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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Infrastructure development for radioactive materials at the NSLS-II

The X-ray Powder Diffraction (XPD) Beamline at the National Synchrotron Light Source-II is a multipurpose instrument designed for high-resolution, high-energy X-ray scattering techniques. In this article, the capabilities, opportunities and recent developments in the characterization of radioactive materials at XPD are described. The overarching goal of this work is to provide researchers access to advanced synchrotron techniques suited to the structural characterization of materials for advanced nuclear energy systems. XPD is a new beamline providing high photon flux for X-ray Diffraction, Pair Distribution Function analysis and Small Angle X-ray Scattering. The infrastructure and software described here extend the existing capabilities at XPD to accommodate radioactive materials. Such techniques will contribute crucial information to the characterization and quantification of advanced materials for nuclear energy applications. We describe the automated radioactive sample collection capabilities and recent X-ray Diffraction and Small Angle X-ray Scattering results from neutron irradiated reactor pressure vessel steels and oxide dispersion strengthened steels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

We introduce SymbolFit (API: https://github.com/hftsoi/symbolfit), a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiring an adequate functional form to be determined before the fit can be performed. The main challenge arises when the appropriate functional forms cannot be derived from first principles, especially when there is no underlying true closed-form function for the distribution. In this work, we develop a framework that automates and streamlines the process by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without requiring a predefined functional form because the functional form itself is treated as a trainable parameter, making the process far more efficient and effortless than traditional regression methods. We demonstrate the framework in high-energy physics experiments at the CERN Large Hadron Collider (LHC) using five real proton-proton collision datasets from new physics searches, including background modeling in resonance searches for high-mass dijet, trijet, paired-dijet, diphoton, and dimuon events. We show that our framework can flexibly and efficiently generate a wide range of candidate functions that fit a nontrivial distribution well using a simple fit configuration that varies only by random seed, and that the same fit configuration, which defines a vast function space, can also be applied to distributions of different shapes, whereas achieving a comparable result with traditional methods would have required extensive manual effort.

Tsoi, Ho Fung [Univ. of Pennsylvania, Philadelphia↗
DOE OSTI2022

Angular systematics-free cosmological analysis of galaxy clustering in configuration space

ABSTRACT Galaxy redshift surveys are subject to incompleteness and inhomogeneous sampling due to the various constraints inherent to spectroscopic observations. This can introduce systematic errors on the summary statistics of interest, which need to be mitigated in cosmological analysis to achieve high accuracy. Standard practices involve applying weighting schemes based on completeness estimates across the survey footprint, possibly supplemented with additional weighting schemes accounting for density-dependent effects. In this work, we concentrate on pure angular systematics and describe an alternative approach consisting in analysing the galaxy two-point correlation function where angular modes are nulled. By construction, this procedure removes all possible known and unknown sources of angular observational systematics, but also part of the cosmological signal. We use a modified Landy–Szalay estimator for the two-point correlation function that relies on an additional random catalogue where angular positions are randomly drawn from the galaxy catalogue, and provide an analytical model to describe this modified statistic. We test the model by performing an analysis of the full anisotropic clustering in mock catalogues of luminous red and emission-line galaxies at 0.43 < z < 1.1. We find that the model fully accounts for the modified correlation function in redshift space, without introducing new nuisance parameters. The derived cosmological parameters from the analysis of baryon acoustic oscillations and redshift-space distortions display slightly larger statistical uncertainties, mostly for the growth rate of structure parameter fσ8 that exhibits a $50{{\ \rm per\ cent}}$ statistical error increase, but free from angular systematic error.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Heat of Melting of Small Clusters in the Model of the Potential with the Effective Well Depth

Within the framework of the cluster model using the Mie pair power-law interaction potential with the effective depth of the potential well, whose depth is determined by the number of particles in the cluster, an equation for the calculation of the heat of melting of cluster systems is obtained. The resulting mathematical expression does not contain empirical constants and shows that the ratio of the heat of melting of a cluster to the heat of melting of a macroscopic sample is a universal function of the number of particles in the cluster and mathematically described by the square of the hyperbolic tangent.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Evaluating the impact of functional groups on membrane‐mediated CO 2 / N 2 gas separations using a common polymer backbone

Abstract Polymeric membranes have shown tremendous promise for the separation of CO 2 from flue gas streams. However, few systematic studies have been conducted to better understand the impact that chemical functionalities have on membrane‐based gas separation performance. To address this gap, we herein describe the synthesis and gas separation performance of a series of vinyl‐addition polynorbornenes bearing various CO 2 ‐philic functional groups. To facilitate direct comparison between functional groups, each material was designed to maintain a common polymer backbone. Though the incorporation of CO 2 ‐philic moieties within a dense polymeric membrane is frequently hypothesized to enhance CO 2 solubility, and thereby increase CO 2 /N 2 selectivity, our results demonstrate that the incorporation of CO 2 ‐philic groups onto a common polymer backbone do not necessarily result in increased gas separation performance. Experimental and computational results demonstrate that the incorporation of amidoxime groups onto a polynorbornene backbone increase CO 2 /N 2 selectivity, whereas commonly employed ethereal side chains only increased permeability.

