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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantitative Analysis of The High‐Yield Hydrolysis of Kelp by Laminarinase and Alginate Lyase

Abstract Kelp is an abundant, farmable biomass‐containing laminarin and alginate as major polysaccharides, providing an excellent model substrate to study their deconstruction by simple enzyme mixtures. Our previous study showed strong reactivity of the glycoside hydrolase family 55 during hydrolysis of purified laminarin, raising the question of its reactivity with intact kelp. In this study, we determined that a combination of a single glycoside hydrolase family 55 β‐1,3‐exoglucanase with a broad‐specificity alginate lyase from the polysaccharide lyase family 18 gives efficient hydrolysis of untreated kelp to a mixture of simple sugars, that is, glucose, gentiobiose, mannitol‐end glucose, and mannuronic and guluronic acids and their soluble oligomers. Quantitative assignments from nanostructure initiator mass spectrometry (NIMS) and 2D HSQC NMR spectroscopy and analysis of the reaction time‐course are provided. The data suggest that binary combinations of enzymes targeted to the unique polysaccharide composition of marine biomass are sufficient to deconstruct kelp into soluble sugars for microbial fermentation.

2D HSQC NMR↗
DOE OSTI2018

Quantitative Analysis of Damping Enhancement and Piezoelectric Effect Mechanism of CNTs/PMN/EP Composites

New types of piezoelectric damping materials, including carbon nanotubes (CNTs)/lead magnesium niobate (PMN)/epoxy (EP) resin, are developed. The tan δ area (TA) analysis method is selected to evaluate the damping properties which obviously clarifies the effect of maximum loss factor (tan δ ) and effective temperature range on damping properties. Furthermore, the dominant factor of damping enhancement is quantitatively analyzed via the value of TA. Compared with PMN, the interfacial friction of CNTs acts as the dominant factor for the content less than 0.6 wt.%. The maximum damping percentage of CNTs reaches 29.14%. CNTs form loop circuits gradually with the content of CNTs increasing, and electrical energy generated via piezoelectric effect of PMN is efficiently dissipated through the conductive network. Thus, PMN becomes the dominant factor as the content of CNTs exceeds 0.8 wt.%, and the damping percentage reaches 47.43% at the content of CNTs of 1.0 wt.%.

Pan, Shiyue (ORCID:0000000257419000)↗
DOE OSTI2024

Separation of Lipoproteins for Quantitative Analysis of 14 C-Labeled Lipid-Soluble Compounds by Accelerator Mass Spectrometry

To date, 14 C tracer studies using accelerator mass spectrometry (AMS) have not yet resolved lipid-soluble analytes into individual lipoprotein density subclasses. The objective of this work was to develop a reliable method for lipoprotein separation and quantitative recovery for biokinetic modeling purposes. The novel method developed provides the means for use of small volumes (10–200 µL) of frozen plasma as a starting material for continuous isopycnic lipoprotein separation within a carbon- and pH-stable analyte matrix, which, following post-separation fraction clean up, created samples suitable for highly accurate 14 C/ 12 C isotope ratio determinations by AMS. Manual aspiration achieved 99.2 ± 0.41% recovery of [5- 14 CH 3 ]-(2R, 4'R, 8'R)-α-tocopherol contained within 25 µL plasma recovered in triacylglycerol rich lipoproteins (TRL = Chylomicrons + VLDL), LDL, HDL, and infranatant (INF) from each of 10 different sampling times for one male and one female subject, n = 20 total samples. Small sample volumes of previously frozen plasma and high analyte recoveries make this an attractive method for AMS studies using newer, smaller footprint AMS equipment to develop genuine tracer analyses of lipophilic nutrients or compounds in all human age ranges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Absolute Band Intensity of the Iodine Monochloride Fundamental Mode for Infrared Sensing and Quantitative Analysis

Iodine monochloride (ICl) is a gaseous off-product of molten salt reactors; monitoring this heteronuclear diatomic is of great interest for both environmental and safety purposes. In this paper we investigate the possibility of infrared monitoring of ICl by measuring the far-infrared absorption cross section of its fundamental band near 381 cm -1 . We have performed quantitative studies of the neat gas in a 20 cm cell at 25, 35, 50 and 70 oC at multiple pressures up to ~ 9 Torr and investigated the temperature and pressure dependence of the band’s infrared cross section. Quantitative measurements were problematic due to sample adhesion to the cell walls and windows as well as reactions/possible hydrolysis of ICl to form HCl gas. Effects were mitigated by measuring only the neat gas, using short measurement times and subtracting out the partial pressure of the HCl(g). The integrated band strength is shown to be temperature independent and was found to be equal to 9.1 x 10 -19 (cm 2 /molecule) cm-1. As expected, the temperature dependence of the band profile showed only a small effect over this limited temperature range. Furthermore, we have also investigated using the absorption data along with inverse least squares multivariate methods for the quantitative monitoring of ICl effluent concentrations under different scenarios using infrared (standoff) sensing and compare these results with traditional Beer’s Law (univariate) techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Quantitative analysis of time-resolved microwave conductivity data

Flash-photolysis time-resolved microwave conductivity (fp-TRMC) is a versatile, highly sensitive technique for studying the complex photoconductivity of solution, solid, and gas-phase samples. The purpose of this paper is to provide a standard reference work for experimentalists interested in using microwave conductivity methods to study functional electronic materials, describing how to conduct and calibrate these experiments in order to obtain quantitative results. The main focus of the paper is on calculating the calibration factor, K, which is used to connect the measured change in microwave power absorption to the conductance of the sample. We describe the standard analytical formulae that have been used in the past, and compare them to numerical simulations. This comparison shows that the most widely used analytical analysis of fp-TRMC data systematically under-estimates the transient conductivity by ~60%. We suggest a more accurate semi-empirical way of calibrating these experiments. However, we emphasize that the full numerical calculation is necessary to quantify both transient and steady-state conductance for arbitrary sample properties and geometry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1995

