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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

ATR-SEIRAS Reveals Potential Inversion and Associated Electron Transfer Kinetics in the Reduction of Surface-Confined Anthraquinone

The detection of stable semiquinone radicals on an anthraquinone (AQ) layer chemically grafted to an electrode surface in aqueous electrolytes has been elucidated by using attenuated total reflection surface enhanced infrared absorption spectroscopy (ATR-SEIRAS). In very alkaline conditions (pH 13), the reduction of the AQ involves no proton transfer, but surface sensitive infrared spectroscopy reveals that the anthraquinone dianion forms a strong hydrogen bonding network with coadsorbed water, leading to irreversible features in the voltammetry. The potential dependence of the IR band assigned to the AQ radical is consistent with the enhanced hydrogen bonding network causing increased stabilization of the quinone radical and supports the predicted response of a system under mild potential inversion, whereby the formal potential for the reduction of the anthraquinone radical is positive of the reduction potential of the neutral AQ molecule. Time-resolved ATR-SEIRAS is used to measure the transient formation of the AQ •– radical, from which rate constant information can be extracted using the Butler–Volmer model involving two one-electron transfers without a direct disproportionation reaction. The potential dependence of the rate constants is consistent with the potential inversion and can be used to qualitatively simulate the measured cyclic voltammograms. In conclusion, the thermodynamic and kinetic analyses re-emphasize long established deficiencies associated with using one-electron reaction formalisms to characterize multi-electron systems.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2021

Clean Cities Coalitions: Advancing Affordable, Domestic Transportation Fuels and Technologies Across the Country

This fact sheet provides an overview of the U.S. Department of Energy's (DOE's) Office of Energy Efficiency and Renewable Energy's Vehicle Technologies Office Clean Cities coalitions, which advance affordable, domestic transportation fuels and technologies nationwide. Nearly 100 coalitions serve as the foundation of Clean Cities, working in communities across the country to help local decision makers and fleets understand and implement alternative and renewable fuels, idle-reduction measures, fuel economy improvements, new mobility choices, and emerging transportation technologies. At the national level, VTO develops and promotes publications, tools, and other unique resources to support coordinators. At the local level, coalitions leverage these resources to create networks of stakeholders.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Successful In Situ Remediation of Cr(VI)-Impacted Deeper Vadose Zone Soils for Groundwater Protection - 18603

Shallow soil at a former industrial wood treating facility was impacted with chromium, arsenic, and copper above cleanup goals and shallow soil has been excavated for protection of direct contact exposure pathways. Deeper vadose zone soil was predominantly impacted with chromium and was undergoing remediation for long-term groundwater protection. The 920-meter (m) long Cr(VI) groundwater plume has undergone full-scale remediation with groundwater recirculation and in situ bioremediation. Following approximately 4 years of operation, the Cr(VI) groundwater plume, with former source area groundwater concentrations over 10,000 micrograms per liter (μg/L), has been reduced to below the laboratory method reporting limit (0.50 μg/L). Approximately 23,000 cubic meters (m{sup 3}) of deeper (4.5 m - 11 m below ground surface, bgs) vadose zone soil required Cr(VI) remediation. The remediation strategy relied on reduction of Cr(VI) and geochemical fixation as trivalent chromium (Cr(III)) and iron hydroxide precipitates. Chromium reduction was facilitated by a carbon source (corn syrup and methanol) to establish and maintain biogeochemical reducing conditions and ferrous iron to support direct Cr(VI) chemical reduction. Combined, the injected soil moisture, carbon source and ferrous iron supported fast and sustained ferrous/ferric iron cycling and Cr(VI) reduction. Remedy performance monitoring included time-series water quality data from a network of vacuum lysimeters and confirmation soil borings. Reagent was applied either through liquid injection wells and direct-push injection points, or by deep soil mixing methodology in areas with difficult geology (i.e., fine-grained soils and discontinuous iron-silica hardpan layer). Following both phases of deep soil remediation, soils met long-term protection of groundwater remediation action objectives. In situ treatment of soils provides a cost-effective remedial solution compared to excavation, specifically when impacts are deep and long-term protection of groundwater is a primary remediation goal. The delivery methods employed in this case study can be applied to sites impacted with a broad range of contaminants amendable to geochemical fixation (e.g., chromium, uranium, strontium, nitrate, etc.). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1990

High speed all optical networks

An inherent problem of conventional point-to-point wide area network (WAN) architectures is that they cannot translate optical transmission bandwidth into comparable user available throughput due to the limiting electronic processing speed of the switching nodes. The first solution to wavelength division multiplexing (WDM) based WAN networks that overcomes this limitation is presented. The proposed Lightnet architecture takes into account the idiosyncrasies of WDM switching/transmission leading to an efficient and pragmatic solution. The Lightnet architecture trades the ample WDM bandwidth for a reduction in the number of processing stages and a simplification of each switching stage, leading to drastically increased effective network throughputs. The principle of the Lightnet architecture is the construction and use of virtual topology networks, embedded in the original network in the wavelength domain. For this construction Lightnets utilize the new concept of lightpaths which constitute the links of the virtual topology. Lightpaths are all-optical, multihop, paths in the network that allow data to be switched through intermediate nodes using high throughput passive optical switches. The use of the virtual topologies and the associated switching design introduce a number of new ideas, which are discussed in detail.

