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DOE OSTI2022

ADAPTIVE GROUP TESTING WITH MISMATCHED MODELS

Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups, instead of testing each individual in the population, can be more efficient. In this work, we consider noisy adaptive group testing design with specific test sensitivity and specificity that select the optimal group given previous test results based on pre-selected utility function. As in prior studies on group testing, we model this problem as a sequential Bayesian Optimal Experimental Design (BOED) to adaptively design the groups for each test. We analyze the required number of group tests when using the updated posterior on the infection status and the corresponding Mutual Information (MI) as our utility function for selecting new groups. More importantly, we study how the potential bias on the ground-truth noise of group tests may affect the group testing sample complexity.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1982

Synoptic studies of chromospheric variability in F - K dwarfs with the IUE

Time-sequential series of IUE spectra for ten F, G and K dwarfs were obtained in 1980 and 1981 to study the rotational dependence of chromospheric flux in the ultraviolet. An interactive computational method using unbiased estimators was developed to measure emission line fluxes free of arbitrary judgement concerning the behavior of the underlying spectrum and shapes of the line profiles. Due to the limited number of observational samples per star, we have used special techniques to analyze the sparsely and anharmonically sampled emission line flux data. Two different autocorrelation measures were computed for each emission line as a function of temporal frequency. Examples and results of this analysis now in progress are given for several stars.

Hallam, K. L.↗

NASA NTRS2014

Geostationary Coastal and Air Pollution Events (GeoCAPE) Filter Radiometer (FR)

The GeoCAPE Filter Radiometer (FR) Study is a different instrument type than all of the previous IDL GeoCape studies. The customer primary goals are to keep mass, volume and cost to a minimum while meeting the science objectives and maximizing flight opportunities by fitting on the largest number of GEO accommodations possible. Minimize total mission costs by riding on a commercial GEO satellite. For this instrument type, the coverage rate, km 2 min, was significantly increased while reducing the nadir ground sample size to 250m. This was accomplished by analyzing a large 2d area for each integration period. The field of view will be imaged on a 4k x 4k detector array of 15 micrometer pixels. Each ground pixel is spread over 2 x 2 detector pixels so the instantaneous field of view (IFOV) is 2048 X 2048 ground pixels. The baseline is, for each field of view 50 sequential snapshot images are taken, each with a different filter, before indexing the scan mirror to the next IFOV. A delta would be to add additional filters.

Geo Cape↗

DOE OSTI2025

Aspects of propagator sparsening in lattice QCD

In lattice field theory, field sparsening aims to replace quantum fields, or objects constructed from them, with approximations that preserve the appropriate symmetries and maintain many aspects of the physics that the fields determine. For example, an effective sparsening of a quark propagator provides an efficient map from a quark propagator on a fine lattice geometry to a quark propagator defined on a coarser geometry in order to reduce storage and computational costs of subsequent calculational stages while maintaining long-distance correlations and corresponding low-energy physical information. Previous studies have focused on decimating lattice sites or randomly sampling lattice sites to reduce the size of the propagator and subsequent costs of Wick contractions. Here, we extend the study of sparsening to incorporate covariant averaging of spatial sites and examine the effects on two-point and three-point correlation functions involving various hadrons. We find that sparsening is most effective in reproducing the unsparsened versions of these correlation functions when weighted covariant-averaging is sequentially applied many times.

Lattice QCD↗

DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2005

Ultrasonic Micro-Blades for the Rapid Extraction of Impact Tracks from Aerogel

The science return of NASA's Stardust Mission with its valuable cargo of cometary debris hinges on the ability to efficiently extract particles from silica aerogel collectors. The current method for extracting cosmic dust impact tracks is a mature procedure involving sequential perforation of the aerogel with glass needles on computer controlled micromanipulators. This method is highly successful at removing well-defined aerogel fragments of reasonable optical clarity while causing minimal damage to the surrounding aerogel collector tile. Such a system will be adopted by the JSC Astromaterials Curation Facility in anticipation of Stardust s arrival in early 2006. In addition to Stardust, aerogel is a possible collector for future sample return missions and is used for capture of hypervelocity ejecta in high power laser experiments of interest to LLNL. Researchers will be eager to obtain Stardust samples for study as quickly as possible, and rapid extraction tools requiring little construction, training, or investment would be an attractive asset. To this end, we have experimented with micro-blades for the Stardust impact track extraction process. Our ultimate goal is a rapid extraction system in a clean electron beam environment, such as an SEM or dual-beam FIB, for in situ sample preparation, mounting and analysis.

