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DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Learning Spectral Templates for Photometric Redshift Estimation from Broadband Photometry

Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived from spectral synthesis models. Both of these approaches have their limitations: either the sample sizes are small and often not representative of the diversity of galaxy colors, or the model colors can be biased (often as a function of wavelength), which introduces systematics in the derived redshifts. In this paper, we learn the underlying spectral energy distributions from an ensemble of ∼100 K galaxies with measured redshifts and colors. We show that we are able to reconstruct emission and absorption lines at a significantly higher resolution than the broadband filters used to measure the photometry for a sample of 20 spectral templates. We find that our training algorithm reduces the fraction of outliers in the derived photometric redshifts by up to 28%, bias up to 91%, and scatter up to 25%, when compared to estimates using a standard set of spectral templates. We discuss the current limitations of this approach and its applicability for recovering the underlying properties of galaxies. Our derived templates and the code used to produce these results are publicly available in a dedicated Github repository: https://github.com/dirac-institute/photoz-template-learning.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1975

Studies Relative to the Radiosensitivity of Man: Based on Retrospective Evaluations of Therapeutic and Accidental Total-Body Irradiation

The radiobiologic studies carried out with joint (AEC) ERDA and NASA support during the years 1964 to 1974 at the Medical Division of Oak Ridge Associated Universities are presented. The physiologic data generated were similar in many ways to those previously observed in other medical radiobiologic experiences. They differed, however, in the methods of data acquisition and analysis. Instead of more conventional analytical methods, pulmonary impedance was recorded and quantitated as a measure of radiation-induced gastrointestinal distress and fatiguability. While refinements in dose response related to gastrointestinal distress were accomplished, it was also found that through the use of Fourier analysis of pulmonary impedance waveform GI distress could easily be recognized and quantified even when the initial stages of nausea were below the subjects subjective level of recognition. The results demonstrate that change in pulmonary impedance waveform closely parallel well-defined stages of GI distress, i.e., initial nausea, a progressive increase in nausea, and finally vomiting episodes.

Ricks, R. C.↗

DOE OSTI2018

Highly Selective and Stable Multivariable Gas Sensors for Enhanced Robustness and Reliability of SOFC Operation

GE Global Research, in partnership with SUNY Polytechnic Institute (SUNY Poly) and GE–Fuel Cells LLC, designed, built, and tested multi-gas sensor prototypes for in situ monitoring of gases produced during steam reforming in solid oxide fuel cell (SOFC) systems. To be widely adopted and to deliver a lower operating cost for customers, SOFC systems should enhance their operation reliability and improve stack robustness. These needs can be achieved by early diagnostics of potential upsets by using in-situ gas sensors for the real-time monitoring of gas mixture compositions. Thus, this project objective was to achieve selectivity and stability of sensing of gases for SOFC applications by implementing a new generation of gas sensors, known as multivariable sensors. Such sensors are intended to complement the mature traditional analytical instruments based on sensor performance and design characteristics. The key sensor-performance characteristics include accurate detection and quantitation of several gases in their mixtures and stability of operation in unattended applications. The key design characteristics of sensors include their unobtrusive form factor and cost-effectiveness. The duration of the project was 18 months with the project structure that included three technical tasks such as (1) design and laboratory validation of multivariable sensors for selective detection of H 2 and CO gases, (2) laboratory validation of sensor stability over a two-week testing, and (3) field-validation of the developed multivariable sensor system prototype at GE–Fuel Cells factory. Multi-gas detection was achieved with multivariable photonic sensors designed to operate at elevated temperature. The sensor design was based on three-dimensional (3D) bio-inspired photonic nanostructure that was fabricated using materials responsive to gases of interest such as H 2 and CO. The gas discrimination and the rejection of interferences such as CH 4 , H 2 O, and others was performed by optimizing the design parameters of the 3D photonic nanostructures such as their composition and morphology. Planar sol-gel and sputtered sensing films were fabricated as controls. As a result of the fabrication, functionalization, excitation, and performance testing of 3D photonic sensors, we developed initial design rules for high temperature multivariable sensors. These design rules pave the way for the new generation of cost-effective industrial sensors. Stability of sensor operation was tested in the laboratory conditions under variations of the ambient environments. Stability tests were designed in the order of enhanced complexity and realism of sensor operation and included quantitation of a single gas, quantitation of a single gas in the presence of interferences, and quantitation of two and three gases. We have found that conventional machine learning data analysis stools were unable to correct for the sensor drift. Thus, we have developed new methods of machine learning data analysis that provided us with needed performance stability. Such performance improvement in gas sensing was a significant milestone in bringing our multivariable sensors to practical applications. The project culminated with initial field validation of developed multivariable sensor prototypes at GE–Fuel Cells LLC. The in situ data generated by the sensor system in the lab and field conditions provided inputs that advanced our fundamental understanding of gas sensing at high temperatures to enable sensors for SOFC applications and allowed development of the recommendations for further deliverables.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Surface analysis insight note: Differentiation methods applicable to noisy data for determination of sp2‐ versus sp3‐hybridization of carbon allotropes and AES signal strengths

