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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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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Event-Based Processing of Neutron Scattering Data

Many of the world's time-of-flight spallation neutrons sources are migrating to the recording of individual neutron events. This provides for new opportunities in data processing, the least of which is to filter the events based on correlating them with logs of sample environment and other ancillary equipment. This paper will describe techniques for processing neutron scattering data acquired in event mode that preserve event information all the way to a final spectrum, including any necessary corrections or normalizations. This results in smaller final errors, while significantly reducing processing time and memory requirements in typical experiments. Results with traditional histogramming techniques will be shown for comparison.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Automated co-adding and energy calibration of large array microcalorimeter data with zero sample knowledge

State-of-the-art microcalorimeter spectrometers now contain large detector arrays with hundreds of individual pixels. Each individual pixel outputs a unique and non-linear response with respect to deposited energy. This work describes a pattern-recognition algorithm to combine these responses into a single energy-calibrated histogram, referred to as co-adding pixels. Photo-peaks from different pixels are matched together based upon how well the match aligns the centroids and heights of neighboring peaks. This usually results in around 100 co-adding calibration points from 30 to 300 keV for a several day acquisition of plutonium items with masses between 0.5 and 10 grams. An additional algorithm energy-calibrates this co-added spectrum using the fluoresced K x-ray emissions from a tantalum absorber and inherent x-ray escape peaks from the tin absorbers. Both algorithms operate without knowledge of the source and are fully automated. This work presents results from the acquisitions of high and low burnup plutonium, 10% enriched uranium, a 153 Gd calibration source, and a 57 Co+ 166m Ho calibration source. In all measurements, resolution defined as the full-width at half-maximum (FWHM) of photo-peaks is preserved between the individual pixel and co-added spectra at around 65 eV for incident photon energies between 60 and 208 keV. The energy calibration algorithm is approximate and yields a calibration curve off by an average of around 200 eV for incident photon energies between 60 and 208 keV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

BoxScore - A real-time beam-diagnosis program for the CAEN digitizer x730 series

BoxScore is a real-time beam diagnosis and monitoring program for the CAEN x730 series digitizer, developed for the ATLAS in-flight system at Argonne National Laboratory. The CAEN x730 series digitizer, with built-in Digital Pulse Processing for the Pulse-Height-Analysis, digitizes the input signal in real-time and processes it using a trapezoidal filter. BoxScore reads the digitizer’s buffer directly, builds and saves events to local files, plots histograms for particle identification, and outputs the rates of selected isotopes every second. Implementation of BoxScore has shortened the time needed for in-flight beam-tuning and has potential applications for other nuclear physics experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Object detection with deep learning for rare event search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. Here, we present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Convolutional neural network↗
DOE OSTI2018

Performance evaluation of the three-point angular correlation function

In recent years, we have observed an increase in the diversity of the processors ecosystem. Different designs and architectures are being studied based on their performance and power characteristics. While using benchmarks for this purpose allows for reproducibility and easy understanding of the results, using real scientific applications allows researchers to realize the actual implications of each design on the overall performance of their codes. This paper analyzes the performance of different implementations of a three-point angular correlation function. This function is used in the study of Large-Scale Distribution of galaxies in a variety of computational platforms. The function is based on histogram construction and presents a large computational cost. This cost dramatically increases with the size of the datasets. We have considered two different GPUs, a set of x86 Intel machines (multi- and many-core), ARM chipsets, as well as an FPGA. We first study the best possible implementation for each platform in terms of time to solution. We then compare the power used by those platforms for a predefined number of datasets. Energy is one of the main constraints that computer architects are facing nowadays. The results will be used to evaluate the performance of this function considering those two targets – time and energy – for those platforms and to analyze the suitability of each of those platforms for this specific problem.

Computer Science↗
DOE OSTI2020

Monte Carlo simulations for understanding the transport properties of metallic nanowires

We report the conductance calculation of metallic nanowires of different metals under the application of a tensile force. The elongation induced by the tensile force was simulated by means of the canonical Monte Carlo method combined with the embedded atom method to compute the energy of the system. We studied nanowires composed of Ag, Au, Pt and Cu. The conductance was calculated by means of the Landauer formula and the non-equilibrium Green function formalism. As previously verified experimentally and using other simulation techniques, the conductance is quantized. In addition to this, we have found that there is a strong correlation between the magnitude of the conductance and the width of the nanowire at its thinnest point. Conductance histograms were analyzed for all the different metals.