A field- and laboratory-based quantitative analysis of alluvium: Relating analytical results to TIMS data

Thermal Infrared Multispectral Scanner (TIMS) data were acquired over the McDowell Mountains northeast of Scottsdale, Arizona during August 1994. The raw data were processed to emphasize lithologic differences using a decorrelation stretch and assigning bands 5, 3, and 1 to red, green, and blue, respectively. Processed data of alluvium flanking the mountains exhibit moderate color variation. The objective of this study was to determine, using a quantitative approach, what environmental variable(s), in the absence of bedrock, is/are responsible for influencing the spectral properties of the desert alluvial surface.

Wenrich, Melissa L.↗
DOE OSTI2016

Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples

Global proteomics approaches allow characterization of whole tissue lysates to an impressive depth. However, it is now increasingly recognized that to better understand the complexity of multicellular organisms, global protein profiling of specific spatially defined regions/substructures of tissues (i.e. spatially-resolved proteomics) is essential. Laser capture microdissection (LCM) enables microscopic isolation of defined regions of tissues preserving crucial spatial information. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, and that impact measurement robustness, quantification, and throughput. Here, we coupled LCM with a fully automated sample preparation workflow that with a single manual step allows: protein extraction, tryptic digestion, peptide cleanup and LC-MS/MS analysis of proteomes from microdissected tissues. Benchmarking against the current state of the art in ultrasensitive global proteomic analysis, our approach demonstrated significant improvements in quantification and throughput. Using our LCM-SNaPP proteomics approach, we characterized to a depth of more than 3,400 proteins, the ontogeny of protein changes during normal lung development in laser capture microdissected alveolar tissue containing ~4,000 cells per sample. Importantly, the data revealed quantitative changes for 350 low abundance transcription factors and signaling molecules, confirming earlier transcript-level observations and defining seven modules of coordinated transcription factor/signaling molecule expression patterns, suggesting that a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes. Furthermore, our LCM-proteomics approach facilitates efficient, spatially-resolved, ultrasensitive global proteomics analyses in high-throughput that will be enabling for several clinical and biological applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Quantitative Analysis of Electrochemical and Electrode Stability with Low Self-Discharge Lithium-Sulfur Batteries

The viability of employing high-capacity sulfur cathodes in building high-energy-density lithium-sulfur batteries is limited by rapid self-discharge, short shelf life, and severe structural degradation during cell resting (static instability). Unfortunately, the static instability has largely been ignored in the literature. Here, we present in this letter a long-term self-discharge study by quantitatively analyzing the control lithium-sulfur batteries with a conventional cathode configuration, which provides meaningful insights into the cathode failure mechanisms during resting. Utilizing the understanding obtained with the control cells, we design and present low self-discharge (LSD) lithium-sulfur batteries for investigating the long-term self-discharge effect and electrode stability.

25 ENERGY STORAGE↗
DOE OSTI2022

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

We report x-ray absorption near edge structure (XANES) is a powerful tool to probe the fingerprint of local structures, and when coupled with X-ray microscopy, the small spot size enables one to probe very specific regions of interest in a device or material, e.g. interfaces/bulk, different grains, good/bad electrical areas. In this work, we investigate the use of linear combination fitting (LCF) of XANES spectra for the particular case of Cu doping in CdTe. We show that the experimental data seem to be accurately represented by standards of Cu 2 Te and its substoichiometric counterpart, Cu 1.43 Te. We use Cu in CdTe as a case study to evaluate the accuracy of linear combination fitting using simulated standards, given that experimental standards for certain phases (e.g. Cu 1.43 Te) or defect structures (e.g. vacancies) cannot be readily obtained. We discuss how spectral features of the FEFF9-simulated standards, fitting ranges, and noise levels all dictate the accuracy of this type of analysis. We show that the greater the spectral difference between the two standards, the better the LCF is able to differentiate between the two structures and to tolerate experimental noise. Finally, we estimate the error of the fitted weights for different spectral features and noise levels and propose a framework to study local structures semi-quantitatively by using binary mixtures of FEFF9-simulated standards.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 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 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↗
NASA NTRS1994

A quantitative analysis of IRAS maps of molecular clouds

We present an analysis of IRAS maps of five molecular clouds: Orion, Ophiuchus, Perseus, Taurus, and Lupus. For the classification and description of these astrophysical maps, we use a newly developed technique which considers all maps of a given type to be elements of a pseudometric space. For each physical characteristic of interest, this formal system assigns a distance function (a pseudometric) to the space of all maps: this procedure allows us to measure quantitatively the difference between any two maps and to order the space of all maps. We thus obtain a quantitative classification scheme for molecular clouds. In this present study we use the IRAS continuum maps at 100 and 60 micrometer(s) to produce column density (or optical depth) maps for the five molecular cloud regions given above. For this sample of clouds, we compute the 'output' functions which measure the distribution of density, the distribution of topological components, the self-gravity, and the filamentary nature of the clouds. The results of this work provide a quantitative description of the structure in these molecular cloud regions. We then order the clouds according to the overall environmental 'complexity' of these star-forming regions. Finally, we compare our results with the observed populations of young stellar objects in these clouds and discuss the possible environmental effects on the star-formation process. Our results are consistent with the recently stated conjecture that more massive stars tend to form in more 'complex' environments.

Wiseman, Jennifer J.↗
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 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↗
NASA NTRS2016

Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

CAR↗
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↗