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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Petrology of the highlands massifs at Taurus-Littrow - An analysis of the 2-4 mm soil fraction

The petrography, phase chemistry, and Ar-40/Ar-39 ages of 2-4 mm fragments from the soils of both mare and highlands stations sampled by Apollo 17 are studied. It is found that the massifs consist of a complex stratigraphy of interlayered noritic breccias of varying metamorphic grades, melt rocks, and anorthositic rocks. A stratigraphic correlation of the lithologies within the North Massif and the South Massif is carried out. The petrogenesis of rock types with respect to thermal and impact history is discussed. The observed spinel cataclasites may represent relatively deep material sampled by the Serenitatis event.

Bence, A. E.↗
NASA NTRS2003

Drinking from the Fire Hose: Why the Flight Management System Can Be Hard to Train and Difficult to Use

The Flight Management Computer (FMC) and its interface, the Multi-function Control and Display Unit (MCDU) have been identified by researchers and airlines as difficult to train and use. Specifically, airline pilots have described the "drinking from the fire-hose" effect during training. Previous research has identified memorized action sequences as a major factor in a user s ability to learn and operate complex devices. This paper discusses the use of a method to examine the quantity of memorized action sequences required to perform a sample of 102 tasks, using features of the Boeing 777 Flight Management Computer Interface. The analysis identified a large number of memorized action sequences that must be learned during training and then recalled during line operations. Seventy-five percent of the tasks examined require recall of at least one memorized action sequence. Forty-five percent of the tasks require recall of a memorized action sequence and occur infrequently. The large number of memorized action sequences may provide an explanation for the difficulties in training and usage of the automation. Based on these findings, implications for training and the design of new user-interfaces are discussed.

Sherry, Lance↗
DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
NASA NTRS2009

Microfluidic Extraction of Biomarkers using Water as Solvent

A proposed device, denoted a miniature microfluidic biomarker extractor (mu-EX), would extract trace amounts of chemicals of interest from samples, such as soils and rocks. Traditionally, such extractions are performed on a large scale with hazardous organic solvents; each solvent capable of dissolving only those molecules lying within narrow ranges of specific chemical and physical characteristics that notably include volatility, electric charge, and polarity. In contrast, in the mu-EX, extractions could be performed by use of small amounts (typically between 0.1 and 100 L) of water as a universal solvent. As a rule of thumb, in order to enable solvation and extraction of molecules, it is necessary to use solvents that have polarity sufficiently close to the polarity of the target molecules. The mu-EX would make selection of specific organic solvents unnecessary, because mu-EX would exploit a unique property of liquid water: the possibility of tuning its polarity to match the polarity of organic solvents appropriate for extraction of molecules of interest. The change of the permittivity of water would be achieved by exploiting interactions between the translational states of water molecules and an imposed electromagnetic field in the frequency range of 300 to 600 GHz. On a molecular level, these interactions would result in disruption of the three-dimensional hydrogen-bonding network among liquid-water molecules and subsequent solvation and hydrolysis of target molecules. The mu-EX is expected to be an efficient means of hydrolyzing chemical bonds in complex macromolecules as well and, thus, enabling analysis of the building blocks of these complex chemical systems. The mu-EX device would include a microfluidic channel, part of which would lie within a waveguide coupled to an electronically tuned source of broad-band electromagnetic radiation in the frequency range from 300 to 600 GHz (see figure). The part of the microfluidic channel lying in the waveguide would constitute an interaction volume. The dimensions of the interaction volume would be chosen in accordance with the anticipated amount of solid sample material needed to ensure extraction of sufficient amount of target molecules for detection and analysis. By means that were not specified at the time of reporting the information for this article, the solid sample material would be placed in the interaction volume. Then the electromagnetic field would be imposed within the waveguide and water would be pumped through the interaction volume to effect the extraction.

Amashukeli, Xenia↗
NASA NTRS2013

Mars Science Laboratory CHIMRA/IC/DRT Flight Software for Sample Acquisition and Processing

The design methodologies of using sequence diagrams, multi-process functional flow diagrams, and hierarchical state machines were successfully applied in designing three MSL (Mars Science Laboratory) flight software modules responsible for handling actuator motions of the CHIMRA (Collection and Handling for In Situ Martian Rock Analysis), IC (Inlet Covers), and DRT (Dust Removal Tool) mechanisms. The methodologies were essential to specify complex interactions with other modules, support concurrent foreground and background motions, and handle various fault protections. Studying task scenarios with multi-process functional flow diagrams yielded great insight to overall design perspectives. Since the three modules require three different levels of background motion support, the methodologies presented in this paper provide an excellent comparison. All three modules are fully operational in flight.

sample processing↗
NASA NTRS2004

Application of Colorimetric Solid Phase Extraction (C-SPE) to Monitoring Nickel(II) and Lead(II) in Spacecraft Water Supplies

