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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Experimental and Computational Evaluation of Lipidomic In-Source Fragmentation as a Result of Postionization with Matrix-Assisted Laser Desorption/Ionization

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide spatially resolved molecular information about a sample. Recently, a postionization approach (MALDI-2) has been commercially integrated with MALDI-MSI, allowing for bettered sensitivity and consequent improved spatial resolution. While advantages of MALDI-2 have previously been established, we demonstrate here statistically increased in-source fragmentation (ISF) results from postionization with a commercial instrument. Via lipid standard analyses, known MALDI ISF pathways (e.g., loss of trimethylamine) were statistically increased in MALDI-2 compared to MALDI-1 (65–172% increase in fragmentation). Gas phase molecular modeling with density functional theory estimated that the most-weighted virtual orbitals to excite within lipids involve ester and phosphate bonds. Protonated lipid excitation energies are furthermore red-shifted compared to those of other adduct types [e.g., 254 nm for protonated PC(16:0/18:1)] and approach the MALDI-2 laser energy (266 nm). Analysis of rat brain homogenate detected statistically more positive-ion mode peaks with MALDI-2 (1090) than that with MALDI-1 (719), where Kernel density estimations showed that the majority of this enhancement occurs with low m/z ions (i.e., m/z 75–500). Taken together with the lipid standard data, these observations may indicate ISF due to postionization. Finally, while artifact contributions from matrix blanks were also noted, both experimental and computational data sets suggest that the overall extent of ISF is statistically increased in MALDI-2 compared to MALDI-1.

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

Predicting Protein–Polymer Block Copolymer Self-Assembly from Protein Properties

Protein–polymer bioconjugate self-assembly has attracted a great deal of attention as a method to fabricate protein nanomaterials in solution and the solid state. To identify protein properties that affect phase behavior in protein–polymer block copolymers, a library of 15 unique protein- b -poly(N-isopropylacrylamide) (PNIPAM) copolymers comprising 11 different proteins was compiled and analyzed. Many attributes of phase behavior are found to be similar among all studied bioconjugates regardless of protein properties, such as formation of micellar phases at high temperature and low concentration, lamellar ordering with increasing temperature, and disordering at high concentration, but several key protein-dependent trends are also observed. In particular, hexagonal phases are only observed for proteins within the molar mass range 20–36 kDa, where ordering quality is also significantly enhanced. While ordering is generally found to improve with increasing molecular weight outside of this range, most large bioconjugates exhibited weaker than predicted assembly, which is attributed to chain entanglement with increasing polymer molecular weight. Additionally, order–disorder transition boundaries are found to be largely uncorrelated to protein size and quality of ordering. However, the primary finding is that bioconjugate ordering can be accurately predicted using only protein molecular weight and percentage of residues contained within β sheets. This model provides a basis for designing protein–PNIPAM bioconjugates that exhibit well-defined self-assembly and a modeling framework that can generalize to other bioconjugate chemistries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Clifford Circuit-Based Heuristic Optimization of Fermion-To-Qubit Mappings

Simulation of interacting Fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analyzing Fermionic systems with a quantum computer hinges on the efficiency of Fermion-to-qubit mappings that encode nonlocal Fermionic degrees of freedom in local qubit degrees of freedom. While recent studies have highlighted the importance of designing Fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far either are restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, in this work, we address this challenge by designing a heuristic numerical optimization framework for Fermion-to-qubit mappings. To this end, we first translate the Fermion-to-qubit mapping problem to a Clifford circuit optimization problem and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For all Fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counterparts, including ternary-tree-based mappings that are known to be optimal for single creation and annihilation operators. We find that our optimized mappings yield between 15% and 40% improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remarkably, the optimized mappings improve the average Pauli weight for 6 × 6 nearest-neighbor hopping and Hubbard models by more than 40% and 20%, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperforms any ternary-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific Fermionic Hamiltonian.