36 MATERIALS SCIENCE↗
DOE OSTI2013

ParCAT: Parallel Climate Analysis Toolkit

Climate science is employing increasingly complex models and simulations to analyze the past and predict the future of Earth's climate. This growth in complexity is creating a widening gap between the data being produced and the ability to analyze the datasets. Parallel computing tools are necessary to analyze, compare, and interpret the simulation data. The Parallel Climate Analysis Toolkit (ParCAT) provides basic tools to efficiently use parallel computing techniques to make analysis of these datasets manageable. The toolkit provides the ability to compute spatio-temporal means, differences between runs or differences between averages of runs, and histograms of the values in a data set. ParCAT is implemented as a command-line utility written in C. This allows for easy integration in other tools and allows for use in scripts. This also makes it possible to run ParCAT on many platforms – from laptops to supercomputers. ParCAT outputs NetCDF files so it is compatible with existing utilities such as Panoply and UV-CDAT. This paper describes ParCAT and presents results from some example runs on the Titan system at ORNL.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

An Analysis of Image Segmentation Time in Beam’s-Eye-View Treatment Planning

In this work we tabulate and histogram the image segmentation time for beam’s eye view (BEV) treatment planning in our center. The average time needed to generate contours on CT images delineating normal structures and treatment target volumes is calculated using a data base containing over 500 patients’ BEV plans. The average number of contours and total image segmentation time needed for BEV plans in three common treatment sites, namely, head/neck, lung/chest, and prostate, were estimated.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Application of Magnetic Resonance Imaging and Three-Dimensional Treatment Planning in the Treatment of Orbital Lymphoma

Radiotherapy for lymphoma of the orbit must be individualized for each patient and clinical setting. Most techniques focus on optimizing the dose to the tumor while sparing the lens. This study describes a technique utilizing magnetic resonance imaging (MRI) and three dimensional (3D) planning in the treatment of orbital lymphoma. A patient presented with an intermediate grade lymphoma of the right orbit. The prescribed tumor dose was 4050 cGy in 18 fractions. Three D planning was carried out and tumor volumes, retina, and lens were subsequently outlined. Dose calculations including dose volume histograms of the target, retina, and lens were then performed. Part of the retina was outside of the treatment volume while 50% of the retina received 90% or more of the prescribed dose. The patient was clinically NED when last seen 2 years following therapy with no treatment-related morbidity. Patients with lymphomas of the orbit can be optimally treated using MRI based 3D treatment planning.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2013

Fluorescence Imaging of Single-Molecule Retention Trajectories in Reversed-Phase Chromatographic Particles

Due to its high specific surface area and chemical stability, porous silica is used as a support structure in numerous applications, including heterogeneous catalysis, biomolecule immobilization, sensors, and liquid chromatography. Reversed-phase liquid chromatography (RPLC), which uses porous silica support particles, has become an indispensable separations tool in quality control, pharmaceutics, and environmental analysis requiring identification of compounds in mixtures. For complex samples, the need for higher resolution separations requires an understanding of the time scale of processes responsible for analyte retention in the stationary phase. In the present work, single-molecule fluorescence imaging is used to observe transport of individual molecules within RPLC porous silica particles. This technique allows direct measurement of intraparticle molecular residence times, intraparticle diffusion rates, and the spatial distribution of molecules within the particle. On the basis of the localization uncertainty and characteristic measured diffusion rates, statistical criteria were developed to resolve the frame-to-frame behavior of molecules into moving and stuck events. The measured diffusion coefficient of moving molecules was used in a Monte Carlo simulation of a random-walk model within the cylindrical geometry of the particle diameter and microscope depth-of-field. Furthermore, the simulated molecular transport is in good agreement with the experimental data, indicating transport of moving molecules in the porous particle is described by a random-walk. Histograms of stuck-molecule event times, locations, and their contributions to intraparticle residence times were also characterized.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Conformations of organophosphine oxides

