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Results for “sample complexity analysis”

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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Multi‐omics of the Soil Microbiome

TheArctic is characterized by vast amounts of carbon stored in permafrost and isan important focal point for the study of climate change as increasing temperature may accelerate microbially‐mediated release of carbon stored inpermafrost into the atmosphere as CO 2 and CH 4 . Yet surprisingly, very little is known about the vulnerability of permafrost andresponse of microorganisms in the permafrost to their changing environment. To address these knowledge gaps we used omics technologiesto explore permafrost microbial communities, to reveal hitherto unknown soilmicrobial populations and to link them to specific soil biogeochemical processes. For these projects we employed an omics pipeline that includes metagenomics, metatranscriptomics and metaproteomics to determine the phylogenetic and functional gene compositions and their expression in complex, non‐sterile soil samples collected from the field at several locations in Alaska. These studies have presented several challenges due to the unprecedented large size of the datasets and the insufficiency of available bioinformatic tools & databases to analyze, correlate and integrate thedata. To address these challenges we developed a novel functional gene databaseand used different assembly and analysis algorithms to determine key featuresin the datasets. The data reveal the microbial diversity, composition andfunctional potential in permafrost and how the microbial communities respond toclimate change impacts. Support or Funding Information Microbiomes in Transition, LDRD, Pacific Northwest National Laboratories, Richland, Washington

Jansson, Janet K.↗
DOE OSTI2016

A Next Generation Digital Counting System For Low-Level Tritium Studies (Project Report)

Since the early seventies, SRNL has pioneered low-level tritium analysis using various nuclear counting technologies and techniques. Since 1999, SRNL has successfully performed routine low-level tritium analyses with counting systems based on digital signal processor (DSP) modules developed in the late 1990s. Each of these counting systems are complex, unique to SRNL, and fully dedicated to performing routine tritium analyses of low-level environmental samples. It is time to modernize these systems due to a variety of issues including (1) age, (2) lack of direct replacement electronics modules and (3) advances in digital signal processing and computer technology. There has been considerable development in many areas associated with the enterprise of performing low-level tritium analyses. The objective of this LDRD project was to design, build, and demonstrate a Next Generation Tritium Counting System (NGTCS), while not disrupting the routine low-level tritium analyses underway in the facility on the legacy counting systems. The work involved (1) developing a test bed for building and testing new counting system hardware that does not interfere with our routine analyses, (2) testing a new counting system based on a modern state of the art DSP module, and (3) evolving the low-level tritium counter design to reflect the state of the science.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

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

A Handheld Medical Diagnostic Device for Harsh Environments

Medical diagnostic capabilities for extreme environments, are currently limited in nature to single marker type assays. We propose to develop a handheld device for medical diagnostics in extreme environments that provide sensitive and specific detection of biomarkers with an emphasis initially on radiation exposure. Such a device will have multiple uses, and will be easily adaptable to many applications. For example, first responders, the battlefield, in low resource settings in developing countries facing emerging disease outbreaks, and during long-duration space flights. To this end, we will develop highly adaptable microfluidic devices (cards) for profiling different biomarkers with an emphasis on those related to radiation exposure. We will leverage the previous centrifugal separator work from LLNL and Sandia National Laboratory. The radiation exposure biomarker work at LLNL will provide a biological platform for demonstrating the utility of the device. We will develop a fluidic system using a pump-less mechanical method (centrifugal) to move, control, order and layer fluids in the platform. This decreases complexity, while enabling simultaneous cell separation and plasma generation for two uses: the first is differential blood cell counts and the second is biomarker assay panels. This work will be accomplished in three Aims: Aim 1) Test multiplex microfluidic cards and integrated reader. Aim 2) Demonstrate system capabilities to take and process blood samples for multi-endpoint analysis. Aim 3) Test multiplex detection based on measuring differential blood cell counts, while measuring plasma protein biomarkers. The proposed work addresses the mission research challenge of chemical and biological countermeasure in that our work aims to provide new research capabilities to respond to man-made or natural outbreaks of pandemic diseases in particular in low resource and triage environments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The microbiologist's guide to metaproteomics

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

bioinformatics↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Standardized naming of microbiome samples in Genomes OnLine Database

