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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

IsoMatchMS : Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra

Due to its speed, accuracy, and adaptability to various sample types, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a popular method to identify molecular isotope profiles from biological samples. Often MALDI-MS data do not include tandem MS fragmentation data, and thus the identification of compounds in samples requires external databases so that the accurate mass of detected signals can be matched to known molecular compounds. Most relevant MALDI-MS software tools developed to confirm compound identifications are focused on small molecules (e.g., metabolites, lipids) and cannot be easily adapted to protein data due to their more complex isotopic distributions. Here, we present an R package called IsoMatchMS for the automated annotation of MALDI-MS data for multiple datatypes: intact proteins, peptides, and glycans. This tool accepts already derived molecular formulas or, for proteomics applications, can derive molecular formulas from a list of input peptides or proteins including proteins with post-translational modifications. In conclusion, visualization of all matched isotopic profiles is provided in a highly accessible HTML format called a trelliscope display, which allows users to filter and sort by several parameters such as match scores and the number of peaks matched. IsoMatchMS simplifies the annotation and visualization of MALDI-MS data for downstream analyses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operators that are locally translation-invariant, even if these operators are high rank or full rank. Such operators arise in Schur complement methods for solving partial differential equations (PDEs), as Hessians in PDE-constrained optimization and inverse problems, as integral operators, as covariance operators, and as Dirichlet-to-Neumann maps. Constructing the approximation requires computing the impulse responses of the operator to point sources centered on nodes in an adaptively refined grid of sample points. A randomized a posteriori error estimator drives the adaptivity. Once constructed, the approximation can be efficiently applied to vectors using the fast Fourier transform. The approximation can be efficiently converted to hierarchical matrix (H-matrix) format, then inverted or factorized using scalable H-matrix arithmetic. The quality of the approximation degrades gracefully as fewer sample points are used, allowing cheap lower quality approximations to be used as preconditioners. Furthermore, this yields an automated method to construct preconditioners for locally translation-invariant Schur complements. We directly address issues related to boundaries and prove that our scheme eliminates boundary artifacts. We test the scheme on a spatially varying blurring kernel, on the nonlocal component of an interface Schur complement for the Poisson operator, and on the data misfit Hessian for an advection dominated advection-diffusion inverse problem. Numerical results show that the scheme outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Quantitative infrared spectroscopy of environmentally sensitive and rough materials

Fourier transform infrared (FTIR) spectroscopy is a widely used characterization technique for studying chemical compositions and lightmatter interactions in the infrared range. However, it remains challenging to use conventional FTIR equipment to characterize materials that are environmentally sensitive and/or have rough surfaces due to their dynamic response to external stimuli (e.g., humidity) and light scattering from the sample surface. Here, we describe an approach enabling quantitative infrared spectroscopy measurements of these challenging samples, including transmittance, reflectance, and emissivity. We designed and 3D-printed a miniaturized environmental chamber that can be directly inserted into the sample holder of a conventional integrating sphere to perform total transmission and reflection measurements in controlled environments. Moreover, a calibration method was developed to exclude light scattering from rough surfaces of the sample and the distortion created by infrared windows. To illustrate the potential application of this method, we have performed quantitative infrared measurements, both specular and diffusive, on a sapphire standard reference with a specular surface, as well as a textile sample that has a rough surface and is dynamically responsive to changes in humidity. The calibrated results measured with the equipped chamber match those measured without the IR windows, demonstrating the viability of our environmental chamber and the proposed calibration method. This quantitative infrared spectroscopy measurement technique may help advance fields such as metasurfaces and adaptive textiles, where samples are often rough, dynamically responsive, and/or environmentally sensitive.

3D printing↗
DOE OSTI2016

Portable ultrahigh-vacuum sample storage system for polarization-dependent total-reflection fluorescence x-ray absorption fine structure spectroscopy

A portable ultrahigh-vacuum sample storage system was designed and built to investigate the detailed geometric structures of mass-selected metal clusters on oxide substrates by polarization-dependent total-reflection fluorescence x-ray absorption fine structure spectroscopy (PTRF-XAFS). This ultrahigh-vacuum (UHV) sample storage system provides the handover of samples between two different sample manipulating systems. The sample storage system is adaptable for public transportation, facilitating experiments using air-sensitive samples in synchrotron radiation or other quantum beam facilities. The samples were transferred by the developed portable UHV transfer system via a public transportation at a distance over 400 km. The performance of the transfer system was demonstrated by a successful PTRF-XAFS study of Pt{sub 4} clusters deposited on a TiO{sub 2}(110) surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

