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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantitative Analysis of the Effects of Natural and Human Factors on a Hydrological System in Zhangweinan Canal Basin

Drought is a water deficit state caused by large-scale climate change that cannot be avoided by a water resource management system. Water scarcity refers to the unsustainable utilization of water resources over a long time, which is the result of water management policies. However, water deficits caused by drought and water scarcity often occur simultaneously and are indistinguishable. This study proposes a model-based simulation framework that can quantitatively distinguish natural factors (drought) from human factors (water scarcity) in a hydrological system. The simulation was applied to the Zhangweinan Canal Basin, based on the runoff sequences from 1950 to 2004. The results show that the runoff curve number, soil depth, soil available water, soil evaporation compensation coefficient, base runoff α coefficient and the maximum canopy interception have the highest sensitivity to runoff, and that the calibrated and validated SWAT model can effectively simulate the runoff process in the Zhangweinan Canal Basin and similar areas. Abrupt changes in human activities in 1975 and water scarcity led to the disappearance of the summer peak runoff period in both wet and dry years. Human factors are the main reason for the change in the hydrological system in the study area; the runoff loss caused by human factors is four times that caused by natural factors according to the proposed variable threshold. This study proposes a model-based simulation framework that can help water resource managers to distinguish the effects of drought and water scarcity in water-stressed areas and adjust management accordingly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Quantitative analysis of doped/undoped ZnO nanomaterials using laser assisted atom probe tomography: Influence of the analysis parameters

In the last decade, atom probe tomography has become a powerful tool to investigate semiconductor and insulator nanomaterials in microelectronics, spintronics, and optoelectronics. In this paper, we report an investigation of zinc oxide nanostructures using atom probe tomography. We observed that the chemical composition of zinc oxide is strongly dependent on the analysis parameters used for atom probe experiments. It was observed that at high laser pulse energies, the electric field at the specimen surface is strongly dependent on the crystallographic directions. This dependence leads to an inhomogeneous field evaporation of the surface atoms, resulting in unreliable measurements. We show that the laser pulse energy has to be well tuned to obtain reliable quantitative chemical composition measurements of undoped and doped ZnO nanomaterials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Application of voltammetry for quantitative analysis of chromium in molten 2LiF-BeF 2 (FLiBe) salt

The electrochemical behavior of Cr 2+ in molten 2LiF-BeF 2 (FLiBe) salt at temperatures between 500 °C and 700 °C was studied using cyclic voltammetry (CV) and linear sweep voltammetry (LSV). LSV has been used to establish a stable reference electrode with the Be/Be 2+ couple. The ability to distinguish between common corrosion products, such as Cr2+ and Fe 2+ , with CV has been demonstrated using the Be/Be 2+ reference reaction. Methods for high-precision, in-situ quantitative measurement of Cr 2+ concentration are developed. Models for predicting the concentration of Cr 2+ and its diffusion coefficient are approximated by relating faradaic cathodic peak current density to Cr 2+ concentration at 600 °C and 650 °C. Here this study provides the basis for the development of a sensor for on-line quantification of corrosion products that improves understanding of materials performance in the molten FLiBe salt.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

TRACKING CORONAL FEATURES FROM THE LOW CORONA TO EARTH: A QUANTITATIVE ANALYSIS OF THE 2008 DECEMBER 12 CORONAL MASS EJECTION

We have tracked a slow magnetic cloud associated coronal mass ejection (CME) continuously from its origin as a flux rope structure in the low solar corona over a four-day passage to impact with spacecraft located near Earth. Combining measurements from the STEREO, ACE, and Wind space missions, we are able to follow major elements with enough specificity to relate pre-CME coronal structure in the low corona to the corresponding elements seen in the near-Earth in situ data. Combining extreme ultraviolet imaging, quantitative Thomson scattering data throughout the flight of the CME, and ''ground-truth'' in situ measurements, we: (1) identify the plasma observed by ACE and Wind with specific features in the solar corona (a segment of a long flux rope); (2) determine the onset mechanism of the CME (destabilization of a filament channel following flare reconnection, coupled with the mass draining instability) and demonstrate that it is consistent with the in situ measurements; (3) identify the origin of different layers of the sheath material around the central magnetic cloud (closed field lifted from the base of the corona, closed field entrained during passage through the corona, and solar wind entrained by the front of the CME); (4) measure mass accretion of the system via snowplow effects in the solar wind as the CME crossed the solar system; and (5) quantify the kinetic energy budget of the system in interplanetary space, and determine that it is consistent with no long-term driving force on the CME.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Color as a tool for quantitative analysis of heterogeneous polymer degradation

Polymers are widely used industry, but degrade due to environmental conditions, such as elevated temperature or radiation. The degradation of polymers is dominated oxidation in oxygenated environments. Such oxidation is commonly referred to as thermal-oxidation or photo-oxidation and in many situations is heterogeneous through thickness of the polymer. While heterogeneous oxidation of polymers is a common phenomenon, the effect on the lifetime of polymers is not well understood as methods to evaluate such oxidation are time consuming and challenging to implement. Here we show a novel method for quantitative evaluation of heterogeneous oxidation in polymers using a fast and accurate color approach. The proposed color approach was observed to improve the efficiency of obtaining heterogenous oxidation profiles in half the time of common approaches, such as indention or Fourier transform infrared spectroscopy. Furthermore, the method was found to produce trends similar to that observed using common approaches and the applicability of the method was verified by mapping oxygen concentration through thickness using energy dispersive X-ray spectroscopy. Lastly, the utility of the proposed method was demonstrated through the generation of an oxidation design space, which is envisioned to assist with designing experiments towards estimation of polymer lifetime. Finally, we anticipate this work to be a starting point for further evaluation of heterogeneous oxidation in polymers using optical properties.

