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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Simultaneous Insight into Dissolution and Aggregation of Metal Sulfide Nanoparticles through Single-Particle Inductively Coupled Plasma Mass Spectrometry

Nanoparticles (NPs) and their colloidal aggregates are ubiquitous and play important roles in the transport and release of metals. Knowledge of their dissolution rates and aggregation behavior in solution are crucial for better prediction of their fate in biogeochemical cycling, ecotoxicity, and environmental remediation. There are however significant technical challenges to accurately obtain such information as a result of the heterogeneity and highly dynamic transformation exhibited by NPs, particularly at relatively low particle concentrations in aqueous systems. Here, we quantitatively examine the simultaneous dissolution and aggregation behavior of metal sulfide NPs using single-particle inductively coupled plasma mass spectrometry (spICP–MS). We focus on nickel sulfide (NiS), with additional data presented for copper sulfide (CuS) and cobalt sulfide (CoS). The kinetics of metal release (dissolution and disaggregation) of NiS was fastest under strongly oxidizing conditions (from 0.04 to >3 min –1 with H 2 O 2 ) and were slower under near-neutral (HEPES buffer/H 2 O) and acidic (1 mM HNO 3 ) conditions (≤0.006 min –1 ). Metal release kinetics in HNO 3 was not faster than in H 2 O or HEPES, suggesting that the solution pH has an influence over both the dissolution kinetics of individual particles and the NP aggregation states, which in combination affect metal release rates over time. Between different metal sulfides, the measured metal release rates were largely consistent with predictions based on the crystallinity, solubility products, and specific surface areas of the NPs, following an order of CoS > NiS > CuS. Here, the spICP–MS approach described here can be easily applied to the characterization of metal release and aggregation of other NPs at low concentrations (~10 5 particles/mL) typically found in natural environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Communication: Orientational self-ordering of spin-labeled cholesterol analogs in lipid bilayers in diluted conditions

Lipid-cholesterol interactions are responsible for different properties of biological membranes including those determining formation in the membrane of spatial inhomogeneities (lipid rafts). To get new information on these interactions, electron spin echo (ESE) spectroscopy, which is a pulsed version of electron paramagnetic resonance (EPR), was applied to study 3β-doxyl-5α-cholestane (DCh), a spin-labeled analog of cholesterol, in phospholipid bilayer consisted of equimolecular mixture of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine and 1,2-dioleoyl-sn-glycero-3-phosphocholine. DCh concentration in the bilayer was between 0.1 mol.% and 4 mol.%. For comparison, a reference system containing a spin-labeled 5-doxyl-stearic acid (5-DSA) instead of DCh was studied as well. The effects of “instantaneous diffusion” in ESE decay and in echo-detected (ED) EPR spectra were explored for both systems. The reference system showed good agreement with the theoretical prediction for the model of spin labels of randomly distributed orientations, but the DCh system demonstrated remarkably smaller effects. The results were explained by assuming that neighboring DCh molecules are oriented in a correlative way. However, this correlation does not imply the formation of clusters of cholesterol molecules, because conventional continuous wave EPR spectra did not show the typical broadening due to aggregation of spin labels and the observed ESE decay was not faster than in the reference system. So the obtained data evidence that cholesterol molecules at low concentrations in biological membranes can interact via large distances of several nanometers which results in their orientational self-ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

CostMAP: an open-source software package for developing cost surfaces using a multi-scale search kernel

Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features introduce akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that determines these critical barriers and corridors. We have built this method into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).

58 GEOSCIENCES↗
DOE OSTI2013

THE EINSTEIN-HOME SEARCH FOR RADIO PULSARS AND PSR J2007+2722 DISCOVERY

Einstein-Home aggregates the computer power of hundreds of thousands of volunteers from 193 countries, to search for new neutron stars using data from electromagnetic and gravitational-wave detectors. This paper presents a detailed description of the search for new radio pulsars using Pulsar ALFA survey data from the Arecibo Observatory. The enormous computing power allows this search to cover a new region of parameter space; it can detect pulsars in binary systems with orbital periods as short as 11 minutes. We also describe the first Einstein-Home discovery, the 40.8 Hz isolated pulsar PSR J2007+2722, and provide a full timing model. PSR J2007+2722's pulse profile is remarkably wide with emission over almost the entire spin period. This neutron star is most likely a disrupted recycled pulsar, about as old as its characteristic spin-down age of 404 Myr. However, there is a small chance that it was born recently, with a low magnetic field. If so, upper limits on the X-ray flux suggest but cannot prove that PSR J2007+2722 is at least {approx}100 kyr old. In the future, we expect that the massive computing power provided by volunteers should enable many additional radio pulsar discoveries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

In-Memory Graph Databases for Web-Scale Data

RDF databases have emerged as one of the most relevant way for organizing, integrating, and managing expo- nentially growing, often heterogeneous, and not rigidly structured data for a variety of scientific and commercial fields. In this paper we discuss the solutions integrated in GEMS (Graph database Engine for Multithreaded Systems), a software framework for implementing RDF databases on commodity, distributed-memory high-performance clusters. Unlike the majority of current RDF databases, GEMS has been designed from the ground up to primarily employ graph-based methods. This is reflected in all the layers of its stack. The GEMS framework is composed of: a SPARQL-to-C++ compiler, a library of data structures and related methods to access and modify them, and a custom runtime providing lightweight software multithreading, network messages aggregation and a partitioned global address space. We provide an overview of the framework, detailing its component and how they have been closely designed and customized to address issues of graph methods applied to large-scale datasets on clusters. We discuss in details the principles that enable automatic translation of the queries (expressed in SPARQL, the query language of choice for RDF databases) to graph methods, and identify differences with respect to other RDF databases.