The derivatives of the spectra are commonly used for quantification in Auger Electron Spectroscopy (AES) spectra, while the derivative of the KLL C Auger line has proven to be valuable in obtaining a measure of the relative proportions of sp 2 ‐ and sp 3 ‐hybridization using the D‐parameter in both AES and X‐ray Photoelectron Spectroscopy (XPS). Differentiation of X‐ray Photoelectron Spectroscopy (XPS) and Auger Electron Spectroscopy (AES) spectra by numerical means is presented and illustrated for polymeric, such as PEEK and Nylon, as well as for graphitic materials including highly ordered pyrolytic graphite and graphene oxide. The most commonly available Savitzky–Golay method is explained mathematically and developed through the case of constructing a 5‐point quadratic polynomial convolution kernel suitable for differentiating spectra of adequate signal to noise. The concept of differentiation of spectra where signal to noise is less than adequate is also developed. Two alternative strategies to Savitzky–Golay differentiation are presented, which fit curves to data that allow derivatives to be obtained where Savitzky–Golay would otherwise fail. These alternative methods involve constructing a parametric curve that fits data over the entire energy interval of interest. Derivatives of spectra are then obtained by differentiating these parametric curves directly. A comparison of results for different materials for which specific sp 2 ‐ vs sp 3 ‐hybridized carbon proportions are of interest is used to emphasize the importance of characterizing methods used to differentiate spectra and understanding the characteristics of instrumentation used to measure spectra. The case for using Principal Component Analysis noise reduction with C KLL spectra is made for spectra collected from a heterogeneous graphene oxide sample.

Fairley, Neal↗

NASA NTRS2008

A Finite Element Analysis for Predicting the Residual Compressive Strength of Impact-Damaged Sandwich Panels

A simple analysis method has been developed for predicting the residual compressive strength of impact-damaged sandwich panels. The method is tailored for honeycomb core-based sandwich specimens that exhibit an indentation growth failure mode under axial compressive loading, which is driven largely by the crushing behavior of the core material. The analysis method is in the form of a finite element model, where the impact-damaged facesheet is represented using shell elements and the core material is represented using spring elements, aligned in the thickness direction of the core. The nonlinear crush response of the core material used in the analysis is based on data from flatwise compression tests. A comparison with a previous analysis method and some experimental data shows good agreement with results from this new approach.

Ratcliffe, James G.↗

NASA NTRS2008

A Finite Element Analysis for Predicting the Residual Compression Strength of Impact-Damaged Sandwich Panels

A simple analysis method has been developed for predicting the residual compression strength of impact-damaged sandwich panels. The method is tailored for honeycomb core-based sandwich specimens that exhibit an indentation growth failure mode under axial compression loading, which is driven largely by the crushing behavior of the core material. The analysis method is in the form of a finite element model, where the impact-damaged facesheet is represented using shell elements and the core material is represented using spring elements, aligned in the thickness direction of the core. The nonlinear crush response of the core material used in the analysis is based on data from flatwise compression tests. A comparison with a previous analysis method and some experimental data shows good agreement with results from this new approach.

Ratcliffe, James G.↗

NASA NTRS1984

A comparison of measured spacecraft modal damping values

The prediction and the measurement of modal damping values of spacecrafts for use in the determination of design loads were summarized. Realistic transient forcing functions were used. The solution of complex eigenvalue response solutions resulting from modal synthesis of subsystems in which each subsystem is assigned modal damping values is discussed. Damping data on both the Voyager and the Galileo spacecrafts using up to eight different types of techniques to analyze modal data are presented. It is concluded that it is difficult to estimate the true damping of a structure and the measured damping values which are dependent on the analysis method of the data.