Chlamtac, Imrich↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluating the Feasibility of Transactive Approach for Voltage Management Using Inverters of a PV Plant

This article evaluates the feasibility of a double-auction-based Transactive Energy System (TES) for engaging the reactive power (Var) capability of inverters in a utility-scale pho¬tovoltaic (PV) plant for voltage management in distribution feed¬ers. In this approach, a PV plant owner (seller) provides Var supply curves in terms of price-quantity pairs consider¬ing the inverters’ P-Q capability, efficiency, opportunity cost for real power curtail¬ment, losses, etc. At the same time, the utility (buyer) proposes Var demand curves in price-quantity pairs exhibiting the marginal price of Var. Then, market clearing points are obtained from the intersection points of supply and demand curves which are then used to create Var dispatch sig-nals from the PV plant. To conduct a fea¬sibility study of this approach, an Australian distribution network with a utility-scale PV plant (capacity 3.275 MWP) is modeled in RSCAD and simu¬lated in real-time on MATLAB, RSCAD-RTDS co-simulation platform using real historical data. Consid¬ering a use case, namely, conservation voltage reduction (CVR), the operational feasibility along with cost-benefit analysis is demonstrated in software-in-the-loop (SIL) and hardware-in-the-loop (HIL) platform. Another use case, exploring the benefit from tap-change reduction of step-voltage-regulator, is investigated. These studies explore further use cases, e.g., PV hosting capacity, network reconfiguration, etc., to be incorporated in TES for experiencing substantial economic benefits.

Alam, Mollah Rezaul↗
DOE OSTI2016

Li/Ag 2 VO 2 PO 4 batteries: the roles of composite electrode constituents on electrochemistry

In this study, we utilize silver vanadium phosphorous oxide, Ag 2 VO 2 PO 4 , as a model system to systematically study the impact of the constituents of a composite electrode, including polymeric and conductive additives, on electrochemistry. Notably, although highly resistive, this bimetallic cathode can be discharged as a pure electroactive material in the absence of a conductive additive as it generates an in situ conductive matrix via a reduction displacement reaction resulting in the formation of silver metal nanoparticles. Also, three different electrode compositions were investigated: Ag 2 VO 2 PO 4 only, Ag 2 VO 2 PO 4 4 with binder, and Ag 2 VO 2 PO 4 with binder and carbon. Constant current discharge, pulse testing and impedance spectroscopy measurements were used to characterize the electrochemical properties of the electrodes as a function of depth of discharge. In situ EDXRD was used to spatially resolve the discharge progression within the cathode by following the formation of Ag 0 . Ex situ XRD and EXAFS modeling were used to quantify the amount of Ag 0 formed. Results indicate that the metal center reduced (V 5+ or Ag + ) was highly dependent on composite composition (presence of PTFE, carbon), depth of discharge (Ag 0 nanoparticle formation), and spatial location within the cathode. The addition of a binder was found to increase cell polarization, and the percolation network provided by the carbon in the presence of PTFE was further increased with reduction and formation of Ag 0 . Lastly, this study provides insight into the factors controlling the electrochemistry of resistive active materials in composite electrodes.

25 ENERGY STORAGE↗
DOE OSTI2016

Impact of Multifunctional Bimetallic Materials on Lithium Battery Electrochemistry