Ishii, H. A.↗

DOE OSTI2023

Synergistic Coupling Effect of Electronic Conductivity and Interphase Compatibility on High-Voltage Na 3 V 2 (PO 4 ) 2 F 3 Cathodes

Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) has been considered an up-and-coming cathode material candidate for sodium (Na) ion batteries in light of its high specific capacity and working voltage. However, an erratic cathode/electrolyte interface layer is inevitably formed, accompanied by continuous electrolyte decomposition on the NVPF surface, when the voltage exceeds 4.2 V vs Na + /Na. Herein, the interphase features of NVPF are obviously enhanced owing to the ameliorated electronic conductivity obtained by combining it with carbon nanotubes (CNT). The NVPF with 3 wt % CNT (NVPF@3% CNT) reduces the Na + diffusion kinetic energy barrier and electron transport resistance. Furthermore, the conducting network formed by CNT with sturdy structure strength can promptly accommodate the volumetric changes during sequential Na + extraction/insertion and thus effectively improve the long-term cyclic performance of NVPF/hard carbon full cells. The initial discharge capacity approaches 105 mA h g –1 at 0.5C, and it retains 94% capacity retention after 200 cycles at the temperature of –10 °C. The cathode/electrolyte interphase characterization results further demonstrate that the interphase layer on the NVPF@3% CNT cathode is thinner and more compact compared with pristine samples. Here, this research provides a competitive strategy to facilitate the interfacial compatibility between the NVPF and electrolytes and accelerate the commercialization of high-performance Na-ion batteries.

25 ENERGY STORAGE↗

NASA NTRS1978

Variable-temperature cryogenic trap for the separation of gas mixtures

The paper describes a continuous variable-temperature U-shaped cold trap which can both purify vacuum-line combustion products for subsequent stable isotopic analysis and isolate the methane and ethane constituents of natural gases. The canister containing the trap is submerged in liquid nitrogen, and, as the gas cools, the gas mixture components condense sequentially according to their relative vapor pressures. After the about 12 min required for the bottom of the trap to reach the liquid-nitrogen temperature, passage of electric current through the resistance wire wrapped around the tubing covering the U-trap permits distillation of successive gas components at optimal temperatures. Data on the separation achieved for two mixtures, the first being typical vacuum-line combustion products of geochemical samples such as rocks and the second being natural gas, are presented, and the thermal behavior and power consumption are reported.

Des Marais, D. J.↗

DOE OSTI2023

Structure and thermodynamics of water adsorption in NU-1500-Cr

Abstract Metal-organic frameworks (MOFs) are a class of materials with diverse chemical and structural properties, and have been shown to effectively adsorb various types of guest molecules. The mechanism of water adsorption in NU-1500-Cr, a high-performance atmospheric water harvesting MOF, is investigated using a combination of molecular dynamics simulations and infrared spectroscopy. Calculations of thermodynamic and dynamical properties of water as a function of relative humidity allow for following the adsorption process from the initial hydration stage to complete filling of the MOF pores. Initial hydration begins at the water molecules that saturate the open Cr 3+ sites of the framework, which is then followed by the formation of water chains that extend along the channels connecting the hexagonal pores of the framework. Water present in these channels gradually coalesces and fills the hexagonal pores sequentially after the channels are completely hydrated. The development of hydrogen-bond networks inside the MOF pores as a function of relative humidity is characterized at the molecular level using experimental and computational infrared spectroscopy. A detailed analysis of the OH-stretch vibrational band indicates that the low-frequency tail stems from strongly polarized hydrogen-bonded water molecules, suggesting the presence of some structural disorder in the experimental samples. Strategies for designing efficient water harvesting MOFs are also proposed based on the mechanism of water adsorption in NU-1500-Cr.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Improvement in the photocurrent collection due to enhanced absorption of light by synthesizing staggered layers of silver nanoclusters in silicon

The quest for increased efficiency of solar cells has driven the research in synthesizing photovoltaic cells involving Si based materials. The efficiency of solar cells involving crystalline Si is stalled around 25% for the last decade. Recently Shi et al. had shown that light trapping can be enhanced by fabricating double layers of Ag nanoparticles in silicon based materials. The light trapping is critically important in a photo devices such as solar cells in order to increase light absorption and efficiency. In the present work, we report enhancement in the absorption of light in Ag ion implanted Si substrates. Multiple low energies Ag ions, ranging from ∼80 keV to ∼30 keV, with different fluences ranging from ∼1 × 10{sup 16} to ∼1 × 10{sup 17} atoms/cm{sup 2} were sequentially implanted into commercially available Si (100) substrates followed by post-thermal annealing to create different sizes of Ag nanoclusters (NC) at different depths in the top 100 nm of the Si. The absorbance of light is increased in Ag implanted Si with a significant increase in the current collection in I-V (current-voltage) photo switching measurements. The experimental photovoltaic cells fabricated with the Ag-implanted Si samples were optically characterized under AM (air mass) 1.5 solar radiation conditions (∼1.0 kW/m{sup 2}). An enhancement in the charge collection were measured in the annealed samples, where prominent Ag NCs were formed in the Si matrix compared to the as-implanted samples with amorphous layers. We believe the enhancement of the photo-current density from the samples with Ag NC is due to the improvement of efficiency of charge collection of e{sup −}-h{sup +} pairs produced by the incident light.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1974

