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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Application of linear least squares to the analysis of Auger electron spectroscopy depth profiles of plutonium oxides [Application of linear least squares to the analysis of AES depth profiles of plutonium oxides]
In this paper, the application of the linear least squares (LLS) methodology allows for quantitative determination of variation in material composition with depth. LLS fits were applied to decompose and enhance the interpretation of spectra obtained by Auger electron spectroscopy during depth profiles of oxidized plutonium surfaces. By means of the LLS algorithm, chemical state assignments of the Pu P 1 VV/O 45 VV, O KLL, and C KLL Auger transitions were determined and the existence of a subsurface oxycarbide layer was identified, with confirmation of the oxide and metal components provided from comparison to previous measurements of standard samples.
Analysis of bulk heterojunction material parameters using lateral device structures
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Bowtie filters for dedicated breast CT: Analysis of bowtie filter material selection
Purpose: For a given bowtie filter design, both the selection of material and the physical design control the energy fluence, and consequently the dose distribution, in the object. Using three previously described bowtie filter designs, the goal of this work is to demonstrate the effect that different materials have on the bowtie filter performance measures. Methods: Three bowtie filter designs that compensate for one or more aspects of the beam-modifying effects due to the differences in path length in a projection have been designed. The nature of the designs allows for their realization using a variety of materials. The designs were based on a phantom, 14 cm in diameter, composed of 40% fibroglandular and 60% adipose tissue. Bowtie design #1 is based on single material spectral matching and produces nearly uniform spectral shape for radiation incident upon the detector. Bowtie design #2 uses the idea of basis-material decomposition to produce the same spectral shape and intensity at the detector, using two different materials. With bowtie design #3, it is possible to eliminate the beam hardening effect in the reconstructed image by adjusting the bowtie filter thickness so that the effective attenuation coefficient for every ray is the same. Seven different materials were chosen to represent a range of chemical compositions and densities. After calculation of construction parameters for each bowtie filter design, a bowtie filter was created using each of these materials (assuming reasonable construction parameters were obtained), resulting in a total of 26 bowtie filters modeled analytically and in the PENELOPE Monte Carlo simulation environment. Using the analytical model of each bowtie filter, design profiles were obtained and energy fluence as a function of fan-angle was calculated. Projection images with and without each bowtie filter design were also generated using PENELOPE and reconstructed using FBP. Parameters such as dose distribution, noise uniformity, and scatter were investigated. Results: Analytical calculations with and without each bowtie filter show that some materials for a given design produce bowtie filters that are too large for implementation in breast CT scanners or too small to accurately manufacture. Results also demonstrate the ability to manipulate the energy fluence distribution (dynamic range) by using different materials, or different combinations of materials, for a given bowtie filter design. This feature is especially advantageous when using photon counting detector technology. Monte Carlo simulation results from PENELOPE show that all studied material choices for bowtie design #2 achieve nearly uniform dose distribution, noise uniformity index less than 5%, and nearly uniform scatter-to-primary ratio. These same features can also be obtained using certain materials with bowtie designs #1 and #3. Conclusions: With the three bowtie filter designs used in this work, the selection of material is an important design consideration. An appropriate material choice can improve image quality, dose uniformity, and dynamic range.
Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT
Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C{sub T}) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C{sub T} yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C{sub T} were conducted. The results showed that C{sub T} was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.
Medium amplitude parallel superposition (MAPS) rheology. Part 2: Experimental protocols and data analysis
An experimental protocol is developed to directly measure the new material functions revealed by medium amplitude parallel superposition (MAPS) rheology. This protocol measures the medium amplitude response of a material to a simple shear deformation composed of three sine waves at different frequencies, revealing a rich dataset consisting of up to 19 measurements of the third-order complex modulus at distinct three-frequency coordinates. We discuss how the choice of input frequencies influences the features of the MAPS domain studied by the experiment. A polynomial interpolation method for reducing the bias of measured values from spectral leakage and reducing variance due to noise is discussed, including a derivation of the optimal range of amplitudes for the input signal. This leads to the conclusion that conducting the experiment in a stress-controlled fashion possesses a distinct advantage to the strain-controlled mode. The experimental protocol is demonstrated through measurements of the MAPS response of a model complex fluid: a surfactant solution of wormlike micelles. The resulting dataset is indeed large and feature-rich, while still acquired in a time comparable to similar medium amplitude oscillatory shear (MAOS) experiments. We demonstrate that the data represent measurements of an intrinsic material function by studying its internal consistency, compatibility with low-frequency predictions for Coleman–Noll simple fluids, and agreement with data obtained via MAOS amplitude sweeps. Finally, the data are compared to predictions from the corotational Maxwell model to demonstrate the power of MAPS rheology in determining whether a constitutive model is consistent with a material’s time-dependent response.
Systems-level analysis of mechanisms regulating yeast metabolic flux
Not provided.
Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation
Genomes reveal nutrient stress patterns Within the surface ocean, nitrogen, iron, and phosphorous can all be limiting nutrients for phytoplankton depending on location. Ustick et al. used the prevalence of Prochlorococcus genes involved in nutrient acquisition to develop maps of inferred nutrient stress across the global ocean (see the Perspective by Coleman). They found broad patterns of limitation consistent with an Earth system model and nutrient addition experiments. Leveraging metagenomic data in this manner is an appealing approach that will help to expand our understanding of the biogeochemistry in the vast open ocean. Science , this issue p. 287 ; see also p. 239
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement
This paper analyzes a popular computational framework to solve infinite-dimensional Bayesian inverse problems, discretizing the prior and the forward model in a finite-dimensional weighted inner product space. We demonstrate the benefit of working on a weighted space by establishing operator-norm bounds for finite element and graph-based discretizations of Matérn-type priors and deconvolution forward models. For linear-Gaussian inverse problems, we develop a general theory to characterize the error in the approximation to the posterior. We also embed the computational framework into ensemble Kalman methods and MAP estimators for nonlinear inverse problems. Furthermore, our operator-norm bounds for prior discretizations guarantee the scalability and accuracy of these algorithms under mesh refinement.
Infrastructure for In Situ System Monitoring and Application Data Analysis.
Abstract not provided.
Performance Analysis for Using Non-Volatile Memory DIMMs: Opportunities and Challenges.
Abstract not provided.
Performance Analysis and Optimization of the RAMPAGE Metal Alloy Potential Generation Software
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Comparative Analysis of Soft-Error Detection Strategies: A Case Study with Iterative Methods
Undetected soft errors caused by transient bit flips can lead to silent data corruption (SDC), an undesirable outcome where invalid results pass for valid ones. This has motivated the design of soft error detectors to minimize SDCs. However, the detectors have been studied under different contexts, making comparative evaluation difficult. In this paper, we present the first comprehensive evaluation of four online soft error detection techniques in detecting the adverse impact of soft errors on iterative methods. We observe that, across five iterative methods, the detectors studied achieve high but not perfect detection rates. To understand the potential for improved detection, we evaluate a machine-learning based detector that takes as features that are the runtime features observed by the individual detectors to arrive at their conclusions. Our evaluation demonstrates improved but still far from perfect detection accuracy for the machine learning based detectors. This extensive evaluation demonstrates the need for designing error detectors to handle the evolutionary behavior exhibited by iterative solvers.
Progress in the Analysis of Complex Atmospheric Particles
This manuscript presents an overview on recent advances in field and laboratory studies of atmospheric particles formed in processes of environmental air-surfaces interactions. The overarching goal of these studies is to advance predictive understanding of atmospheric particle composition, particle chemistry during aging, and their environmental impacts. The diversity between chemical constituents and lateral heterogeneity within individual particles adds to the chemical complexity of particles and their surfaces. Once emitted, particles undergo transformation via atmospheric aging processes that further modify their complex composition. We highlight a range of modern analytical approaches that enable multi-modal chemical characterization of particles with both molecular and lateral specificity. When combined, they provide a comprehensive arsenal of tools for understanding the nature of particles at air-surface interactions and their reactivity and transformations with atmospheric aging. We discuss applications of these novel approaches in recent studies and highlight additional research areas to explore environmental effects of air-surface interactions.
Nanoscale Analysis of LSM/YSZ Interfaces within Composite Cathodes for Commercial Solid Oxide Fuel Cells
17th International Symposium on Solid Oxide Fuel Cells (SOFC-XVII), Virtual, July 18-23, 2021
An Analysis of the Impact of Particle Growth on Transport Losses in Polymer-Electrolyte Fuel Cells
Voltage cycling causes catalyst nanoparticles in polymer-electrolyte fuel cells to grow. The concomitant loss of interfacial area results in larger kinetic, transport, and possibly ohmic overpotentials. This paper uses recently published experimental data and mathematical models to investigate the evolutions of transport and ohmic resistances to platinum nanoparticles located on the surface and inside the micropores of carbon black supports. Resistance to oxygen transport rises as surface area declines primarily because the flux to each remaining larger platinum particle increases. The path lengths governing oxygen diffusion to surface and buried platinum sites also increase as nanoparticles grow. Platinum nanoparticles on the surface become relatively less favorable as voltage cycling proceeds because they grow faster than platinum in micropores. Because voltage cycling causes total interfacial area to decline, and the fraction located inside micropores to increase, nanoscale ohmic losses increase as a catalyst layer decays. The practical importance of this effect is difficult to discern because proton conductivity in carbon micropores is not well characterized.
Stability Analysis of Electrodeposition across a Structured Electrolyte with Immobilized Anions
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Galvanostatic Intermittent Titration and Performance Based Analysis of LiNi 0.5 Co 0.2 Mn 0.3 O 2 Cathode
Galvanostatic intermittent titration technique (GITT) - a popular method for characterizing kinetic and transport properties of battery electrodes - is predicated on the proper evaluation of electrode active area. LiNi 0.5044 Co 0.1986 Mn 0.2970 O 2 (NCM523) material exhibits a complex morphology in which sub-micron primary particles aggregate to form secondary particle agglomerates. Our work proposes a new active area formulation for primary/secondary particle agglomerate materials to better mimic the morphology of NCM532 electrodes. Furthermore, this formulation is then coupled with macro-homogeneous models to simulate GITT and half-cell performance of NCM523 electrodes. Subsequently, the model results are compared against the experimental results to refine the area formulation. A single parameter, the surface roughness factor, is proposed to mimic the change in interfacial area, diffusivity and exchange current density simultaneously and detailed modeling results are presented to provide valuable insights into the efficacy of the formulation.