Wada, B. K.↗

NASA NTRS2006

Implementing EVM Data Analysis Adding Value from a NASA Project Manager's Perspective

Data Analysis is one of the keys to an effective Earned Value Management (EVM) Process. Project Managers (PM) must continually evaluate data in assessing the health of their projects. Good analysis of data can assist PMs in making better decisions in managing projects. To better support our P Ms, National Aeronautics and Space Administration (NASA) - Marshall Space Flight Center (MSFC) recently renewed its emphasis on sound EVM data analysis practices and processes, During this presentation we will discuss the approach that MSFC followed in implementing better data analysis across its Center. We will address our approach to effectively equip and support our projects in applying a sound data analysis process. In addition, the PM for the Space Station Biological Research Project will share her experiences of how effective data analysis can benefit a PM in the decision making process. The PM will discuss how the emphasis on data analysis has helped create a solid method for assessing the project s performance. Using data analysis successfully can be an effective and efficient tool in today s environment with increasing workloads and downsizing workforces

Counts, Stacy↗

NASA NTRS2004

High Altitude Long Endurance Remotely Operated Aircraft - National Airspace System Integration - Simulation IPT: Detailed Airspace Operations Simulation Plan. Version 1.0

The primary goal of Access 5 is to allow safe, reliable and routine operations of High Altitude-Long Endurance Remotely Operated Aircraft (HALE ROAs) within the National Airspace System (NAS). Step 1 of Access 5 addresses the policies, procedures, technologies and implementation issues of introducing such operations into the NAS above pressure altitude 40,000 ft (Flight Level 400 or FL400). Routine HALE ROA activity within the NAS represents a potentially significant change to the tasks and concerns of NAS users, service providers and other stakeholders. Due to the complexity of the NAS, and the importance of maintaining current high levels of safety in the NAS, any significant changes must be thoroughly evaluated prior to implementation. The Access 5 community has been tasked with performing this detailed evaluation of routine HALE-ROA activities in the NAS, and providing to key NAS stakeholders a set of recommended policies and procedures to achieve this goal. Extensive simulation, in concert with a directed flight demonstration program are intended to provide the required supporting evidence that these recommendations are based on sound methods and offer a clear roadmap to achieving safe, reliable and routine HALE ROA operations in the NAS. Through coordination with NAS service providers and policy makers, and with significant input from HALE-ROA manufacturers, operators and pilots, this document presents the detailed simulation plan for Step 1 of Access 5. A brief background of the Access 5 project will be presented with focus on Steps 1 and 2, concerning HALE-ROA operations above FL400 and FL180 respectively. An overview of project management structure follows with particular emphasis on the role of the Simulation IPT and its relationships to other project entities. This discussion will include a description of work packages assigned to the Simulation IPT, and present the specific goals to be achieved for each simulation work package, along with the associated deliverables necessary to achieve these goals and the needs of other Access 5 IPTs. The simulation environment chosen for this task is then outlined. This section includes a description of the system architecture, a list of the necessary assumptions made by the Simulation IPT, and the roles, responsibilities and interactions of simulation participants. The method of simulation conduct is presented in the next section with particular emphasis on scenario development and applicability to evaluation of Step 1 HALE-ROA operations. Following, data collection and analysis methods are discussed for air traffic specialists and air vehicle control station operators. Lastly, a schedule of Step 1 simulation activities is presented for reference.

Source record↗

NASA NTRS1985

Computerized data acquisition and analysis for measuring thermal diffusivity

JPL has been leading a concentrated effort to develop improved thermoelectric materials for space applications. Thermoelectric generators are an attractive source of electrical energy for space power because of lack of moving parts and slow degradation of performance. Thermoelectric material is characterized by: Seebeck coefficient, electrical resistivity and thermal conductivity. To measure the high temperature thermal conductivity is experimentally very difficult. However, it can be calculated from the specific heat and thermal diffusivity which are easier to measure at high temperatures, especially using the flash method. Data acquisition and analysis for this experiment were automated at JPL using inexpensive microcomputer equipment. This approach is superior to tedious and less accurate manual analysis of data. It is also preferred to previously developed systems utilizing expensive minicomputers or mainframes.

Chmielewski, A.↗

NASA NTRSDate not supplied

From Formal Errors Towards Realistic Uncertainties

Evaluation of uncertainties of geodetic parameter estimates is the problem that is not yet solved in a satisfactory way. A direct evaluation of the uncertainties derived from a least square solution is labeled "formal" and is usually biased, sometimes up to an order of magnitude. Customary, the use of formal errors for scientific analysis is discouraged. We claim that the root of the problem is neglecting off-diagonal elements in the variance-covariance matrix of the noise in the data. A careful reconstruction of the full variance-covariance matrix, including the off-diagonal terms greatly improves realism of uncertainty estimates derived from least squares. We processed the dataset of VLBI group delays and built a prior variance-covariance of the atmosphere-driven noise based on analysis of the output of NASA high-resolution numerical weather models. We found that the uncertainties of parameter estimates derived from this least square solution that uses such variance-covariance matrices become much closer to realistic errors. We consider approaches for implementation of this method in routine data analysis of space geodesy data.