Hamiltonians↗
DOE OSTI2025

Creation of Self-Semi-Interpenetrating Network Structures in PIM-1 Membranes for Enhanced Physical Aging Resistance

A series of self-semi-interpenetrating network (ssIPN) thin films based on PIM-1 structure were developed by end-cross-linking telechelic PIM-1 oligomers end-capped with curable carboxylic acid groups to form model networks, which are penetrated by linear high-molecular-weight PIM-1 chains. ssIPN films with systematically varied network content ranging from 10 to 30 wt % were comprehensively examined on their microstructure and gas permeation properties. Fresh PIM-1 ssIPN films exhibited gas separation performances close to those of as-cast linear PIM-1, where the films closely followed the upper bound trade-off line, gaining as much selectivity as they lose gas permeability as the network content increases. This indicates that gas permeability is largely preserved despite cross-linking. Wide-angle X-ray scattering supported this observation, with peaks shifting toward lower d-spacing as oligomer content increased, suggesting tighter chain packing with higher cross-linkable oligomer loading. In physical aging studies over two months, all ssIPN films outperform linear PIM-1 for H 2 /CH 4 and O 2 /N 2 separations. Notably, the PIM-1 ssIPN with 10 wt % network content showed the best physical aging resistance, with negligible permeability loss even after one month. Finally, these findings highlight a promising macromolecular strategy for enhancing the physical aging resistance of microporous polymer membranes for gas separation.

PIM-1↗
DOE OSTI2018

Battery and backup generator sizing for a resilient microgrid under stochastic extreme events

Owing to the recent power outages caused by extreme events, installing battery energy storage and backup generators is important to improve resiliency for a grid-tied microgrid. In the design stage, the event occurrence time and duration, which are highly uncertain and cannot be effectively predicted, may affect the needed battery and backup generator capacity but are usually assumed to be pre-determined in utility planning tools. This study investigates the optimal battery and backup generator sizing problem considering the stochastic event occurrence time and duration for the grid-tied microgrid under islanded operation. The reliability requirement is quantified by the mean value of the critical customer interruption time in each stochastic islanding time window (ITW), whose length is the duration and the centre is the occurrence time. The stochastic ITW constraint is then transformed to a probability-weighted expression to derive an equivalent Mixed Integer Linear Programming model. Numerical simulations on a realistic grid-tied PV-based microgrid demonstrate that the total cost is reduced by 11.5% considering the stochastic ITW, compared with the deterministic ITW under the same reliability requirement.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗
DOE OSTI2022

The MaNGA firefly Value-Added-Catalogue: resolved stellar populations of 10,010 nearby galaxies

We present the Mapping Nearby Galaxies at Apache Point Observatory survey (MaNGA) FIREFLY Value Added Catalogue (VAC) – a catalogue of ~3.7 million spatially resolved stellar population properties across 10 010 nearby galaxies from the final data release of the MaNGA survey. The full spectral fitting code FIREFLY is employed to derive parameters such as stellar ages, metallicities, stellar and remnant masses, star formation histories, star formation rates, and dust attenuation. In addition to Voronoi-binned measurements, our VAC also provides global properties, such as central values and radial gradients. Two variants of the VAC are available: presenting the results from fits using the M11-MILES and the novel MaStar stellar population models. MaStar allows to constrain the fit over the whole MaNGA wavelength range, extends the age-metallicity parameter space, and uses empirical spectra from the same instrument as MaNGA. The fits employing MaStar models find, on average, slightly younger ages, higher mass-weighted metallicities, and smaller colour excesses. These differences are reduced when matching the wavelength range and converging template grids. We further report that FIREFLY stellar masses are systematically lower by ~0.3 dex than masses from the MaNGA PCA and Pipe3D VACs, but match masses from the NSA best with only ~0.1-dex difference. Finally, we show that FIREFLY stellar ages correlate with spectral index age indicators H δ A and D n (4000), though with a clear additional metallicity dependence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Environmental and ecological signals predict food shortages for subtropical populations of Australian flying foxes, reservoirs of Hendra virus

Food availability determines where and how animals use space across a landscape and, therefore, affects the risk of encounters leading to zoonotic spillover. This relationship is evident in Australian flying foxes (Pteropus spp.; fruit bats), where acute food shortages precede clusters of Hendra virus spillovers. Using machine learning, we predicted months of food shortages from climatological and ecological covariates (1996–2022) in subtropical Australia. Overall accuracy in predicting months of low food availability on a test set from 2018 up to 2022 reached 93.33 and 92.59% based on climatological and bat-level features, respectively. Seasonality and the Oceanic El Niño Index were the most important environmental features, while the number of bats in rescue centres and their body weights were the most important bat-level features. These models support predictive signals up to nine months in advance, facilitating action to mitigate spillover risk.

