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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Hydrothermally robust Ti/SiO 2 epoxidation catalysts via surface modification with oligomeric PMHS

We investigate grafting of poly(methylhydrosiloxane) (PMHS) onto Ti/SiO 2 catalysts for improving their robustness to water, using olefin epoxidation catalysis with organic hydroperoxide as oxidant as a relevant probe reaction. Our approach involves triggering catalyst deactivation with added water in the reactants as a means to simulate catalyst deactivation, thereby causing grafted Ti-site aggregation. When using harsh aqueous Soxhlet extraction conditions as a trigger for this aggregation, significant differences are observed via diffuse-reflectance UV-Vis spectrscopy on samples before and after treatment, for Ti/SiO 2 catalysts containing grafted PMHS. Not only was there a lack of appearance of red-shifted bands in the UV-Vis spectrum following Soxhlet extraction, the batch catalytic activity for catalysts before and after PMHS grafting remained virtually unchanged. This was in stark contrast to an unmodified Ti/SiO 2 catalyst, which showed a more than 60% decrease in catalytic activity following Soxhlet extraction as a result of aggregation, as ascertained by red shifts in its UV-Vis spectrum after Soxhlet extraction. Similar red shifts were also observed for a hydrophobically modified TMS Ti/SiO 2 catalyst. However, when comparing these catalysts under more gentle olefin epoxidation catalysis conditions with added water in a flow reactor, it was more difficult to differentiate the TMS Ti/SiO2 and grafted PMHS catalysts. Both catalysts exhibited similar activity and selectivity after a prolonged time on stream, though the rate of initial deactivation in the latter was less pronounced. Altogether, our data demonstrate that grafted PMHS catalysts are positively differentiated from conventional catalysts based on either TMS surface modification or no surface modification, under conditions involving aggregation induced via extreme hydrothermal environmentst. Thus, we expect that PMHS grafting of Lewis acid catalysts can offer an alternative approach for endowing hydrolytic stability to such catalysts, as demonstrated here for Ti/SiO 2 epoxidation catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Photoactive Blend Morphology Engineering through Systematically Tuning Aggregation in All-Polymer Solar Cells

Polymer aggregation plays a critical role in the miscibility of materials and the performance of all-polymer solar cells (APSCs). However, many aspects of how polymer texturing and aggregation affect photoactive blend film microstructure and photovoltaic performance are poorly understood. Here the effects of aggregation in donor–acceptor blends are studied, in which the number-average molecular weights (Mns) of both an amorphous donor polymer, poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b]dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-)-2-carboxylate-2-6-diyl)] (PBDTT-FTTE) and a semicrystalline acceptor polymer, poly{[N,N'-bis(2-octyldodecyl)naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)} (P(NDI2OD-T2)) are systematically varied. The photovoltaic performance is correlated with active layer microstructural and optoelectronic data acquired by in-depth transmission electron microscopy, grazing incidence wide-angle X-ray scattering, thermal analysis, and optical spectroscopic measurements. Coarse-grained modeling provides insight into the effects of polymer aggregation on the blend morphology. Notably, the computed average distance between the donor and the acceptor polymers correlates well with solar cell photovoltaic metrics such as short-circuit current density (Jsc) and represents a useful index for understanding/predicting active layer blend material intermixing trends. Importantly, these results demonstrate that for polymers with different texturing tendencies (amorphous/semicrystalline), the key for optimal APSC performance, photovoltaic blend morphology can be controlled via both donor and acceptor polymer aggregation.

bio-inspired↗
DOE OSTI2018

Impact of acidic pH on plant cell wall polysaccharide structure and dynamics: insights into the mechanism of acid growth in plants from solid-state NMR

Acidification of plant primary cell walls causes cell wall expansion and plant growth. To understand how acidic pH affects the molecular structure and dynamics of wall polysaccharides, we have now characterized and compared Arabidopsis thaliana primary cell walls in neutral (pH 6.8) and acidic (pH 4.0) conditions using solid-state NMR spectroscopy. Quantitative 13C solid-state NMR spectra indicate that the pH 4.0 cell wall has neutral galacturonic acid residues in homogalacturonan (HG) and rhamnogalacturonan (RG). 13C INEPT spectra, which selectively detect highly dynamic polymers, indicate that some of the HG and RG chains in the interfibrillar region have become more dynamic in the acidic wall compared to the neutral cell wall, whereas other chains have become more rigid. Consistent with this increased dynamic heterogeneity, C–H dipolar couplings and 2D 13C–13C correlation spectra indicate that some of the HG backbones are partially aggregated in the acidic cell wall. Moreover, 2D correlation spectra measured with long mixing times indicate that the acidic cell wall has weaker cellulose–pectin interactions, and water-polysaccharide 1H spin diffusion data show that cellulose microfibrils are better hydrated at low pH. Taken together, these results indicate a cascade of chemical and conformational changes of wall polysaccharides due to cell wall acidification. These changes start with neutralization of the pectic polysaccharides, which disrupts calcium crosslinking of HG, causes partial aggregation of the interfibrillar HG, weakens cellulose–pectin interactions, and increases the hydration of both cellulose microfibrils and matrix polysaccharides. These molecular-level structural and dynamical changes are expected to facilitate polysaccharide slippage, which underlies cell wall loosening and expansion, and may occur both independent of and as a consequence of protein-mediated wall loosening.

biofuels (including algae and biomass), bio-inspir↗
DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗
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 OSTI2012

Evaluation of Miscellaneous and Electronic Device Energy Use in Hospitals

Miscellaneous and electronic loads (MELs) consume about one-thirdof the primary energy used in US buildings, and their energy use is increasing faster than other end-uses. In healthcare facilities, 30percent of the annual electricity was used by MELs in 2008. This paper presents methods and challenges for estimating medical MELs energy consumption along with estimates of energy use in a hospital by combining device-level metered data with inventories and usage information. An important finding is that common, small devices consume large amounts of energy in aggregate and should not be ignored when trying to address hospital energy use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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↗