The application of ERTS imagery to monitoring Arctic sea ice

The author has identified the following significant results. Because of the effect of sea ice on the heat balance of the Arctic and because of the expanding economic interest in arctic oil and minerals, extensive monitoring and further study of sea ice is required. The application of ERTS data for mapping ice is evaluated for several arctic areas, including the Bering Sea, the eastern Beaufort Sea, parts of the Canadian Archipelago, and the Greenland Sea. Interpretive techniques are discussed, and the scales and types of ice features that can be detected are described. For the Bering Sea, a sample of ERTS-1 imagery is compared with visual ice reports and aerial photography from the NASA CV-990 aircraft. The results of the investigation demonstrate that ERTS-1 imagery has substantial practical application for monitoring arctic sea ice. Ice features as small as 80-100 m in width can be detected, and the combined use of the visible and near-IR imagery is a powerful tool for identifying ice types. Sequential ERTS-1 observations at high latitudes enable ice deformations and movements to be mapped. Ice conditions in the Bering Sea during early March depicted in ERTS-1 images are in close agreement with aerial ice observations and photographs.

Barnes, J. C.↗

NASA NTRS2017

Boron Nitride Nanoribbons from Exfoliation of Boron Nitride Nanotubes

Two types of boron nitride nanotubes (BNNTs) were exfoliated into boron nitride nanoribbons (BNNR), which were identified using transmission electron microscopy: (1) commercial BNNTs with thin tube walls and small diameters. Tube unzipping was indicated by a large decrease of the sample's surface area and volume for pores less than 2 nm in diameter. (2) BNNTs with large diameters and thick walls synthesized at NASA Glenn Research Center. Here, tube unraveling was indicated by a large increase in external surface area and pore volume. For both, the exfoliation process was similar to the previous reported method to exfoliate commercial hexagonal boron nitride (hBN): Mixtures of BNNT, FeCl3, and NaF (or KF) were sequentially treated in 250 to 350 C nitrogen for intercalation, 500 to 750 C air for exfoliation, and finally HCl for purification. Property changes of the nanosized boron nitride throughout this process were also similar to the previously observed changes of commercial hBN during the exfoliation process: Both crystal structure (x-ray diffraction data) and chemical properties (Fourier-transform infrared spectroscopy data) of the original reactant changed after intercalation and exfoliation, but most (not all) of these changes revert back to those of the reactant once the final, purified products are obtained.

boron nitride nanotube↗

DOE OSTI2022

Functional Data Analysis for Extracting the Intrinsic Dimensionality of Spectra: Application to Chemical Homogeneity in the Open Cluster M67

High-resolution spectroscopic surveys of the Milky Way have entered the Big Data regime and have opened avenues for solving outstanding questions in Galactic archeology. However, exploiting their full potential is limited by complex systematics, whose characterization has not received much attention in modern spectroscopic analyses. In this work, we present a novel method to disentangle the component of spectral data space intrinsic to the stars from that due to systematics. Using functional principal component analysis on a sample of 18,933 giant spectra from APOGEE, we find that the intrinsic structure above the level of observational uncertainties requires ≈10 functional principal components (FPCs). Our FPCs can reduce the dimensionality of spectra, remove systematics, and impute masked wavelengths, thereby enabling accurate studies of stellar populations. To demonstrate the applicability of our FPCs, we use them to infer stellar parameters and abundances of 28 giants in the open cluster M67. We employ Sequential Neural Likelihood, a simulation-based Bayesian inference method that learns likelihood functions using neural density estimators, to incorporate non-Gaussian effects in spectral likelihoods. By hierarchically combining the inferred abundances, we limit the spread of the following elements in M67: Fe ≲ 0.02 dex; C ≲ 0.03 dex; O, Mg, Si, Ni ≲ 0.04 dex; Ca ≲ 0.05 dex; N, Al ≲ 0.07 dex (at 68% confidence). Our constraints suggest a lack of self-pollution by core-collapse supernovae in M67, which has promising implications for the future of chemical tagging to understand the star formation history and dynamical evolution of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Design, fabrication and test of a pneumatically controlled, renewable, microfluidic bead trapping device for sequential injection analysis applications