The conformations of a series of organophosphine oxides, OP(CH 3 ) 2 R, where R = methyl, ethyl, isopropyl, tert-butyl, vinyl, and phenyl, are predicted using the MP2/cc-pVTZ level of theory. Comparison of potential energy surfaces for rotation about P–C bonds with crystal structure data reveals a strong correlation between predicted location and energetics of minima and histograms of dihedral angle distributions observed in the solid state. In addition, the most stable conformers are those that minimize the extent of steric repulsion between adjacent rotor substituents, and the torsional barriers tend to increase with the steric bulk of the rotating alkyl group. MM3 force field parameters were adjusted to fit the MP2 results, providing a fast and accurate model for predicting organophosphine oxides shapes—an essential part of understanding the chemistry of these compounds. As a result, the predictive power of the modified MM3 model was tested against MP2/cc-pVTZ conformations for triethylphosphine oxide, OP(CH 2 CH 3 ) 3 , and triphenylphosphine oxide, OP(Ph) 3 .

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Surface-sensitive photon avalanche behavior revealed by single-avalanching-nanoparticle imaging

Avalanching nanoparticles (ANPs) are a new class of lanthanide-based upconverting material demonstrating steep optical nonlinearities with the potential to advance applications ranging from subwavelength bioimaging to neuromorphic computing, nanothermometry, and pressure transduction. Here, we use single-nanocrystal imaging to uncover design-dependent heterogeneity in ANP threshold intensity (I th ). Quantitative comparisons between distributions of I th and ANP shell properties reveal correlations between mean I th values, histogram widths, and nanocrystal shell thickness. Evaluating avalanching behaviors using an established model of shell-dependent surface energy transfer shows that variations in shell thickness-and the resultant energy transfer through the shell to the surface and environment-are likely the primary contributor to ANP-to-ANP I th heterogeneity. Further, nanocrystals with an ~6 nm average shell thickness show I th heterogeneity beyond the extent expected from statistical measurements of shell size and variability using transmission electron microscopy (TEM). Here these results provide a principal guide for the design and application of ANPs to environmental sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Investigation of the Effect of Framework Flexibility on CO 2 Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

Metal–organic frameworks (MOFs) offer promise as selective CO 2 sorbents, but successful MOF sorbent materials need high CO 2 binding affinity and selectivity for CO 2 over water. This work focuses on the use of machine-learned force fields (MLFFs) to model CO 2 adsorption in flexible MOFs, with a focus on SIFSIX-3-Cu, an anion-pillared MOF known for its high CO 2 affinity. A preliminary high-throughput screening of over 900 anion-pillared MOFs was performed using rigid UFF+DDEC6 force fields to predict zero-loading heats of adsorption for CO 2 and H 2 O. SIFSIX-3-Cu was selected for further computational study due to its predicted CO 2 heat of adsorption and experimental relevance. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies and forces, with an iterative sampling scheme combining molecular dynamics, geometry optimization, random geometric insertion, and NVT Monte Carlo-based configuration generation to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included, contrasting with previous models that approximated the MOF as rigid. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations with the MLFF produced CO 2 adsorption isotherms in good agreement with experimental data at direct air capture (DAC) pressures (e.g., 40 Pa), in contrast to previous overestimations of CO 2 sorption by models with rigid structures. Bond and angle histogram analysis showed that MOF flexibility increased the variance of fluorine–fluorine diagonal distances at adsorption sites, resulting in a lower predicted sorption for flexible, asymmetric SIFSIX-3-Cu pore geometries compared to the rigid, symmetric DFT-optimized SIFSIX-3-Cu pore geometry. A detailed description of flexibility afforded by the MLFF resulted in an accurately predicted CO 2 uptake (0.88 mmol/g) at low pressure (40 Pa) compared to the experimentally measured value (1.24 mmol/g). In conclusion, these results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

adsorption↗
DOE OSTI2017

Visualizing Current Flow at the Mesoscale in Disordered Assemblies of Touching Semiconductor Nanocrystals