Pteropus↗
DOE OSTI2015

Improving Wind Turbine Drivetrain Reliability Using a Combined Experimental, Computational, and Analytical Approach

Nontorque loads induced by the wind turbine rotor overhang weight and aerodynamic forces can greatly affect drivetrain loads and responses. If not addressed properly, these loads can result in a decrease in gearbox component life. This work uses analytical modeling, computational modeling, and experimental data to evaluate a unique drivetrain design that minimizes the effects of nontorque loads on gearbox reliability: the Pure Torque(R) drivetrain developed by Alstom. The drivetrain has a hub-support configuration that transmits nontorque loads directly into the tower rather than through the gearbox as in other design approaches. An analytical model of Alstom's Pure Torque drivetrain provides insight into the relationships among turbine component weights, aerodynamic forces, and the resulting drivetrain loads. Main shaft bending loads are orders of magnitude lower than the rated torque and are hardly affected by wind conditions and turbine operations.

17 WIND ENERGY↗
DOE OSTI2015

SU-E-T-547: Modeling Biological Response to Proton Irradiation and Evaluating Its Potential Clinical Consequences

Purpose: In addition to physical uncertainties, proton therapy may also be associated with biologic uncertainties. Currently a generic RBE value of 1.1 is used for treatment planning. In this work the effects of variable RBE, in comparison to a fixed RBE, were evaluated by calculating the effective dose for proton treatments. Methods: The repair misrepair fixation (RMF) model was used to calculate variable proton RBEs. The RBE weighted spread-out Bragg peak (SOBP) dose in water phantom was calculated using Monte Carlo simulation and compared to 1.1 weighted SOBP dose. A head and neck proton treatment was used to evaluate the potential effects, by comparing the head and neck treatment plan computed with a commercial treatment planning system that incorporates fixed RBE of 1.1 and a Monte Carlo treatment planning system that incorporates variable RBE. Results: RBE calculations along the depth of SOBP showed that the RBE at the entrance is approximately 1 and reaches 1.1 near the center of the SOBP. However, in distal regions the RBE rises to higher values (up to 3.5 depending on the cell type). Comparison of commercial treatment plans using a fixed RBE of 1.1 and Monte Carlo using variable RBE showed noticeable differences in the effective dose distributions. Conclusion: The comparison of the treatment planning with fixed and variable RBE shows that using commercial treatment planning systems that incorporate fixed RBE (1.1) could Result in overestimation of the effective dose to part of head and neck target volumes, while underestimating the effective dose to the normal tissue beyond the tumor. The accurate variable RBE as a function of proton beam energy in patient should be incorporated in treatment planning to improve the accuracy of effective dose calculation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Optical Properties of Polyethylene Filled with Bi{sub 2}Te{sub 3} Nanocrystallites

Composite mixtures with various component concentrations are fabricated by the thermal pressing of low-density polyethylene and Bi{sub 2}Te{sub 3} in the powder-like state with a nanocrystal size of ~50 nm. These samples preliminarily characterized by X-ray diffractometry and Raman spectroscopy are investigated by spectroscopic ellipsometry in a photon energy range of 1–6 eV. The dielectric functions for composites with a nominal Bi{sub 2}Te{sub 3} weight concentration of 5, 10, 30, 40, 50, 60, and 70% are calculated based on the measured optical constants of polyethylene and the Bi{sub 2}Te{sub 3} crystal in the approximation of the Bruggeman effective medium. The analysis of depolarization features of reflected light makes it possible to establish the actual inhomogeneity of the composite samples under study caused by the clusterization of nanocrystallites with an increase in the Bi{sub 2}Te{sub 3} weight concentration in polyethylene and propose the most reliable optical model of the formed composites.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Bayesian Segmentation (BCNN) v.1

Deep learning has been applied with great success to the segmentation of 3D X-Ray Computed Tomography (CT) scans. Establishing the credibility of these segmentations requires uncertainty quantification (UQ) to identify problem areas. Bayesian neural networks (BNNs), which use variational inference to learn the posterior distribution of the neural network weights, have been proposed to incorporate UQ into deep learning models. This software is an implementation of a novel 3D Bayesian convolutional neural network (BCNN) that provides accurate binary segmentations and uncertainty maps for 3D volumes. In particular, the uncertainty maps generated by this BCNN capture continuity and visual gradients, making them interpretable as confidence intervals for segmentation usable in numerical simulations. SAND2019-12927 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

LaBonte, TylerMatthew↗
DOE OSTI2023

pnnl/frequency_sensitivity

This research code base includes functions to compute statistics of image distributions in (spatial DFT) frequency space, train deep learning image classifiers on these distributions with variable depth and weight decay, and finally measure the sensitivity of the trained models to perturbations along (spatial DFT) frequency components.