Electric energy storage devices such as batteries are complex systems comprised of a variety of materials with each playing separate yet interactive roles, complicated by length scale interactions occurring from the molecular to the mesoscale. Thus, addressing specific battery issues such as functional capacity requires a comprehensive perspective initiating with atomic level concepts. For example, the electroactive materials which contribute to the functional capacity in a battery comprise approximately 30% or less of the total device mass. Thus, the design and implementation of multifunctional materials can conceptually reduce or eliminate the contribution of passive materials to the size and mass of the final system. Material multi-functionality can be achieved through appropriate material design on the atomic level resulting in bimetallic electroactive materials where one metal cation forms mesoscale conductive networks upon discharge while the other metal cation can contribute to atomic level structure and net functional secondary capacity, a device level issue. Specifically, this Account provides insight into the multi-mechanism electrochemical redox processes of bimetallic cathode materials based on transition metal oxides (MM’O) or phosphorous oxides (MM’PO) where M = Ag and M’ = V or Fe. One discharge process can be described as reduction-displacement where Ag + is reduced to Ag 0 and displaced from the parent structure. This reduction-displacement reaction in silver-containing bimetallic electrodes allows for the in-situ formation of a conductive network, enhancing the electrochemical performance of the electrode and reducing or eliminating the need for conductive additives. A second discharge process occurs through the reduction of the second transition metal, V or Fe, where the oxidation state of the metal center is reduced and lithium cations are inserted into the structure. As both metal centers contribute to the functional capacity, determining the kinetically and thermodynamically preferred reduction processes at various states of discharge is critical to elucidating the mechanism. Specific advanced in-situ and ex-situ characterization techniques are conducive to gaining insight regarding the electrochemical behavior of these multifunctional materials over multiple length scales. At the material level, optical microscopy, scanning electron microscopy, and local conductivity measurement via a nanoprobe can track the discharge mechanism of an isolated single particle. At the mesoscale electrode level, in-situ data from synchrotron based energy dispersive X-ray diffraction (EDXRD) within fully intact steel batteries can be used to spatially map the distribution of silver metal generated through reduction displacement as a function of discharge depth and discharge rate. As illustrated here, appropriate design of materials with multiple electrochemically active metal centers and properties tuned through strategically conceptualized materials synthesis may provide a path toward the next generation of high energy content electroactive materials and systems. In conclusion, full understanding of the multiple electrochemical mechanisms can be achieved only by utilizing advanced characterization tools over multiple length scales.

25 ENERGY STORAGE↗
DOE OSTI2024

Visual Analytics of Multivariate Networks With Representation Learning and Composite Variable Construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This article presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Adapting In Situ Accelerators for Sparsity With Granular Matrix Reordering

Neural network (NN) inference is an essential part of modern systems and is found at the heart of numerous applications ranging from image recognition to natural language processing. In situ NN accelerators can efficiently perform NN inference using resistive crossbars, which makes them a promising solution to the data movement challenges faced by conventional architectures. Although such accelerators demonstrate significant potential for dense NNs, they often do not benefit from sparse NNs, which contain relatively few non-zero weights. Processing sparse NNs on in situ accelerators results in wasted energy to charge the entire crossbar where most elements are zeros. To address this limitation, this paper proposes Granular Matrix Reordering (GMR): a preprocessing technique that enables an energy-efficient computation of sparse NNs on in situ accelerators. GMR reorders the rows and columns of sparse weight matrices to maximize the crossbars' utilization and minimize the total number of crossbars needed to be charged. The reordering process does not rely on sparsity patterns and incurs no accuracy loss. Finally, GMR achieves an average of 28% and up to 34% reduction in energy consumption over seven pruned NNs across four different pruning methods and network architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems

Preventing and slowing the spread of epidemics is achieved through techniques such as vaccination and social distancing. Given practical limitations on the number of vaccines and cost of administration, optimization becomes a necessity. Previous approaches using mathematical programming methods have shown to be effective but are limited by computational costs. In this work, we make several contributions: First, we present a new approach for intervention via maximizing the influence of vaccinated nodes on the network. We call this method \preempt. Next, we prove submodular properties associated with the objective function of our method so that it aids in construction of an efficient greedy approximation strategy. Consequently, we present a new parallel algorithm based on greedy hill climbing for \preempt, and present an efficient parallel implementation for distributed CPU-GPU heterogeneous platforms. Our results demonstrate that \preempt{} is able to achieve a significant reduction (up to 6.75$\times$) in the percentage of people infected on a city-scale network. We also show strong scaling results of \preempt{} on 128 nodes of the Summit supercomputer. Our parallel implementation is able to significantly reduce time to solution, from hours to minutes on large networks. This work represents a first-of-its-kind effort in parallelizing greedy hill climbing and applying it toward devising effective interventions for epidemics.