The transport of electrons through assemblies of nanocrystals is important to performance in optoelectronic applications for these materials. Previous work has primarily focused on single nanocrystals or transitions between pairs of nanocrystals. There is a gap in knowledge of how large numbers of nanocrystals in an assembly behave collectively, and how this collective behavior manifests at the mesoscale. In this work, the variable range hopping (VRH) transport of electrons in disordered assemblies of touching, heavily doped ZnO nanocrystals was visualized at the mesoscale as a function of temperature both theoretically, using the model of Skinner, Chen and Shklovskii (SCS), and experimentally, with conductive atomic force microscopy on ultrathin films only a few particle layers thick. Agreement was obtained between the model and experiments, with a few notable exceptions. The SCS model predicts that a single network within the nanocrystal assembly, comprised of sites connected by small resistances, dominates conduction - namely the optimum band from variable range hopping theory. However, our experiments revealed that in addition to the optimum band, there are subnetworks that appear as additional peaks in the resistance histogram of conductive atomic force microscopy (CAFM) maps. Furthermore, the connections of these subnetworks to the optimum band change in time, such that some subnetworks become connected to the optimum band while others become disconnected and isolated from the optimum band; this observation appears to be an experimental manifestation of the ‘blinking’ phenomenon in our images of mesoscale transport.

Chen, Qinyi↗
DOE OSTI2019

Microscopic Dynamics in an Ionic Liquid Augmented with Organic Solvents

We introduce the complex microscopic dynamics of 1-butyl-3-methyl-imidazolium bis(trifluromethylsulfonyl)imide, [Bmim + ][TFSI – ], ionic liquid mixed with organic solvents to improve its properties for energy storage applications. To probe the microscopic dynamics on different length and time scales, we have used different neutron scattering spectrometers for quasielastic neutron scattering measurements to compare the effects of solvation in several organic solvents with nearly the same dipole moment but broadly varying bulk diffusivity. The ionic liquid–solvent mixtures show a nanoscopic phase separation into an ionic liquid-rich and a solvent-rich phase, as revealed by the model-free dynamic susceptibility data and further corroborated by the cluster histogram analysis of molecular dynamics (MD) simulation. In both phases, we observe a long-range translational mobility of the [Bmim + ] cation, which scales with the bulk diffusivities of the organic solvents; this correlation becomes stronger in the solvent-rich phase. Furthermore, various localized motion modes of the [Bmim + ] cation are observed. A combination of neutron scattering and MD simulations reveals the parameters governing solvent-controlled diffusivity in an ionic liquid, which helps formulate new electrolytes optimized for effective energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2019

Simulating Enhanced Methane Deliverable Capacity of Guest Responsive Pores in Intrinsically Flexible MOFs

In this work, a novel computational procedure, based on the principles of flat-histogram Monte Carlo, is developed for facile prediction of the adsorption thermodynamics of intrinsically flexible adsorbents. We then demonstrate how an accurate prediction of methane deliverable capacity in a metal–organic framework (MOF) with significant intrinsic flexibility requires use of such a method. Dynamic side chains in the framework respond to methane adsorbates and reorganize to exhibit a more conducive pore space at high adsorbate densities while simultaneously providing a less conducive pore space at low adsorbate densities. This “responsive pore” MOF achieves ~20% higher deliverable capacity than if the framework were rigid and elucidates a strategy for designing high deliverable capacity MOFs in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Evidence of Universal Temperature Scaling in Self-Heated Percolating Networks

During routine operation, electrically percolating nanocomposites are subjected to high voltages, leading to spatially heterogeneous current distribution. The heterogeneity implies localized self-heating that may (self-consistently) reroute the percolation pathways and even irreversibly damage the material. In the absence of experiments that can spatially resolve the current distribution and a nonlinear percolation model suitable to interpret them, one relies on empirical rules and safety factors to engineer these materials. In this paper, we use ultrahigh resolution thermo-reflectance imaging, coupled with a new imaging processing technique, to map the spatial distribution ΔT(x, y; I) and histogram f(ΔT) of temperature rise due to self-heating in two types of 2D networks (percolating and copercolating). Remarkably, we find that the self-heating can be described by a simple two-parameter Weibull distribution, even under voltages high enough to reconfigure the percolation pathways. Given the generality of the phenomenological argument supporting the distribution, other percolating networks are likely to show similar stress distribution in response to sufficiently large stimuli. Furthermore, the spatial evolution of the self-heating of network was investigated by analyzing the spatial distribution and spatial correlation, respectively. An estimation of degree of hotspot clustering reveals a mechanism analogous to crystallization physics. Lastly, the results should encourage nonlinear generalization of percolation models necessary for predictive engineering of nanocomposite materials.

42 ENGINEERING↗