Central, PNNL Developer↗
DOE OSTI2018

SAXS analysis of a soluble cytosolic NgBR construct including extracellular and transmembrane domains

The Nogo-B receptor (NgBR) is involved in oncogenic Ras signaling through directly binding to farnesylated Ras. It recruits farnesylated Ras to the non-lipid-raft membrane for interaction with downstream effectors. However, the cytosolic domain of NgBR itself is only partially folded. The lack of several conserved secondary structural elements makes this domain unlikely to form a complete farnesyl binding pocket. We find that inclusion of the extracellular and transmembrane domains that contain additional conserved residues to the cytosolic region results in a well folded protein with a similar size and shape to the E.coli cis-isoprenyl transferase (UPPs). Small Angle X-ray Scattering (SAXS) analysis reveals the radius of gyration (R g ) of our NgBR construct to be 18.2 Å with a maximum particle dimension (D max ) of 61.0 Å. Ab initio shape modeling returns a globular molecular envelope with an estimated molecular weight of 23.0 kD closely correlated with the calculated molecular weight. Both Kratky plot and pair distribution function of NgBR scattering reveal a bell shaped peak which is characteristic of a single globularly folded protein. In addition, circular dichroism (CD) analysis reveals that our construct has the secondary structure contents similar to the UPPs. However, this result does not agree with the currently accepted topological orientation of NgBR which might partition this construct into three separate domains. This discrepancy suggests another possible NgBR topology and lends insight into a potential molecular basis of how NgBR facilitates farnesylated Ras recruitment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Characteristics and Energy Use of Volume Servers in the United States

Servers’ field energy use remains poorly understood, given heterogeneous computing loads, configurable hardware and software, and operation over a wide range of management practices. This paper explores various characteristics of 1- and 2-socket volume servers that affect energy consumption, and quantifies the difference in power demand between higher-performing SPEC and ENERGY STAR servers and our best understanding of a typical server operating today. We first establish general characteristics of the U.S. installed base of volume servers from existing IDC data and the literature, before presenting information on server hardware configurations from data collection events at a major online retail website. We then compare cumulative distribution functions of server idle power across three separate datasets and explain the differences between them via examination of the hardware characteristics to which power draw is most sensitive. We find that idle server power demand is significantly higher than ENERGY STAR benchmarks and the industry-released energy use documented in SPEC, and that SPEC server configurations—and likely the associated power-scaling trends—are atypical of volume servers. Next, we examine recent trends in server power draw among high-performing servers across their full load range to consider how representative these trends are of all volume servers before inputting weighted average idle power load values into a recently published model of national server energy use. Finally, we present results from two surveys of IT managers (n=216) and IT vendors (n=178) that illustrate the prevalence of more-efficient equipment and operational practices in server rooms and closets; these findings highlight opportunities to improve the energy efficiency of the U.S. server stock.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Hubble Parameter Measurement Constraints on The Redshift of The Deceleration–Acceleration Transition, Dynamical Dark Energy, and Space Curvature

We compile an updated list of 38 measurements of the Hubble parameter $H(z)$ between redshifts 0.07 $≤ z ≤$ 2.36 and use them to place constraints on model parameters of constant and time-varying dark energy cosmological models, both spatially flat and curved. We use five models to measure the redshift of the cosmological deceleration–acceleration transition, $z_{da}$, from these H(z) data. Within the error bars, the measured zda are insensitive to the model used, depending only on the value assumed for the Hubble constant $H_0$. The weighted mean of our measurements is $z_{da}$ = 0.72 ± 0.05 (0.84 ± 0.03) for $H_0$ = 68 ± 2.8 (73.24 ± 1.74) km s -1 Mpc -1 and should provide a reasonably model-independent estimate of this cosmological parameter. The H(z) data are consistent with the standard spatially flat ΛCDM cosmological model but do not rule out nonflat models or dynamical dark energy models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

54 ENVIRONMENTAL SCIENCES↗