A Novel Method for Phasor Measurement Unit Sampling Time Error Compensation

This paper focuses on a novel method for successive approximation register analog to digital converter control in synchronized phasor measurement units (PMUs). To compensate for the sampling time error caused by the division remainder between the desirable sampling rate and the oscillator frequency, a variable sampling interval control method is presented by interlacing two integers under a proposed criterion. The frequency of the onboard oscillator is monitored in real-time using the pulse per second (PPS) timing reference from global positioning system. The sampling control is adaptively adjusted each second according to the latest estimated oscillator frequency. The “saw tooth” in phasor angle error and DC offset and spikes in frequency error can be effectively eliminated by applying the proposed method. The simulation and experiment results validate the effectiveness and accuracy of the proposed method, which is believed to be able to greatly improve the performance of PPS-disciplined synchronous sampling methods in PMUs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

National Virtual Biotechnology Laboratory: Report on Rapid R&D Solutions to the COVID-19 Crisis

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply short ages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. Within months, NVBL teams produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment (PPE), creating nearly 1,000 new jobs. They used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and U.S. Department of Defense (DoD) efforts to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. NVBL teams also studied how to control indoor virus movement to minimize uptake and protect human health. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development (R&D) solutions. As the fight against COVID continues, sustained efforts are needed to confront this pandemic as well as future threats. Examples include: 1) Establishing “supply chains on demand” to meet emergency production needs by leveraging the materials and manufacturing expertise of DOE national laboratories and developing advances in electronics, sensing, robotics, and automation capabilities; 2) Improving the speed and robustness of drug discovery by integrating experimental platforms with DOE’s computational and experimental user facilities, which provide unique resources to support the discovery of high-potential therapeutic agents; 3) Protecting public, environmental, and animal health by developing new testing protocols and instrumentation adaptable to diverse sample types (both physiological and environmental) to quickly detect a wide range of pathogens and monitor other biorisks; 4) Supporting near-real-time data needs of decision-makers at the local, regional, state, and national levels by advancing data curation, analysis, and modeling using artificial intelligence and new data science tools for managing and evaluating large diverse datasets; 5) Harnessing DOE’s expertise in environmental modeling to design rooms and air handling for offices, classrooms, restaurants, and other structures to minimize biorisk transmissions. Going forward, NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, the Office of Science will continue to be an integral component of agency wide efforts to prepare for and respond to biorisks and other crises.

42 ENGINEERING↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2019

Bayesian modeling of inconsistent plastic response due to material variability

The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. To account for material response variability through variations in physical parameters, we adapt a recent Bayesian embedded modeling error calibration technique. We use Bayesian model selection to determine the most plausible of a variety of plasticity models and the optimal embedding of parameter variability. To expedite model selection, we develop an adaptive importance-sampling-based numerical integration scheme to compute the Bayesian model evidence. In conclusion, we demonstrate that the new framework provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Accelerated weight histogram method for exploring free energy landscapes

Calculating free energies is an important and notoriously difficult task for molecular simulations. The rapid increase in computational power has made it possible to probe increasingly complex systems, yet extracting accurate free energies from these simulations remains a major challenge. Fully exploring the free energy landscape of, say, a biological macromolecule typically requires sampling large conformational changes and slow transitions. Often, the only feasible way to study such a system is to simulate it using an enhanced sampling method. The accelerated weight histogram (AWH) method is a new, efficient extended ensemble sampling technique which adaptively biases the simulation to promote exploration of the free energy landscape. The AWH method uses a probability weight histogram which allows for efficient free energy updates and results in an easy discretization procedure. A major advantage of the method is its general formulation, making it a powerful platform for developing further extensions and analyzing its relation to already existing methods. Here, we demonstrate its efficiency and general applicability by calculating the potential of mean force along a reaction coordinate for both a single dimension and multiple dimensions. We make use of a non-uniform, free energy dependent target distribution in reaction coordinate space so that computational efforts are not wasted on physically irrelevant regions. We present numerical results for molecular dynamics simulations of lithium acetate in solution and chignolin, a 10-residue long peptide that folds into a β-hairpin. We further present practical guidelines for setting up and running an AWH simulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach

Public transit is a vital mode of transportation in urban areas, and its efficiency is crucial for the daily commute of millions of people. To improve the reliability and predictability of transit systems, researchers have developed separate single-task learning models to predict the occupancy and delay of buses at the stop or route level. However, these models provide a narrow view of delay and occupancy at each stop and do not account for the correlation between the two. We propose a novel approach that leverages broader generalizable patterns governing delay and occupancy for improved prediction. We introduce a multitask learning toolchain that takes into account General Transit Feed Specification feeds, Automatic Passenger Counter data, and contextual temporal and spatial information. The toolchain predicts transit delay and occupancy at the stop level, improving the accuracy of the predictions of these two features of a trip given sparse and noisy data. We also show that our toolchain can adapt to fewer samples of new transit data once it has been trained on previous routes/trips as compared to state-of-the-art methods. Finally, we use actual data from Chattanooga, Tennessee, to validate our approach. We compare our approach against the state-of-the-art methods and we show that treating occupancy and delay as related problems improves the accuracy of the predictions. We show that our approach improves delay prediction significantly by as much as 4% in F1 scores while producing equivalent or better results for occupancy.