This paper describes the design, fabrication, and testing of a pneumatically controlled,renewable, microfluidic device for conducting bead-based assays in an automated sequential injection analysis system. The device used a “brick wall”-like pillar array (pillar size: 20 μm length X 50 μm width X 45 μm height) with 5 μm gaps between the pillars serving as the micro filter. The flow channel where bead trapping occurred is 500 μm wide X 75 μm deep. An elastomeric membrane and an air chamber were located underneath the flow channel. By applying pressure to the air chamber, the membrane is deformed and pushed upward against the filter structure. This effectively traps beads larger than 5 μm and creates a “bed” or micro column of beads that can be perfused and washed with liquid samples and reagents. Upon completion of the assay process, the pressure is released and the beads are flushed out from underneath the filter structure to renew the device. Mouse IgG was used as a model analyte to test the feasibility of using the proposed device for immunoassay applications. Resulting microbeads from an on-chip fluorescent immunoassay were individually examined using flow cytometry. The results show that the fluorescence signal intensity distribution is fairly narrow indicating high chemical reaction uniformity among the beads population. Electrochemical onchip assay was also conducted. A detection limit of 0.1 ng/mL1 ppb was achieved and good device reliability and repeatability were demonstrated. The novel microfluidic-based beadstrapping device thus opens up a new pathway to design micro-bead based biosensor immunoassays for clinical and othervarious applications.

beads trapping↗

DOE OSTI2021

Temperature-Swing Synthesis of Large-Size Single-Crystal LiNi 0.6 Mn 0.2 Co 0.2 O 2 Cathode Materials

Single-crystal lithium-nickel-manganese-cobalt-oxide (SC-NMC) has recently emerged as a promising battery cathode material due to its outstanding cycle performance and mechanical stability over the tradional polycrystalline NMC. It is favorable to further increase the grain size of SC-NMC particles to achieve a higher volumetric energy density and minimize surface-related degradations. However, the preparation of large-size yet high performance SC-NMC particles faces a challenge in choosing a suitable temperature for sintering. High temperature promotes grain growth but induces cation mixing that negatively impacts the electrochemical performance. Here we report a temperature-swing sintering (TSS) strategy with two isothermal stages that fulfils the needs for grain growth and structural ordering sequentially. A high-temperature sintering is first used for a short period of time to increase grain size and then the reaction temperature is lowered and kept constant for a longer period of time to improve structural ordering and complete the lithiation process. SC-LiNi 0.6 Mn 0.2 Co 0.2 O 2 materials prepared via TSS exhibit large grain size (~4 μm), a low degree of cation mixing (~0.9%), and outperform the control samples prepared by the conventional sintering method. Furthermore, this work highlights the importance of understanding the process-structure-property relationships and may guide the synthesis of other SC Ni-rich cathode materials.

25 ENERGY STORAGE↗

NASA NTRS1971

Some sequential, distribution-free pattern classification procedures with applications

Some sequential, distribution-free pattern classification techniques are presented. The decision problem to which the proposed classification methods are applied is that of discriminating between two kinds of electroencephalogram responses recorded from a human subject: spontaneous EEG and EEG driven by a stroboscopic light stimulus at the alpha frequency. The classification procedures proposed make use of the theory of order statistics. Estimates of the probabilities of misclassification are given. The procedures were tested on Gaussian samples and the EEG responses.

Poage, J. L.↗

NASA NTRSDate not supplied

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material

Recent microstructure characterization techniques combined with Symbolic Regression(SR)analysis has been proven to generate white box plasticity models well suited for incorporation into FEA software.The current work builds upon those efforts and demonstrates the applicability of Sequential Monte-Carlo (SMC) methods within SR analysis to condense model development and robust optimization into a single, co-dependent process. In this project, SMC methods provide a mechanism through which the observed microstructure features and associated variability can be incorporated into the discovery phase of model development and simultaneously recover approximate parameter distributions through SR analysis. The demonstration utilized a data set consisting of tensile test results from a limited number of sample specimens with corresponding EBSD data from which microstructure features were characterized.The maximum threshold stress model in the Visco-Plastic Self-Consistent (VPSC) code developed by Los Alamos National Laboratories was calibrated using mechanical test data.Synthetic volume elements with statistically equivalent microstructure were generated with DREAM3Dbased on the observed EBSD data. VPSC was used to simulate the corresponding tensile test response for each of the synthetic volume elements. The simulated microstructure and tensile test data was used astraining datafor SMC-SR algorithm and the resulting model was validated with data from the original empirical data set.

Karl Garbrecht↗

DOE OSTI2017

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