Leonid Petrov↗

DOE OSTI2019

Search for Low-Mass Dark Matter with CDMSlite Using a Profile Likelihood Fit

The Cryogenic Dark Matter Search low ionization threshold experiment (CDMSlite) searches for interactions between dark matter particles and germanium nuclei in cryogenic detectors. The experiment has achieved a low energy threshold with improved sensitivity to low-mass (<10 GeV/c2) dark matter particles. We present an analysis of the final CDMSlite data set, taken with a different detector than was used for the two previous CDMSlite data sets. This analysis includes a data "salting" method to protect against bias, improved noise discrimination, background modeling, and the use of profile likelihood methods to search for a dark matter signal in the presence of backgrounds. We achieve an energy threshold of 70 eV and significantly improve the sensitivity for dark matter particles with masses between 2.5 and 10 GeV/c2 compared to previous analyses. We set an upper limit on the dark matter-nucleon scattering cross section in germanium of 5.4×10-42 cm2 at 5 GeV/c2, a factor of ~2.5 improvement over the previous CDMSlite result.

Agnese, R↗

DOE OSTI2023

Machine-learning-assisted automation of single-crystal neutron diffraction

Neutron scattering is a powerful but expensive technique to study materials and discover new matter. Advanced detector technology has significantly improved the efficiency of neutron experiments, increasing the complexity of neutron data reduction and analysis. Machine learning (ML) brings new directions for neutron diffraction data reduction and experiment operation. Here, this work presents an ML-assisted data reduction and analysis method for precise recognition of Bragg peaks and the corresponding regions of interest; it can then automatically screen and align a measured crystal using the recognized peaks, and subsequently plan and optimize the data collection with user-provided information and uncertainty quantification values of detected peaks. This method shows robust performance in different complex sample environments and enables automated single-crystal neutron diffraction.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Dynamical Components Analysis (DCA) v1.0.0

Dynamical Components Analysis is a Python implementation of the method described in "Unsupervised discovery of temporal structure in noisy data with dynamical components analysis". It implements the method as well as related data analysis functions.

Livezey, Jesse↗

DOE OSTI2021

Dynamical Components Analysis (DCA) v1.0.0

Livezey, Jesse↗

DOE OSTI2020

AFIP6-MkII and RERTR-12 Porosity Data Collection and Analysis for Modeling and Simulation

Gathering data for the improvement of nuclear fuel modeling and simulation efforts is the primary driver for this work. Mechanistic models allow for a better understanding of the material on a micro- and macrostructural level while saving time and money over traditional experiment efforts. Historically, summarized data and correlations are the inputs for empirical material models and model validation. When improving these models for nuclear fuels with experimental results, there is a lack of reliable data readily available. Experiments - RERTR-12 and AFIP6-MkII - were conducted to understand the irradiation behavior of metallic U-10Mo monolithic fuels for use in extreme reactor environments such as research reactors like the Advanced Test Reactor (ATR) or the High Flux Isotope Reactor (HFIR). Microstructural characteristics of fission gas pores (FGP) in each experiment are collected using an automated image analysis technique developed at the University of Florida and presented here. A series of statistical tests are performed to explore the reliability of the results, as well as understand where the data is lacking and what future data collection is necessary to provide sufficient information to assist modeling efforts. The focus is on the porosity, pore size, and eccentricity of FGPs formed during irradiation in three AFIP6-MkII samples and one RERTR-12 sample. From the analysis, it is clear there are substantial impacts of fission density on the pore structure, but there also exist also underlying connections between each sample and the behavior observed in the pores. Further analyses of the pre- and post-irradiation microstructure are needed to improve the understanding of these connections. An early method for microstructural data analysis is presented within and is currently being expanded to include other microstructure data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

DASSA v0.0.1

DASSA (Parallel DAS Data Storage and Analysis) provides a data storage engine and analysis engine for data from distributed acoustic sensing and other methods. It supports various data analysis operations, from FFT , signal filter, cross-correlation, compression, stacking etc.

Dong, Bin↗