Archived water samples collected on the International Space Station (ISS) and returned to Earth for analysis have, in a few instances, contained trace levels of heavy metals. Building on our previous advances using Colorimetric Solid Phase Extraction (C-SPE) as a biocide monitoring technique, we are devising methods for the low level monitoring of nickel(II), lead(II) and other heavy metals. C-SPE is a sorption-spectrophotometric platform based on the extraction of analytes onto a membrane impregnated with a colorimetric reagent that are then quantified on the surface of the membrane using a diffuse reflectance spectrophotometer. Along these lines, we have determined nickel(II) via complexation with dimethylglyoxime (DMG) and begun to examine the analysis of lead(II) by its reaction with 2,5- dimercapto-1,3,4-thiadiazole (DMTD) and 4-(2- pyridylazo)-resorcinol (PAR). These developments are also extending a new variant of C-SPE in which immobilized reagents are being incorporated into this methodology in order to optimize sample reaction conditions and to introduce the colorimetric reagent. This paper describes the status of our development of these two new methods.

Diaz, Neil C.↗
DOE OSTI2024

Methods for Quantitative Thermal Analysis of Lithium Solid-State and Beyond Battery Safety

The use of differential scanning calorimetry (DSC) to measure the thermal behavior of individual components and electrolyte/electrode combinations is common. However, here we focus on DSC tests on an anode, cathode, and electrolyte (ACE) component combination over a temperature range that includes many of the phase transitions and key reactions (i.e., to 500 °C) that contribute to thermal runaway. This method can help quantify the complex reaction network in a full cell, thereby informing potential safety issues. Here, we used DSC heat flow data from a solid-state Li 0.43 CoO 2 +C+PVDF | LLZO | Li metal ACE sample and its components to quantify key factors affecting results. We focused on three areas: (1) ACE sample preparation and assembly in DSC pans, (2) DSC measurement parameters, and (3) heat flow analysis. Key points include the choice of component ratios (e.g., commercially relevant N:P capacity ratio), the importance of conductive carbon and binder, type of pan used, DSC ramp rate, and integration method used when dealing with broad and overlapping exothermic peaks. This work deepens the scientific basis and best practices for obtaining heat flow data from ACE samples for early-stage evaluation of solid-state and beyond battery safety.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Transient local secondary structure in the intrinsically disordered C‐term of the Albino3 insertase

Albino3 (Alb3) is an integral membrane protein fundamental to the targeting and insertion of light‐harvesting complex (LHC) proteins into the thylakoid membrane. Alb3 contains a stroma‐exposed C‐terminus (Alb3‐Cterm) that is responsible for binding the LHC‐loaded transit complex before LHC membrane insertion. Alb3‐Cterm has been reported to be intrinsically disordered, but precise mechanistic details underlying how it recognizes and binds to the transit complex are lacking, and the functional roles of its four different motifs have been debated. Using a novel combination of experimental and computational techniques such as single‐molecule fluorescence resonance energy transfer, circular dichroism with deconvolution analysis, site‐directed mutagenesis, trypsin digestion assays, and all‐atom molecular dynamics simulations in conjunction with enhanced sampling techniques, we show that Alb3‐Cterm contains transient secondary structure in motifs I and II. The excellent agreement between the experimental and computational data provides a quantitatively consistent picture and allows us to identify a heterogeneous structural ensemble that highlights the local and transient nature of the secondary structure. This structural ensemble was used to predict both the inter‐residue distance distributions of single molecules and the apparent unfolding free energy of the transient secondary structure, which were both in excellent agreement with those determined experimentally. We hypothesize that this transient local secondary structure may play an important role in the recognition of Alb3‐Cterm for the LHC‐loaded transit complex, and these results should provide a framework to better understand protein targeting by the Alb3‐Oxa1‐YidC family of insertases.

Okoto, Patience S.↗
DOE OSTI2023

Analysis of gas storage and transport in Eagle Ford shale using pressure pulse decay measurements with He, Kr and CO 2