Zulqarnain, Ammar Bin↗
DOE OSTI2019

Optical Detection of Phase-Resolved Ferromagnetic Resonance in Epitaxial FeCo Thin Films

We report phased-resolved ferromagnetic resonance (FMR) measurements in an epitaxial Fe50Co50 thin film using a heterodyne, optical detection method. We track the evolution of the precessional phase across the saturated and unsaturated regimes along the FeCo hard axis. For the two regimes, opposite phase evolutions with frequency are observed, which coincide with the negative effective field dependence on the biasing field for the unsaturated modes. In addition, a nonzero phase advance between 0.2 and 0.4 rad is found for the saturated with respect to the unsaturated modes, which indicates an incomplete spatial overlap of the two modes in the film caused by surface pinning effect. The optical method has the advantage of resolving both the spatial and phase information in FMR measurements, which will be closely relevant and adaptable to epitaxial samples with multiple anisotropy axes, and for both flip-chip-based continuous films and spin-torque-based nano-devices.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Quantification of UV-Visible and Laser Spectroscopic Techniques for Materials Accountability and Process Control

Ultraviolet–visible spectroscopy (UV–Visible) and time-resolved laser fluorescence spectroscopy (TRLFS) optical techniques can permit on-line analysis of actinide elements in a solvent extraction process in real time. These techniques have been used for measuring actinide speciation and concentration under laboratory conditions and are easily adaptable to multiple sampling geometries, such as dip probes, fiber-optic sample cells, and flow-through cell geometries. To fully exploit these techniques, researchers must determine the fundamental speciation of target actinides and the resulting influence on spectroscopic properties. Detection limits, process conditions, and speciation of key actinide components can be established and utilized in a range of areas, particularly those related to materials accountability and process control. Through this project, researchers will develop tools and spectroscopic techniques to evaluate solution extraction conditions and concentrations of U, Pu, and Cm in extraction processes, addressing areas of process control and materials accountability. The team will evaluate UV– Visible and TRLFS for use in solvent extraction-based separations. Ongoing research is examining efficacy of UV-Visible spectroscopy to evaluate uranium and plutonium speciation under conditions found in the UREX process and using TRLFS to evaluate Cm speciation and concentration in the TALSPEAK process. A uranyl and plutonium nitrate UV–Visible spectroscopy study met with success, which supports the utility and continued exploration of spectroscopic methods for evaluation of actinide concentrations and solution conditions for other aspects of the UREX+ solvent extraction scheme. This project will examine U and Pu absorbance in TRUEX and TALSPEAK, perform detailed examination of Cm in TRUEX and TALSPEAK, study U laser fluorescence, and apply project data to contactors. The team will also determine peak ratios as a function of solution concentrations for the UV-Visible spectroscopy studies. The use of TRLFS to examine Cm and U will provide data to evaluate lifetime, peak location, and peak ratios (mainly for U). The bases for the spectroscopic techniques have been investigated, providing fundamental evidence for the application’s utility.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Resin Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Reversible oxidative modifications on protein thiols have recently emerged as important mediators of cellular function. Herein we describe the detailed procedure of a quantitative redox proteomics method that utilizes resin-assisted capture (RAC) in combination with tandem mass tag (TMT) isobaric labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to allow multiplexed stochiometric quantification of oxidized protein thiols at the proteome level. The site-specific quantitative information on oxidized cysteine residues provides additional insight into the functional impacts of such modifications. The workflow is adaptable across many sample types including cultured cells (e.g. mammalian, prokaryotic) and whole tissues (e.g. heart, lung, muscle) which are initially lysed/homogenized with free thiols being alkylated to prevent artificial oxidation. The oxidized protein thiols are then reduced and captured by a thiol affinity resin which streamlines and simplifies the workflow steps by allowing the proceeding digestion, labeling, and washing procedures to be performed without additional transfer of proteins/peptides. Finally, labeled peptides are eluted and analyzed by LC-MS/MS to reveal comprehensive stoichiometric changes related to thiol oxidation across the entire proteome. This method greatly improves our understanding of the role of redox dependent regulation under physiological and pathophysiological states related to protein thiol oxidation.