The power of next-generation sequencing has resulted in an explosive growth in the number of projects aiming to understand the metagenomic diversity of complex microbial environments. The interdisciplinary nature of this microbiome research community, along with the absence of reporting standards for microbiome data and samples, poses a significant challenge for follow-up studies. Commonly used names of metagenomes and metatranscriptomes in public databases currently lack the essential information necessary to accurately describe and classify the underlying samples, which makes a comparative analysis difficult to conduct and often results in misclassified sequences in data repositories. The Genomes OnLine Database (GOLD) (https:// gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute has been at the forefront of addressing this challenge by developing a standardized nomenclature system for naming microbiome samples. GOLD, currently in its twenty-fifth anniversary, continues to enrich the research community with hundreds of thousands of metagenomes and metatranscriptomes with well-curated and easy-to-understand names. Through this manuscript, we describe the overall naming process that can be easily adopted by researchers worldwide. Additionally, we propose the use of this naming system as a best practice for the scientific community to facilitate better interoperability and reusability of microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error. After correcting the wavelength and intensity of the field data, SpecQuant displays the calculated mixing ratio versus the experimental data for each analyte along with the residual spectrum with any or all analyte fits subtracted for visual inspection of the fit and residuals. The software performance for multianalyte quantification was demonstrated using moderate resolution (0.5 cm –1 ) infrared spectra that were collected during the time-resolved infrared photolysis of methyl iodide.

Thompson, Christopher J.↗
DOE OSTI2021

A Simple, Cost-Effective, and Automation-Friendly Direct PCR Approach for Bacterial Community Analysis

Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples.

16S rRNA sequencing↗
DOE OSTI2017

Parameter estimation in a thermoelastic composite problem via adjoint formulation and model reduction

Advances in nondestructive material characterization are providing a wealth of information that could be exploited to gain insight into general aspects of material performance and, in particular, discover relationships between microstructure and thermo-mechanical properties in polycrystalline and other complex composite materials. Here, in order to facilitate the integration of such measurements into existing models, as well as inform new physics-based predictions, we developed a C++/MPI computational framework for sensitivity analysis and parameter estimation. The framework utilizes a micro-mechanical modeling based on fast Fourier transforms, direct and adjoint formulations, and Markov chain Monte Carlo sampling techniques. We illustrate the characteristics of this framework and demonstrate its utility by computing the residual stresses arising from thermal expansion of an elastic composite and using data from simulated experiments. Lastly, we show that the availability of nondestructive 3-D measurements is crucial to reduce the uncertainty in predictions, emphasizing the importance of an integrated experimental/modeling/data analysis approach for improved material characterization and design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Influence of Ga doping on magnetic properties, magnetocaloric effect, and electronic structure of pseudobinary GdZn 1 - x Ga x ( x = 0 - 0.1 )

Here, we explore the impact of introducing IIIA-group element Ga in place of IIB-group element Zn in binary intermetallic GdZn on its magnetic and magnetocaloric properties, as well as explicate the modified electronic band structure of the compound. The magnetic transition temperature of the compound decreases with the increase of Ga concentration in GdZn 1-x ⁢Ga x ⁢(x = 0–0.1) while the crystal structure (CsCl prototype) and lattice parameters remain unchanged. Our detailed analysis of magnetization and magnetocaloric data conclusively proves that long-ranged magnetic ordering exists in the sample, despite the magnetic interaction considerably weakening with the increase of Ga. The experimental data are rationalized using both a theoretical machine-learning model and first-principle density functional theory. The electronic band structure of GdZn is manifested with some unusual complex features which gradually diminish with Ga doping and the conventional sinusoidal feature of Ruderman-Kittel-Kasuya-Yosida-type interactions also disappears. A mean-field theory model is developed and can successfully describe the overall magnetocaloric behavior of the GdZn 1-x⁢ Ga x series of samples.

