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Results for “Function 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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First order structure function analysis of statistical scale invariance n the AIRS Observed Water Vapor Field
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Structural and Functional Analysis of the C-Terminal Domain of Nup358/RanBP2
Abstract Not Provided
CR-39 Neutron Sensitivity Study for Improved One-Dimensional Imager of Neutrons (ODIN) Neutron Response Function Analysis at the Sandia Z Facility.
Abstract not provided.
A Metagenomic Meta-analysis Reveals Functional Signatures of Health and Disease in the Human Gut Microbiome
While recent research indicates that human health is affected by the gut microbiome, the functional mechanisms that underlie host-microbiome interactions remain poorly resolved. Metagenomic clinical studies can address this problem by revealing specific microbial functions that stratify healthy and diseased individuals. To improve our understanding of the relationship between the gut microbiome and health, we conducted the first integrative functional analysis of nearly 2,000 publicly available fecal metagenomic samples obtained from eight clinical studies. We identified characteristics of the gut microbiome that associate generally with disease, including functional alpha-diversity, beta-diversity, and beta-dispersion. Using regression modeling, we identified specific microbial functions that robustly stratify diseased individuals from healthy controls. Many of these functions overlapped multiple diseases, suggesting a general role in host health, while others were specific to a single disease and may indicate disease-specific etiologies. Our results clarify potential microbiome-mediated mechanisms of disease and reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics.
Improved Models for Metallic Nanoparticle Cores from Atomic Pair Distribution Function (PDF) Analysis
X-ray atomic pair distribution functions (PDFs) were collected from a range of canonical metallic nanomaterials, both elemental and alloyed, prepared using different synthesis methods and exhibiting drastically different morphological properties. Widely applied shape-tuned attenuated crystal (AC) fcc models proved inadequate, yielding structured, coherent, and correlated fit residuals. However, equally simple discrete cluster models could account for the largest amplitude features in these difference signals. A hypothesis testing based approach to nanoparticle structure modeling systematically ruled out effects from crystallite size, composition, shape, and surface faceting as primary factors contributing to the AC misfit. On the other hand, decahedrally twinned cluster cores were found to be the origin of the AC structure misfits for a majority of the nanomaterials reported here. It is further motivated that the PDF can readily differentiate between the arrangement of domains in these multiply twinned motifs. Most of the nanomaterials surveyed also fall within the sub-5 nm size regime where traditional electron microscopy cannot easily detect and quantify domain structures, with sampling representative of the average nanocrystal synthesized. Here, the results demonstrate that PDF analysis is a powerful method for understanding internal atomic interfaces in small noble metallic nanomaterials. Such core cluster models, easily built algorithmically, should serve as starting structures for more advanced models able to capture atomic positional disorder, ligand induced or otherwise, near nanocrystal surfaces.
Tolerance Bound Calculation for Compact Model Calibration Using Functional Data Analysis.
Abstract not provided.
A Density Functional Theory Analysis of Trends in Glycerol Decomposition on Close-Packed Transition Metal Surfaces
We describe an accelerated density functional theory (DFT)-based computational strategy to determine trends in the decomposition of glycerol via elementary dehydrogenation, C–C, and C–O bond scission reactions on close-packed transition metal surfaces. Beginning with periodic DFT calculations on Pt(111), the thermochemistry of glycerol dehydrogenation on Pd(111), Rh(111), Cu(111) and Ni(111) is determined using a parameter-free, bond order-based scaling relationship. By combining the results with Brønsted–Evans–Polanyi (BEP) relationships to estimate elementary reaction barriers, free energy diagrams are developed on the respective metal surfaces, and trends concerning the relative selectivity and activity for C–C and C–O bond scission in glycerol on the various metals are obtained. The results are consistent with available theoretical and experimental literature and demonstrate that scaling relationships are capable of providing powerful insights into the catalytic chemistry of complex biomolecules.
Ellipsometric characterization and density-functional theory analysis of anisotropic optical properties of single-crystal α-SnS
We report on the anisotropic optical properties of single-crystal tin monosulfide (SnS). The components ε{sub a}, ε{sub b}, and ε{sub c} of the pseudodielectric-function tensor (ε)=(ε₁)+i(ε₂) spectra are taken from 0.73 to 6.45 eV by spectroscopic ellipsometry. The measured (ε) spectra are in a good agreement with the results of the calculated dielectric response from hybrid density functional theory. The (ε) spectra show the direct band-gap onset and a total of eight above-band-gap optical structures that are associated with the interband-transition critical points (CPs). We obtain accurate CP energies by fitting analytic CP expressions to second-energy-derivatives of the (ε) data. Their probable electronic origins and implications for photovoltaic applications are discussed.
Construction and Study of Instrument Response Functions for Analysis of the National Ignition Facility ( NIF) Neutron Time-of-Flight Detectors
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From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas
Functional data analysis (FDA), which is a branch of statistics on modeling infinite dimensional random vectors resided in functional spaces, has become a major research area for Journal of Multivariate Analysis. We review some fundamental concepts of FDA, their origins and connections from multivariate analysis, and some of its recent developments, including multi-level functional data analysis, high-dimensional functional regression, and dependent functional data analysis. Here, we also discuss the impact of these new methodology developments on genetics, plant science, wearable device data analysis, image data analysis, and business analytics. Two real data examples are provided to motivate our discussions.