RAC, TMT, thiol redox proteomics, cysteine, PTM st↗
DOE OSTI2016

Investigation of high‐level waste glass melting using X‐ray computed tomography

Abstract A method has been developed to quantitatively assess the melting behavior of simulated nuclear waste glasses in a 5‐cm‐diameter stainless steel beaker heated from the bottom. The method applies X‐ray scanning and computed tomography to build three‐dimensional volumetric data of a heat‐treated sample and performs an adaptive segmentation analysis of the volumetric data to identify morphologically distinct regions in the sample matrix and quantify the amount of material in each region based on computed tomography density. The method was applied to two different series of simulated high‐level waste glass melter feeds, and the results showed that it provides detailed images of samples at various stages of melting, including distribution of gas bubbles of varying sizes within the sample matrix, as well as a quantitative measure of how fast various waste/frit feeds melted relative to each other. The results show that the melting rate is influenced by the rate of calcine gas evolution, melt viscosity, and the presence of modifier ions in the feed.

Choi, Alexander S.↗
DOE OSTI2021

Interpreting Write Performance of Supercomputer I/O Systems with Regression Models

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10× on some samples for both of the target systems.

Xie, Bing↗
DOE OSTI2013

The Role of Viral Population Diversity in Adaptation of Bovine Coronavirus to New Host Environments

The high mutation rate of RNA viruses enables a diverse genetic population of viral genotypes to exist within a single infected host. In-host genetic diversity could better position the virus population to respond and adapt to a diverse array of selective pressures such as host-switching events. Multiple new coronaviruses, including SARS, have been identified in human samples just within the last ten years, demonstrating the potential of coronaviruses as emergent human pathogens. Deep sequencing was used to characterize genomic changes in coronavirus quasispecies during simulated host-switching. Three bovine nasal samples infected with bovine coronavirus were used to infect human and bovine macrophage and lung cell lines. The virus reproduced relatively well in macrophages, but the lung cell lines were not infected efficiently enough to allow passage of non lab-adapted samples. Approximately 12 kb of the genome was amplified before and after passage and sequenced at average coverages of nearly 9506(454 sequencing) and 38,0006(Illumina). The consensus sequence of many of the passaged samples had a 12 nucleotide insert in the consensus sequence of the spike gene, and multiple point mutations were associated with the presence of the insert. Deep sequencing revealed that the insert was present but very rare in the unpassaged samples and could quickly shift to dominate the population when placed in a different environment. The insert coded for three arginine residues, occurred in a region associated with fusion entry into host cells, and may allow infection of new cell types via heparin sulfate binding. Analysis of the deep sequencing data indicated that two distinct genotypes circulated at different frequency levels in each sample, and support the hypothesis that the mutations present in passaged strains were ‘‘selected’’ from a pre-existing pool rather than through de novo mutation and subsequent population fixation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for simultaneous metabolite, protein, and lipid extraction (MPLEx) from the exact same soil sample was created by adapting a solvent-based approach. This MPLEx protocol has proven to be simple yet robust for many sample types and even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Preparing Lamellae from Vitreous Biological Samples using a Dual-Beam Scanning Electron Microscope for Cryo-Electron Tomography

Presented here is a protocol for preparing cryo-lamellae from plunge-frozen grids of Plasmodium falciparum-infected human erythrocytes, which could easily be adapted for other biological samples. The basic principles for preparing samples, milling, and viewing lamellae are common to all instruments and the protocol can be followed as a general guide to on-grid cryo-lamella preparation for cryo-electron microscopy (cryoEM) and cryo-electron tomography (cryoET). Electron microscopy grids supporting the cells are plunge-frozen into liquid nitrogen-cooled liquid ethane using a manual or automated plunge freezer, then screened on a light microscope equipped with a cryo-stage. Frozen grids are transferred into a cryo-scanning electron microscope equipped with a focused ion beam (cryoFIB-SEM). Grids are routinely sputter coated prior to milling, which aids dispersal of charge build-up during milling. Alternatively, an e-beam rotary coater can be used to apply a layer of carbon-platinum to the grids, the exact thickness of which can be more precisely controlled. Once inside the cryoFIB-SEM an additional coating of an organoplatinum compound is applied to the surface of the grid via a gas injection system (GIS). This layer protects the front edge of the lamella as it is milled, the integrity of which is critical for achieving uniformly thin lamellae. Regions of interest are identified via SEM and milling is carried out in a step-wise fashion, reducing the current of the ion beam as the lamella reaches electron transparency, in order to avoid excessive heat generation. A grid with multiple lamellae is then transferred to a transmission electron microscope (TEM) under cryogenic conditions for tilt-series acquisition. A robust and contamination-free workflow for lamella preparation is an essential step for downstream techniques, including cellular cryoEM, cryoET, and sub-tomogram averaging. Development of these techniques, especially for lift-out and milling of high-pressure frozen samples, is of high-priority in the field.

59 BASIC BIOLOGICAL SCIENCES↗