Detailed characterization of gas transport and storage in low-permeability and organic-rich shales is associated with an array of challenges due to the complex morphology of the pore space, representing a broad range of pore sizes, combined with the heterogeneous fabric of the shale matrix. These factors, and their interplay during gas transport and sorption, complicate a) the analysis of shale samples at the laboratory scale (~ up to a ft. In length), and b) the prediction of natural gas production and carbon sequestration potential at larger scales. Here, in this work, tandem experiments with inert (helium – He) and adsorbing (krypton - Kr and carbon dioxide - CO 2 ) gases were performed and analyzed to develop an efficient workflow for characterizing and modeling transport and sorption at the laboratory scale. In particular, pressure pulse-decay (PPD) measurements were conducted on an Eagle Ford shale core sample at room temperature using He, Kr, and CO 2 . PPD measurements with He (a non-sorbing gas) were employed to probe the overall porosity, including natural fractures, microcracks, mesopores, and micropores. A triple-porosity model (TPM) was adopted to interpret the gas transport in the shale sample: The pore space is represented by three interacting continua, including macropores (larger fractures), mesopores (including microcracks), and micropores. To facilitate the application of the TPM, a modified analytical approach is introduced to extract effective transport parameters (in terms of the characteristic time for transport at relevant porosity levels) directly from the PPD measurements with He. To validate the modeling approach, model parameters extracted from two He PPD measurements are demonstrated to provide excellent agreement with a 3rd He PPD measurement performed at a higher pressure. The effective transport parameters, extracted from the He experiments, were subsequently converted for application to the Kr and CO 2 PPD experiments, by accounting for relevant transport modes and differences in fluid properties. Excess adsorption isotherms were extracted from the equilibrium pressures of the Kr and CO 2 PPD experiments using the He pore volume as a baseline. These adsorption isotherms were then integrated into the TPM to predict combined gas transport and sorption for Kr and CO 2 , with transport coefficients translated from the He measurements. The predictions for Kr and CO 2 are demonstrated to be in excellent agreement with the experimental observations. This, in turn, demonstrates that the proposed analytical approach provides for an effective characterization of mass transfer rates in shales, that can be applied directly in a TPM representation of mass transfer and sorption.

58 GEOSCIENCES↗
NASA NTRS1990

Oxidation characteristics of Ti-14Al-21Nb ingot alloy

Static oxidation kinetics of Ti14Al21Nb (wt pct) ingot alloy were studied in air over the temperature interval of 649 to 1093 C in a thermogravimetric apparatus. The oxidation products were characterized by x ray diffraction, electron microprobe analysis, energy dispersive x ray analysis, and Auger electron spectroscopy. Cross-sections of the oxidized samples were also examined using light and scanning electron microscopy. The oxidation rate was substantially lower than the conventional alloys of titanium, but the kinetics displayed a complex behavior involving two or more oxidation rates depending on the temperature and duration of exposure. The primary oxide formed was TiO2, but this oxide was doped with Nb. Small amounts of Al2O3 and TiN were also present in the scale. Diffusion of oxygen into the alloy was observed and the diffusivity seemed to be dependent on the microstructure of the metal. A model was presented to explain the oxidation behavior of the alloy in terms of the reduction in the oxygen diffusivity in the oxide caused by the modification of the defect structure of TiO2 by Nb ions.

Sankaran, Sankara N.↗
NASA NTRSDate not supplied

Carbon Limited Soils of Iceland: Analogs for Identifying Carbon-Mineral Interactions in Gale Crater, Mars Surface Materials

The Mars Science Laboratory’s Curiosity rover mission to Gale Crater, Mars has a goal to search for organic carbon and corresponding evidence of ancient microbiology preserved in clay mineral-rich sedimentary deposits [1]. No clear correlation of clay mineral (smectite) abundances with the Sample Analysis at Mars (SAM) carbon detections (up to 2000 ppm C) [2] has been established, suggesting other phases may preserve organic C in martian sediments. Terrestrial volcanic glass soils can accumulate C due to the C-complexing abilities of poorly crystalline Al- and Fe-oxyhydroxides [3]. The Xray amorphous component (20 to 73 wt. %) in Gale Crater materials [4,5] has received little consideration regarding organic C preservation in Gale Crater. The objectives of this work are to 1) Evaluate C content and level of soil development of basaltic glass-bearing Icelandic soils to determine the role that poorly crystalline phases have in complexing organic C in carbon limited soils and 2) Determine if evolved gas analysis (EGA) of well-characterized Mars analog basaltic materials from Iceland can be used to interpret SAM data to understand the nature of organic-C mineral/geochemical associations in Gale materials.

B. Sutter↗
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↗
NASA NTRS1987

Gas chromatographic instrumentation for the analysis of aerosols and gases in Titan's atmosphere

Instrumentation presently being developed by NASA for the collection and analysis of organic gases and aerosols in Titan's atmosphere is described together with the results of the preliminary experiments. For the aerosols, stepwise pyrolysis was shown to be a suitable method for preparing complex organic material for gas chromatography (GC), and a pyrolysis-gas chromatograph was developed and successfully used to analyze a simulated Titan aerosol. Atmospheric gases will be collected by a low-pressure gas sampling system using large-volume sample loops and analyzed by GC. The results of preliminary studies using a 20 cu cm sampling system and a very sensitive metastable ionization detector showed that hydrocarbon components at the 10 ppb level can be detected. Studies are in progress on shortening the overall analysis time by improving the pyrolysis system, the gas sampling system, and the associated gas chromatograph. Further development of the gas sampling system is planned to ensure rapid collection of samples adequate for analysis by GC over the entire range of pressures to be encountered during the probe's descent.

Scattergood, T. W.↗
NASA NTRS2000

Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data

A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.

Bose, Tamal↗
DOE OSTI2019

Parameter inference with deep jointly informed neural networks

Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.

Humbird, Kelli D.↗