Density functional theory analysis of the impact of steric interaction on the function of switchable polarity solvents
Here, a density functional theory (DFT) analysis has been performed to explore the impact of steric interactions on the function of switchable polarity solvents (SPS) and their implications on a quantitative structure-activity relationship (QSAR) model previously proposed for SPS. An x-ray crystal structure of the N,N-dimethylcyclohexylammonium bicarbonate (Hdmcha) salt has been solved as an asymmetric unit containing two cation/anion pairs, with a hydrogen bonding interaction observed between the bicarbonate anions, as well as between the cation and anion in each pair. DFT calculations provide an optimized structure of Hdmcha that closely resembles experimental data and reproduces the cation/anion interaction with the inclusion of a dielectric field. Relaxed potential energy surface (PES) scans have been performed on Hdmcha-based computational model compounds, differing in the size of functional group bonded to the nitrogen center, to assess the steric impact of the group on the relative energy and structural properties of the compound. Results suggest that both the length and amount of branching associated with the substituent impact the energetic limitations on rotation of the group along the N-R bond and NC-R bond, and disrupt the energy minimized position of the hydrogen bonded bicarbonate group. The largest interaction resulted from functional groups that featured five bonds between the ammonium proton and a proton on a functional group with the freedom of rotation to form a pseudo-six membered ring which included both protons.
Covariate Dependent Sparse Functional Data Analysis
This study proposes a method to incorporate covariate information into sparse functional data analysis. The method aims at cases where each subject has a limited number of longitudinal measurements and is associated with static covariates. This research is motivated by several use cases in practice. One representative example is void swelling, a nuclear-specific material degradation mechanism. Void swelling is affected by many covariates, including alloy composition and irradiation type. How to accurately model the complicated joint effects of such covariates on the swelling process is the key to mitigating the effect of swelling and ensuring safe operation. Unlike most of the existing methods, the proposed method can handle high-dimensional covariates with the informative covariate identification procedure and sparse and irregularly spaced measurements, that is, does not require complete or dense observations. The main innovation of the proposed method is that we model the variation coming from covariates and the variation left conditioned on covariates, such that the functional principal component analysis and Gaussian process can be conducted in a unified manner. Further, we also propose a systematic approach to identify important covariates in the hypothesis testing context. The methodology is demonstrated on applications in nuclear engineering and healthcare and simulation studies.
Competitive effects of glucan’s main hydrolysates on biochar formation: A combined experiment and density functional theory analysis
We report the complexity of polysaccharide hydrothermal products increases the difficulty of exploring the formation of biochar, limiting the development of biochar. This work clarifies the completive effects of glucan’s main hydrolysates on biochar formation from three aspects: experimental, thermodynamic, and kinetic. The products distribution illustrates that 5-HMF, FA, and LA are mainly involved in the formation of biochar. Biochar mainly includes furan ring, ether group, and ester group by the analysis of magic-angle-spinning nuclear magnetic resonance, X-ray photoelectron spectroscopy, and elemental analysis. Combined experiments and density functional theory analysis, the etherification reaction of 5-HMF itself is most likely to occur and is key to form biochar, followed by the esterification of FA with 5-HMF, and then the etherification of 5-HMF and LA. The further verified experiments also manifest these results. This work will develop a foundation for exploring the complex formation mechanism of cellulose-based biochar.
Silver-Mordenite for Radiologic Gas Capture from Complex Streams: Dual Catalytic CH3-I Decomposition and Iodine Confinement.
Abstract not provided.
Software-Based Challenges of Developing the Future Distribution Grid
The software that the utility industry currently uses may be insufficient to analyze the distribution grid as it rapidly modernizes to include active resources such as distributed generation, switch and voltage control, automation, and increasingly complex loads. Although planners and operators have traditionally viewed the distribution grid as a passive load, utilities and consultants increasingly need enhanced analysis that incorporates active distribution grid loads in order to ensure grid reliability. Numerous commercial and open-source tools are available for analyzing distribution grid systems. These tools vary in complexity from providing basic load-flow and capacity analysis under steady-state conditions to time-series analysis and even geographical representations of dynamic and transient events. The need for each type of analysis is not well understood in the industry, nor are the reasons that distribution analysis requires different techniques and tools both from those now available and from those used for transmission analysis. In addition, there is limited understanding of basic capability of the tools and how they should be practically applied to the evolving distribution system. The study reviews the features and state of the art capability of current tools, including usability and visualization, basic analysis functionality, advanced analysis including inverters, and renewable generation and load modeling. We also discuss the need for each type of distribution grid system analysis. In addition to reviewing basic functionality current models, we discuss dynamics and transient simulation in detail and draw conclusions about existing software?s ability to address the needs of the future distribution grid as well as the barriers to modernization of the distribution grid that are posed by the current state of software and model development. Among our conclusions are that accuracy, data transfer, and data processing abilities are key to future distribution grid modeling, and measured data sources are a key missing element . Modeling tools need to be calibrated based on measured grid data to validate their output in varied conditions such as high renewables penetration and rapidly changing topology. In addition, establishing a standardized data modeling format would enable users to transfer data among tools to take advantage of different analysis features. ?