36 MATERIALS SCIENCE↗
DOE OSTI2023

BuildStockQuery [SWR-23-58]

BuildStockQuery is a python library designed to simplify and streamline the process of querying massive, terabyte-scale datasets generated by ResStock(TM). ResStock (SWR-19-15) is a U.S. DOE-supported, NREL-built, national residential building energy stock model that enables a new approach to large-scale residential energy analysis across the U.S. by combining large public and private data sources, statistical sampling, detailed sub-hourly building simulations, and high-performance computing. BuildStockQuery offers an intuitive Object-Oriented Programming (OOP) interface to the ResStock output dataset allowing users to easily perform common queries and receive results in familiar pandas DataFrame format, abstracting away the need for complex SQL query. By initializing a query object with the pertinent Athena database and table names, users can easily query for various kinds of insights, for example, timeseries electricity for an end use for a given state grouped by building types.

Adhikari, Rajendra↗
DOE OSTI2017

Method for Hot Real-Time Analysis of Pyrolysis Vapors at Pilot Scale

Pyrolysis oils contain more than 400 compounds, up to 60% of which do not re-volatilize for subsequent chemical analysis. Vapor chemical composition is also complicated as additional condensation reactions occur during quenching and collection of the product. Due to the complexity of the pyrolysis oil, and a desire to catalytically upgrade the vapor composition before condensation, online real-time analytical techniques such as Molecular Beam Mass Spectrometry (MBMS) are of great use. However, in order to properly sample hot pyrolysis vapors at the pilot scale, many challenges must be overcome.

09 BIOMASS FUELS↗
DOE OSTI2020

Proposed Analytical Methods for Determining Filter Media Properties

High Efficiency Particulate Air (HEPA) filters, commonly used in nuclear filtration applications, are an integral part of the waste management processes in nuclear plants. HEPA filters are 99.97% efficient filtration devices characterized by their high resistance to air flow, or pressure drop. From theoretical models, the initial pressure drop across a clean filter is proven to be a function of the filter media fiber diameters and porosity of the media. These filter properties are relatively difficult to obtain with traditional manual methods; therefore, there is a need to develop analytical methods to find the fiber diameter and porosity to determine the pressure drop. Typically, these values can be found by substituting a calculated representative value based upon measured values, such as equivalent fiber diameter based upon a clean pressure drop. However, these values may also be directly recorded and measured by incorporating Scanning Electron Microscope (SEM) image analysis and other measurement methods to analyze the filter media and determine its physical properties without the need for media testing. DiameterJ, an open source Java plug-in, used with ImageJ, can process images of filter media taken by an SEM to find statistical data such as mean fiber diameter and porosity. To produce the raw data, SEM images of two filter media type samples are taken and segmented in DiameterJ using the traditional and statistical region merging segmentation algorithms. Manual segmentation is necessary after the initial segmentation by the algorithms as the images tend to be too complex for the algorithms to output with the necessary accuracy. However, complications exist in the manual segmentation process as these methods can be time intensive and prone to the individual bias of the user. This in turn can skew the final mean fiber diameter result, and lead to either an over or under prediction of the pressure drop. It was also discovered that the porosity data produced by DiameterJ is inaccurate, as the SEM analyzes the three-dimensional filter media by projecting its geometry onto a plane and analyzing it as a two-dimensional binary image. Thus, the porosity is artificially inflated through the segmentation process, rendering this result incorrect. Alternatively, density determination, gravimetric analysis, and thickness testing of the filter media is collectively used to determine the filter fiber porosity. Together, the SEM image analysis and analytical lab methods produce results through direct measurements which allow for the prediction of the initial pressure drop from clean filter media without the need for prior media testing to collect pressure data. By improving upon this proposed analytical method in the future, there is potential to streamline the process of finding these filter properties into a more direct methodology for determining the pressure drop across HEPA filters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Digital Rock Physics and 3D Printing for Fractured Porous Media

Imaging techniques for the analysis of porous structures have revolutionized our ability to quantitatively characterize geomaterials. Digital representations of rock from CT images and physics modeling based on these pore structures provide the opportunity to further advance our quantitative understanding of fluid flow, geomechanics, and geochemistry, and the emergence of coupled behaviors. Additive manufacturing, commonly known as 3D printing, has revolutionized production of custom parts with complex internal geometries. For the geosciences, recent advances in 3D printing technology may be co-opted to print reproducible porous structures derived from CT-imaging of actual rocks for experimental testing. The use of 3D printed microstructure allows us to surmount typical problems associated with sample-to-sample heterogeneity that plague rock physics testing and to test material response independent from pore-structure variability. Together, imaging, digital rocks and 3D printing potentially enables a new workflow for understanding coupled geophysical processes in a real, but well-defined setting circumventing typical issues associated with reproducibility, enabling full characterization and thus connection of physical phenomena to structure. Here we report on our research exploring the possibilities that these technologies can bring to geosciences for coupled multiscale experimental and numerical analysis using 3D printed fractured rock specimens.