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

A standardized quantitative analysis strategy for stable isotope probing metagenomics

ABSTRACT Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg , an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, “who is eating what?” and “who is active?” within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism’s taxonomic identity and genome composition while providing quantitative estimates of the microorganism’s isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Quantitative Analysis of Human Salivary Gland-Derived Intact Proteome Using Top-Down Mass Spectrometry

There are several notable challenges inherent to fully characterizing the entirety of the human saliva proteome using bottom-up approaches, including polymorphic isoforms, post-translational modifications, unique splice variants, deletions, and truncations. To address these challenges, we have developed a top-down based liquid chromatography-mass spectrometry (LC-MS) approach, which cataloged 20 major human salivary proteins with a total of 83 proteoforms, containing a broad range of post-translational modifications. Among these proteins, several previously reported disease biomarker proteins were identified at the intact protein level, such as beta-2 microglobulin (B2M). In addition, intact glycosylated proteoforms of several saliva proteins were also characterized, including intact N-glycosylated protein prolactin inducible protein (PIP) and O-glycosylated acidic protein rich protein (aPRP). These characterized proteoforms constitute an intact saliva proteoform database, which was used for quantitative comparison of intact salivary proteoforms among six healthy individuals. Human parotid (PS) and submandibular/sublingual gland (SMSL) secretion samples (2 μg of protein each) from six healthy individuals were compared using RPLC coupled with the 12T FTICR mass spectrometer. Significantly different protein and PTM patterns were resolved with high reproducibility between PS and SMSL glands. The results from this study provide further insight into the potential mechanisms of PTM pathways in oral glandular secretion, expanding our knowledge of this complex yet easily accessible fluid. Intact protein LC-MS approach presented herein can potentially be applied for rapid and accurate identification of biomarkers from only a few microliters of human glandular saliva.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2016

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Quantitative Analysis of Ultralow-Density Materials Using Laboratory-Based Quasi-Monochromatic Radiography

The measurement of the density of materials, especially ultralow-density foams, is difficult in that the measurement must be precise and localizable. The density of the material is often governed by its cellular (i.e., porous) structure, and many techniques exist to create that structure. Often, the cellular structure can vary from one location within the material to another, and when at low densities (i.e., densities lower than ~500 mg/cm 3 ), it can vary due to shrinkage during syneresis, collapse under the weight of gravity, or gas/water vapor uptake. Quantifying this variation is important for a variety of applications, especially when used in plasma physics targets. Knowing the density and its variation across the sample is critical for experimental results to be accurately predicted by physics calculations and for modeling the results of the physics targets. The use of quasi-monochromatic radiography provides a means to image the two-dimensional (2-D) distribution of density variation within silica aerogel materials and to quantitatively measure that variation from sample to sample and lot to lot. For this work, two batches of silica aerogels with targeted densities of ~20 mg/cm 3 were created, one batch at Lawrence Livermore National Laboratory, and the other batch at Los Alamos National Laboratory. Outlined here is a quasi-monochromatic radiography system using various X-ray sources coupled to a doubly curved crystal optic and X-ray charge-coupled device camera to image and characterize these materials. It was found that measuring the density both gravimetrically and using quasi-monochromatic radiography were statistically identical, although the two batches were found to be slightly higher than their targeted density due to shrinkage. The radiography system also provided 2-D information as to the aerogel quality, i.e., presence of voids, chipped material, or inclusions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Multiplexed Quantitative Analysis of Germline Single Amino Acid Variants by Targeted Proteomics in Nondepleted Human Plasma

Single amino acid variants (SAAVs) in protein sequences are often a direct result of single-nucleotide polymorphisms (SNPs). Certain germline SAAVs have shown biological relevance in different disease conditions but lack precise quantification in circulation, which could hinder functional investigations and progress in biomarker development. Here, we have developed a multiplexed liquid chromatography-selected reaction monitoring (LC-SRM) assay that monitors 5 wild-type and variant peptide pairs (Complement Factor B: CFB-R32Q/R32W, Clusterin: CLU-N317H, Fetuin B: FETUB-K360R, and Kininogen: KNG1-L212P) in nondepleted human plasma. The assay was optimized for imprecision, linearity, stability, and calibration assessments with CVs of under 20%. The wild-type and variant peptide pairs were characterized in a set of healthy individual plasma samples. These target identifications were also validated by SNP genotyping with more than 99% accuracy. For all protein targets, we observed significantly lower concentrations of WT species in the presence variant peptides. In CFB, the concentration of R32Q was significantly lower than its counterpart R32W variant and WT species. Furthermore, our results distinguished phenotypes of homozygosity and heterozygosity of the SAAV presence through direct concentration level characterization. These findings provide some insights into how SAAVs affect quantitative assessments of target peptides. The assay demonstrates a platform for proteogenomic analyses with potential applications in both research and clinical settings.