GEMS↗
DOE OSTI2021

sycl-reduction benchmark v1.0.0

Some different SYCL implementations of the reduction operation. The parallel reduction kernel aggregates vector elements into a single result. It consists of four ways to implement the reduction operation using two different data management strategies, which we could not find elsewhere as a single package.

Dufek, Amanda↗
DOE OSTI2023

Azure Data Tools (FKA: OEDI (Open Energy Data Initiative) Data Access Tools) [SWR-23-92]

The Open Energy Data Initiative (OEDI) provides a number of tools to enable the use of the open data published through this initiative. The source is largely written in Python, including Jupyter notebooks. The Open Energy Data Initiative (OEDI) is a partnership between the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), Amazon, Microsoft, and Google to provide universal access to big data in the cloud. At the heart of OEDI is a centralized repository of high-value energy research datasets aggregated from DOE Program Offices, National Laboratories and other collaborators.

Jonathan, Weers↗
DOE OSTI2023

Wildfire Smoke PM2.5 per Zipcode

This dataset contains daily aggregated measurements of daily PM2.5 from ambient wildfire smoke in the contiguous United States, spanning from 2006 to 2016. The data is sourced from a study by Childs et al. (2022), titled "Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US" published in Environmental Science & Technology. To compute the standardized weight across different zip codes, we computed the weight calculation on a 10km grids. To compute the weight, we obtain area-weights that add up to 1 in each polygon of zipcodes. This enabled us to calculate the smoke values per zipcode for the aforementioned period. Those interested in replicating our data processing pipeline can access it at https://github.com/NSAPH-Data-Processing/smoke_aggregation.

PM2.5↗
DOE OSTI2026

Underlying Data for the EVI-RoadTrip Web Tool

The dataset contains simulation-based charging infrastructure outputs that are visualized on the EVI-RoadTrip webtool. The outputs are aggregated to lower spatial resolution (e.g., state-level, corridor-level).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Omega-Limit Sets and Input-To-State Stability in Power Grids with Switching Equilibria

This paper studies a power transmission system with both conventional generators (CGs) and distributed energy assets (DEAs) providing frequency control. We consider an operating condition with demand aggregating two dynamic components: one that switches between different values on a finite set, and one that varies smoothly over time. Such dynamic operating conditions may result from protection scheme activations, external cyber-attacks, or due to the integration of dynamic loads, such as data centers. Mathematically, the dynamics of the resulting system are captured by a system that switches between a finite number of vector fields - or modes -, with each mode having a distinct equilibrium point induced by the demand aggregation. To analyze the stability properties of the resulting switching system, we leverage tools from hybrid dynamic inclusions and the concept of ..omega.. -limit sets from sets. Specifically, we characterize a compact set that is semi-globally practically asymptotically stable under the assumption that the switching frequency and load variation rate are sufficiently slow. For arbitrarily fast variations of the load, we use a level-set argument with multiple Lyapunov functions to establish input-to-state stability of a larger set and with respect to the rate of change of the loads. The theoretical results are illustrated via numerical simulations on the IEEE 39-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a “few-shot learning” challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

An Exploratory Data Mining Investigation for Constructing a Publicly Sourced Dataset of Foreign Hypersonic Tests

This document details a data mining exercise that resulted in an exploratory dataset of publicly reported foreign (non-US) hypersonic vehicle test events. Using a combination of targeted English language searches and country-specific queries, the study aggregates information from digital news media, official press releases, and social media posts. The resulting list of events captures the publicly available accounts of foreign hypersonic tests, although it does not represent an exhaustive record. Limitations such as inconsistent reporting, translation challenges, and the inherently provisional nature of open-source data are acknowledged. This dataset serves as an initial reference point for further inquiries into high-speed atmospheric phenomena and may facilitate future efforts to correlate these events with geophysical measurements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Tethys Water Demand Data