Minutoli, Marco↗
DOE OSTI2022

Characterizing manufacturing sector disruptions with targeted mitigation strategies

It has become clear in recent decades that manufacturing supply chains are increasingly vulnerable to disruptions of varying geographical scales and intensities. These disruptions—whether intentional, accidental, or resulting from natural disasters—cause failures and capacity reductions to manufacturing infrastructure, with lasting effects that can cascade throughout the manufacturing network. An overall lack of understanding of solutions to mitigate disturbances has rendered the challenge of reducing manufacturing supply chain vulnerability even more difficult. Additionally, the variability of disruptions and their impacts complicates policy maker and stakeholder efforts to plan for specific disruptive scenarios. It is necessary to comprehend different kinds of disturbances and group them based on stakeholder-provided metrics to support planning processes and modeling efforts that promote adaptable, resilient manufacturing supply chains. This paper reviews existing methods for risk management in manufacturing supply chains and the economic and environmental impacts of disruptions. In addition, we develop a framework using agglomerative hierarchical clustering to classify disruptions using U.S. manufacturing network data between 2000 and 2021 and characteristic metrics defined in the literature. Our review identifies five groups of disruptions and discusses both general mitigation methods and strategies targeting each identified group. Further, we highlight gaps in the literature related to estimating and including environmental costs in disaster preparedness and mitigation planning. We also discuss the lack of easily available metrics to quantify environmental impacts of disruptions and how such metrics could be included into our methodology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1974

Note on the transfer wave matrix

A relationship exists between the elements of the transfer wave matrix and the ABCD matrix elements which allows a considerable reduction of effort required to find the matrix for cascaded two-port network sections. This relationship and the simplified expressions for obtaining the matrix elements are presented here.

Chang, F.-C.↗
NASA NTRS1988

Heat exchanger expert system logic

The reduction is described of the operation and fault diagnostics of a Deep Space Network heat exchanger to a rule base by the application of propositional calculus to a set of logic statements. The value of this approach lies in the ease of converting the logic and subsequently implementing it on a computer as an expert system. The rule base was written in Process Intelligent Control software.

Cormier, R.↗
DOE OSTI2025

Aided Active Learning (AAL) for Enhanced Critical Heat Flux Prediction

Accurate prediction of critical heat flux (CHF) is crucial for the safe and efficient operation of nuclear reactors. Traditional CHF modeling methods often require extensive experimental data, which are hard to obtain. This study introduces the Aided Active Learning (AAL) framework, which strategically minimizes data requirements without sacrificing model accuracy. Unlike conventional Active Learning (AL), AAL introduces an additional step of randomly selecting a subset from the sample pool before applying the query strategy. To evaluate the performance of AAL, two query strategies—uncertainty-based sampling and error-reduction sampling—were evaluated across the following models: random forest (RF), feedforward neural network (FNN), and variational feedforward neural network (vFNN). The proposed framework demonstrated that AAL effectively reduces the number of training samples needed to achieve comparable predictive accuracy. For the RF model, AL required only 710 samples to achieve an R2 score of 0.98, as compared to the 4,785 samples needed by random sampling. Similarly, the FNN model achieved the same R2 score with just 355 samples when using AL, a significant improvement over the 825 samples required by random sampling. In case of uncertainty-based sampling strategy, vFNN attained an R2 of 0.98 with 3,420 samples, reducing the sample requirement by 47% relative to the 6,440 samples needed for random sampling. Its performance suggests that larger training data are required to fully leverage its uncertainty quantification capabilities.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

All-Atom Molecular Dynamics Simulations of the Temperature Response of Densely Grafted Polyelectrolyte Brushes

Water acts a “good” solvent for most polyelectrolyte (PE) systems due to its high dielectric strength. Theoretical and experimental studies have highlighted some remarkably unconventional properties of strongly water-swollen PE brushes such as a reduction in brush height with increase in temperature. However, an understanding of the atomistic-scale response of the grafted PE chains as well as the local arrangement and organization of the brush-supported counterions and water molecules to changes in temperature is missing. Here, we conduct an all-atom molecular dynamics (MD) study to probe the influence of temperature on the “microstructure” of densely grafted, fully ionized polyacrylic acid (PAA) brushes. The atomistic insights obtained from our study shed light on prior experimental observations and elucidate the key role played by changes in hydrogen bonding network in dictating the hydrophilicity of the brushes. In addition to the temperature-mediated reduction in height of the PE brush layer, we study the influence of temperature on several properties of the brush-supported water molecules such as the number distribution and kinetics of water-water and water-PE hydrogen bonds (HBs), mass density, orientational tetrahedral order parameter, and the activation energy associated with the self-diffusion of water. The effect of temperature on the properties of the brush-trapped counterions is also quantified via changes in their solvation structure, counterion-water radial distribution function (RDF), and the translational mobility. Our findings unravel several hitherto unknown phenomena such as the enhanced propensity for attaining PE brush-induced “water-in-salt”-like scenarios at elevated temperatures, and an increase in the activation energy for the self-diffusion of water with increase in the degree of PE brush-induced nanoconfinement (from ‘bulk water’ to ‘sparsely grafted PE brushes’ to ‘densely grafted PE brushes’). Further, we anticipate that this work will be helpful in improving our predictive capabilities of the temperature response of water-swollen PE brushes, which would be instrumental for better design of several PE brush systems used in a multitude of applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