genetics↗
DOE OSTI2015

Quantitative analysis of breast echotexture patterns in automated breast ultrasound images

Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors’ database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Quantitative analysis of chemical interaction and doping of the Si(111) native oxide surface with tetrafluorotetracyanoquinodimethane

The charge-transfer states and the carrier concentration of the native oxide Si(111) surface adsorbed with 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F{sub 4}-TCNQ) were investigated by X-ray photoelectron spectroscopy (XPS) and independently driven four-probe electrical conductivity measurements. The XPS results show that F{sub 4}-TCNQ molecules are involved in charge transfer with the SiO{sub 2}/Si(111) surface. The Si 2p XPS spectra and the surface photovoltage shift provide the evidences of (i) change in the oxidation states at the SiO{sub 2}-Si(111) interface region and (ii) formation of a p-type space charge layer (SCL) with a hole concentration of 1.7 × 10{sup 10} cm{sup −2}, respectively. The four-probe I–V measurements also support the formation of the p-type SCL, and the estimated hole concentration of 2.0 × 10{sup 10} cm{sup −2} agrees well with the XPS results. The estimated SCL hole concentrations were much smaller than the excess charge density in the F{sub 4}-TCNQ layer, of the order of 10{sup 13} cm{sup −2}, suggesting that most of charges were localized as the oxidation states at the SiO{sub 2}-Si(111) interface region. The present quantitative methods ensure precise determination of the doping concentration near the surface region.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Healthcare Needs of the State: Quantitative Analysis

Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Quantitative Analysis of HAADF–STEM Images of MoVTeTaO M1 Phase Catalyst for Propane Ammoxidation to Acrylonitrile

Abstract High‐angle annular dark‐field scanning transmission electron microscopy (HAADF–STEM) image simulations were performed to determine accurate metal distributions in the MoVTeTaO M1 phase catalyst for propane ammoxidation. QSTEM simulation, based on a multislice method with the frozen phonon approximation, was chosen because of the excellent agreement between experimental and simulated HAADF–STEM images. This simulation method successfully provided accurate metal distributions in the MoVTeTaO M1 phase and indicated significantly lower V occupancies of the linking sites 1, 2, 3, 4, and 7, and higher Ta occupancy in site 9 than the previously reported metal occupancies. This novel methodology for accurate HAADF–STEM image analysis is a valuable tool for accurate analysis of local chemical compositions in a wide range of ordered catalytic materials.

Woo, Jungwon↗
DOE OSTI2017

Quantitative Analysis of Drugs with Highly Different Concentrations of Pharmaceutical Components Using Spectral Subtraction Techniques

Two simple spectrophotometric methods were developed for determination of empagliflozin and metformin by manipulating their ratio spectra with application on a recently approved pharmaceutical combination, Synjardy® tablets. A spiking technique was used to increase the concentration of empagliflozin after extraction from the tablets to allow its simultaneous determination with metformin. Validation parameters according to ICH guidelines were acceptable over the concentration range of 2–12 μg/mL for both drugs using constant multiplication and spectrum subtraction methods. The optimized methods are suitable for QC labs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Quantitative analysis of Raman spectral parameters for carbon fibers: practical considerations and connection to mechanical properties

Although the literature on the Raman spectra of carbon fibers is vast, no consistent, robust predictive relationship between mechanical properties of carbon fibers and spectral parameters exists. This shortcoming is due to the use of numerous fitting functions to evaluate Raman spectra of carbon fibers and the inconsistencies in establishing the best fitting models in a statistically robust fashion. To address this gap, we present a comprehensive work on the Raman spectra of carbon fibers that combines a vast library of experimental data with a robust numerical analysis and a statistical evaluation of a wide range of suggested fitting models. This manuscript begins with a brief review of the commonly applied fitting models. Then, the Raman spectra of 32 commercially available polyacrylonitrile-based carbon fibers collected at excitation wavelengths 532, 633, and 785 nm are presented and the best fit for all fibers is evaluated based on several statistical criteria in conjunction with numerical calculations and physical arguments. The results suggest that high-performance fibers must be fit with at least five peaks, whereas high-tensile modulus fibers are best fit with at least six distinct peaks. Finally, we employ simultaneous fitting of the Raman spectra of specific fibers and wavelengths and demonstrate that strong correlations exist between mechanical properties and the D1 peak position and shape across the range of evaluated mechanical properties. Finally, we suggest straightforward improvements in fitting analysis procedures that can be implemented to increase coherency in the understanding of the underlying carbon fiber microstructure intuited from Raman spectroscopy.

36 MATERIALS SCIENCE↗