U.S. water demand varies sharply by sector and region as land use, population, weather patterns, and economic activity co-evolve. High-resolution water demand data is required to capture these dynamics, support integrated energy-water-land modeling, and local-to-regional water scarcity assessments. This dataset contains gridded (1/8 degree), monthly, multi-sector water demand dataset for the contiguous United States (CONUS) covering 1980-2100 across eight future scenarios of human-Earth system change. The dataset covers irrigation, thermoelectric, municipal (public-supply and domestic), livestock, manufacturing, and mining demands, separately for withdrawals and consumption, and includes per-cell renewable vs. non-renewable water source attributions. The dataset is validated against the latest USGS 2010-2020 water-use data for the three largest water demand sectors (Domestic, Electricity, and Irrigation), with correlations ranging from 0.73-0.95 at the HUC6 scale. The two datasets largely agree on an aggregate basis with per-sector bias falling within +/-7%, but they disagree on the spatial allocation of water with individual HUC6 basins having normalized RMSE from 68-171% and median absolute percent difference from 37-86%. This dataset advances prior global products by combining state-resolved sectoral demands from GCAM-USA, future power-plant siting from the CERF model, and scenario-consistent high-resolution climate and population forcing data across the eight scenarios.

GCAM-USA↗
DOE OSTI2025

Ionic Associations and Hydration in the Electrical Double Layer of Water-in-Salt Electrolytes

Water-in-Salt-Electrolytes (WiSEs) are an exciting class of concentrated electrolytes finding applications in energy storage devices because of their expanded electrochemical stability window, good conductivity and cation transference number, and fire-extinguishing properties. These distinct properties are thought to originate from the presence of an anion-dominated ionic network and interpenetrating water channels for cation transport, which indicates that associations in WiSEs are crucial to understanding their properties. Currently, associations have mainly been investigated in the bulk, while little attention has been given to the electrolyte structure near electrified interfaces. Here, we develop a theory for the electrical double layer (EDL) of WiSEs, where we consistently account for the thermoreversible associations of species into Cayley tree aggregates. The theory predicts an asymmetric structure of the EDL. At negative voltages, hydrated Li + dominates, and cluster aggregation is initially slightly enhanced before disintegration at larger voltages. At positive voltages, when compared to the bulk, clusters are strictly diminished. Performing atomistic molecular dynamics (MD) simulations of the EDL of WiSE provides EDL data for validation and bulk data for parametrization of our theory. Validating the predictions of our theory against MD showed good qualitative agreement. Furthermore, we performed electrochemical impedance measurements to determine the differential capacitance of the studied LiTFSI WiSE and also found reasonable agreement with our theory. Overall, the developed approach can be used to investigate ionic aggregation and solvation effects in the EDL, which, among other properties, can be used to understand the precursors for solid-electrolyte interphase formation.

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

Performance Year 1 Technical Report - OPEN COG Grid: Extendable Coherent Models-Datasets for Cognitive Power Grids

The OPEN COG Grid project is a collaborative effort between LLNL, NREL, and Texas A&M University (TAMU) to develop synthetic power system datasets that (i) contain all technical information that would be available in a real system, allowing to conduct studies ranging from dynamic simulation to long term planning studies; ii) are accessible to researchers from the broader data sciences community, as oppossed to power system experts only; and (iii) This report summarizes the work conducted during the first 15 months of execution of the project. These activities encompassed: 1. Conduct a survey of existing open data sets and open source power systems simulators, their supported use cases, and accessibility (Chapter 1). 2. Define a new extensible specification for power system data, covering all parameters necessary for most computational use cases (Chapter 2). 3. Collecting real technical system data to complete missing parameters in existing open source datasets (Chapter 3). 4. Develop models that capture the behavior of emergent actors in power grids, neglected by existing datasets; aggregated residential demand response (Chapter 4) and demand response of cryptocurrency miners (Chapter 5). 5. Collect detailed spatial information on distributed energy resources, particular, solar photovoltaic facilities (Chapter 6). The following chapters provide detailed descriptions of these tasks, the assumptions taken, and their findings. In conducting these tasks, the project team produced: two (accepted) conference papers; one journal paper under submission; one draft journal paper pending submission; released one repository with the developed power system data specification, with documentation and examples; and one extended dataset for the Texas power grid under review for release. The team hopes these contributions will enhance access to power system data and remove barriers to the development of new computational techniques for power systems, particularly, those inspired by cognitive sciences.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Crystal Structures of Diaryl Hydrazone and Sulfone Stabilizers in Complex with an Amyloidogenic Light Chain Reveal an Alternate Ligand‐Binding Cavity

Abstract Stabilization of amyloidogenic immunoglobulin light chains (LCs) by binding of small molecule “kinetic stabilizers” is under development as a novel treatment for light chain amyloidosis. From a high‐throughput screen, we previously identified 16 full‐length (FL) LC stabilizers from five distinct chemotypes. We then obtained structural biological information on two classes of hits, coumarins and hydantoins, revealing that both chemotypes bind to a pocket at the V L −V L interface of the FL LC dimer. Here, we report crystal structures of three screening hits from two other chemotypes, diaryl hydrazones and sulfones, in complex with an amyloidogenic FL LC. While two of these hits bind to the previously identified pocket, one diaryl hydrazone binds to a different pocket bisected by the C 2 symmetry axis of the dimer. These data further expand on the FL LC stabilizer‐binding surface that could be used in design of more potent FL LC aggregation inhibitors.

Chemistry↗