, distributed solar installations that relies on a network of ground-based high-definition (HD) cameras and surface pyranometers. GHI forecasts are being produced for both regions using 8 ground-based HD cameras and at least 2 surface pyranometers. When stitched together, the camera images can be used to forecast the impact of clouds on available solar irradiance over a domain of ~50 km 2 . Phase I of the project was the engineering scale up conceptual design stage. Phase II was the initial field test and demonstration, in which the technology was scaled up by a factor of 20 times and successfully demonstrated in eastern Long Island. In Phase III, an additional forecasting network was added in upstate NY and both networks are currently being operated until at least a full year of data is gathered by the Albany network to allow for the collection of sufficient data to evaluate performance against the persistence and smart persistence models using the Solar Arbiter.

14 SOLAR ENERGY↗
DOE OSTI2021

Optimization of selenium in CdZnTeSe quaternary compound for radiation detector applications

X- and gamma-ray detectors are increasingly becoming essential tool for science and technology in various fields include homeland security, nonproliferation, nuclear security, medical imaging, astrophysics, and high energy physics. Cd 1-x Zn x Te 1-y Se y (CZTS) is emerging as a next-generation compound semiconductor for such applications. CZTS was found to possesses a very low concentration of Te inclusions and free from sub-grain boundary networks. Being a quaternary compound with varying alloy composition, optimization of the composition was performed to determine the minimum amount of selenium required to produce CZTS with reduced defects. The optimized composition was found to be x=0.10 and y=0.02, i.e., Cd 0.9 Zn0.1Te 0.98 Se 0.02 , for excellent material properties as a radiation detector. The resulting material was free from sub-grain boundary networks and with a highly reduced concentration of Te inclusions. The bulk dark resistivity obtained was in the range of 1-3x1010 ohm-cm with the highest achieved mobility-lifetime product of ~6.6x10-3 cm2/V for the optimized CZTS composition. Impurity analyses were performed by the Glow Discharge Mass Spectroscopy (GDMS) technique, and the results showed relatively high impurity concentrations compared to commercial detector-grade CdZnTe. Thus, CZTS has room for further improvement with additional purification of the starting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2024

Static and Dynamic Thermomechanical Properties of Phase-Separated Epoxy Networks with Tuned Microstructures

Here, polymerization-induced phase separation is a useful method for the construction of heterogeneous epoxy networks with properties exceeding their homogeneous counterparts. In this work, we examine the static and dynamic thermomechanical properties of phase-separated epoxy networks salient to their application as encapsulants. Three heterogeneous epoxy-amine networks with nano-, meso-, and macro-phase-separated morphologies comprised of hard and soft domains are compared to a rigid, unstructured network. The glass transition profiles of the heterogeneous networks are complex, spanning many decades in the frequency domain. The nanophase-separated morphology leads to higher coefficient of thermal expansion, yet surprisingly is characterized by reduced residual stress. Under both quasi-static and dynamic compression (strain rates of order 10 –3 and 10 3 s –1 , respectively), the nanophase-separated network also exhibits higher modulus and strength. In split-Hopkinson bar experiments, the energy dissipation characteristics of the epoxy networks were nearly identical. Curiously, however, the Hugoniot response of the macro-phase-separated network determined by ballistic shockwave analysis indicates a remarkable ability of this material to mitigate shockwave propagation in comparison to many homogeneous and heterogeneous polymer materials. Collectively, this work reveals several previously unreported phenomena with respect to structure–property relationships in phase-separated epoxy networks, illustrating the potential value of systematically tuned microstructures for optimization of application-specific physical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Nuclear Forensics International Technical Working Group (ITWG): a collaboration of scientists, law enforcement officials, and regulators working to combat nuclear terrorism and proliferation

Founded in 1996 upon the initiative of the “Group of 8” governments (G8), the Nuclear Forensics International Technical Working Group (ITWG) is an ad hoc organization of official Nuclear Forensics practitioners (scientists, law enforcement, and regulators) that can be called upon to provide technical assistance to the global community in the event of a seizure of nuclear or radiological materials. The ITWG is supported by and is affiliated with nearly 40 countries and international partner organizations including the International Atomic Energy Agency (IAEA), EURATOM, INTERPOL, EUROPOL, and the United Nations Interregional Crime and Justice Research Institute (UNICRI) (Figure 1). Besides providing a network of nuclear forensics laboratories that are able to assist the global community during a nuclear smuggling event, the ITWG is also committed to the advancement of the science of nuclear forensic analysis, largely through participation in periodic table top and Collaborative Materials Exercises (CMXs). Exercise scenarios use “real world” samples with realistic forensics investigation time constraints and reporting requirements. These exercises are designed to promote best practices in the field and test, evaluate, and improve new technical capabilities, methods and techniques in order to advance the science of nuclear forensics. Past efforts to advance nuclear forensic science have also included scenarios that asked laboratories to adapt conventional forensics methods (e.g. DNA, fingerprints, tool marks, and document comparisons) for collecting and preserving evidence comingled with radioactive materials.

Schwantes, Jon M.↗
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