42 ENGINEERING↗
DOE OSTI2017

Improved molecular level identification of organic compounds using comprehensive two-dimensional chromatography, dual ionization energies and high resolution mass spectrometry

A new analytical methodology combining comprehensive two-dimensional gas chromatography (GC×GC), dual ionization energies and high resolution time of flight mass spectrometry has been developed that improves molecular level identification of organic compounds in complex environmental samples. GC×GC maximizes compound separation providing cleaner mass spectra by minimizing erroneous fragments from interferences and co-eluting peaks. Traditional electron ionization (EI, 70 eV) provides MS fragmentation patterns that can be matched to published EI MS libraries while vacuum ultraviolet photoionization (VUV, 10.5 eV) yields MS with reduced fragmentation enhancing the abundance of the molecular ion providing molecular formulas when combined with high resolution mass spectrometry. We demonstrate this new approach by applying it to a sample of organic aerosol. In this sample, 238 peaks were matched to EI MS library data with FM ≥ 800 but a fifth (42 compounds) were determined to be incorrectly identified because the molecular formula was not confirmed by the VUV MS data. This highlights the importance of using a complementary technique to confirm compound identifications even for peaks with very good matching statistics. In total, 171 compounds were identified by EI MS matching to library spectra with confirmation of the molecular formula from the high resolution VUV MS data and were not dependent on the matching statistics being above a threshold value. A large number of unidentified peaks were still observed with FM < 800, which in routine analysis would typically be neglected. Where possible, these peaks were assigned molecular formulas from the VUV MS data (211 in total). In total, the combination of EI and VUV MS data provides more than twice as much molecular level peak information than traditional approaches and improves confidence in the identification of individual organic compounds. The molecular formula data from the VUV MS data was used, in conjunction with GC×GC retention times and the observed EI MS, to generate a new, searchable EI MS library compatible with the standard NIST MS search program. This library is deliberately dynamic and editable so that other end users can add new entries and update existing entries as new information becomes available.A new analytical methodology has been developed to improve molecular level identification of organic compounds in complex samples.

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

Advancing Elemental and Isotopic Analysis of Uranium Mineral Inclusions: Rapid Screening via Laser-Induced Breakdown Spectroscopy and High-Resolution Laser Ablation-ICP-MS Mapping

The work presented herein employs two laser-based analytical techniques (laser-induced breakdown spectroscopy (LIBS) and laser ablation – inductively coupled plasma – mass spectrometry (LA-ICP-MS)) to spatially determine the elemental and isotopic composition of uranium bearing minerals. Uniquely, this work leverages the high-speed applicability of LIBS to “screen” the sample(s) for their elemental constituents. After determining the location of the uranium inclusions (via LIBS), high-resolution LA-ICP-MS was employed to further characterize the inclusions. The high-resolution (sub-µm) capabilities of LA-ICP-MS were able to extract important information from the uranium minerals including discerning its chemical form (e.g., finchite from carnotite, Sr- and K-bearing uranyl vanadates, respectively) as well as their 235 U/ 238 U isotopic composition. This approach, LIBS followed by LA-ICP-MS, significantly reduces the analysis time (~95 %) in comparison to employing a LA-ICP-MS only approach. Furthermore, this work presented a novel approach to analyzing inclusions via a particle/inclusion analysis tool which is commercially available within the iolite 4 software. This tool allowed for a more accurate characterization of the isotopic distribution of the inclusions, as well as allowing for rapid sizing of the inclusions. Finally, this analytical approach could readily be applied to other sample types in which the target species (e.g., µm-sized inclusions) are embedded in complex matrices (e.g., cm-sized samples).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