Higgins, Morgan A.↗
DOE OSTI2020

Nucleon localization function in rotating nuclei

An electron localization function was originally introduced to visualize in positional space bond structures in molecules. It became a useful tool to describe electron configurations in atoms, molecules, and solids. In nuclear physics, a nucleon localization function (NLF) has been used to characterize cluster structures in light nuclei, formation of fragments in fission, and pasta phases appearing in the inner crust of neutron stars. In the case of a deformed rotating nucleus, several NLFs can be introduced, depending on the definition of the spin-quantization axis, direction of the total angular momentum, and self-consistent symmetries of the system. Contributions to the NLF from the current density, spin-current tensor density, and density gradient terms are negligible in the nuclear interior. The oscillating pattern of the simplified NLF can be explained in terms of a constructive interference between kinetic-energy and particle densities. The characteristic nodal pattern seen in the NLF in the direction of major axis of a rotating nucleus comes from single-particle orbits carrying large aligned angular momentum. The variation of the NLF along the minor axis of the nucleus can be traced back to deformation-aligned orbits.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Remote Sensing of Multitemporal Functional Lake‐To‐Channel Connectivity and Implications for Water Movement Through the Mackenzie River Delta, Canada

The Mackenzie River Delta in Canada is a mediator of hydrological transport between the expansive Mackenzie River watershed and the Beaufort Sea. Within the delta, lakes frequently act as water and sediment traps, limiting or delaying the movement of material to the coastal ocean. The degree to which this filtering takes place depends on the ease with which sediment-laden water is transported from distributary channels into deltaic lakes, referred to as functional lake-to-channel connectivity, which varies both spatially and temporally. Tracking of connectivity has previously been limited to either small regions of the delta or has focused on a snapshot of connectivity at a single instance in time. Here we describe an algorithm that uses Landsat imagery to track summertime functional lake-to-channel connectivity of 10,362 lakes between 1984 and 2022 on an image-by-image basis. We calculate a total average connected lake area of 1400.7 km 2 during the 2 weeks after peak discharge, 763.6 km 2 higher than previous estimates, suggesting a larger influence of connected lakes on water movement through the delta than previously estimated. We also identify water level thresholds that lead to the initiation of high sediment river water movement into 5,989 lakes (908 lakes with uncertainty ≤±0.5 m), and identify an additional 2899 lakes whose connectivity does not vary at all. As the Arctic hydrological cycle responds to climate change, this work lays a foundation for tracking the movement of water, and the matter it carries, from the Mackenzie River watershed to the Beaufort Sea.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Interplay between Terminal and Bridging Diiron Hydrides in Neutral and Oxidized States

This study describes the structural, spectroscopic, and electrochemical properties of electronically unsymmetrical diiron hydrides. The terminal hydride Cp*Fe(pdt)Fe(dppe)(CO)H ([1(t-H)]0, Cp*– = Me5C5 –, pdt2– = CH2(CH2S–)2, dppe = Ph2PC2H4PPh2) was prepared by hydride reduction of [Cp*Fe(pdt)Fe(dppe)(CO)(NCMe)]+. As established by X-ray crystallography, [1(t- H)]0 features a terminal hydride ligand. Unlike previous examples of terminal diiron hydrides, [1(t-H)]0 does not isomerize to the bridging hydride [1(µ-H)]0. Oxidation of [1(t-H)]0 gives [1(t- H)]+, which was also characterized crystallographically as its BF4 – salt. Density functional theory (DFT) calculations indicate that [1(t-H)]+ is best described as containing an Cp*Fe(III) center. In solution, [1(t-H)]+ isomerizes to [1(µ-H)]+ as anticipated by DFT. Reduction of [1(µ- H)]+ by Cp2Co affords the diferrous bridging hydride [1(µ-H)]0. Electrochemical measurements and DFT calculations indicate that the couples [1(t-H)]+/0 and [1(µ-H)]+/0 differ by 210 mV. Qualitative measurements indicate that [1(t-H)]0 and [1(µ-H)]0 are close in free energy. Protonation of [1(t-H)]0 in MeCN solution affords H2 even with weak acids via hydride transfer.

Yu, Xin↗
DOE OSTI2020

Starting-point-independent quantum Monte Carlo calculations of iron oxide

Quantum Monte Carlo (QMC) methods are useful for studies of strongly correlated materials because they are many body in nature and use the physical Hamiltonian. Typical calculations assume as a starting point a wave function constructed from single-particle orbitals obtained from one-body methods, e.g., density functional theory. However, mean-field-derived wave functions can sometimes lead to systematic QMC biases if the mean-field result poorly describes the true ground state. In this study, we examine the accuracy and flexibility of QMC trial wave functions using variational and fixed-node diffusion QMC estimates of the total spin density and lattice distortion of antiferromagnetic iron oxide (FeO) in the ground state B1 crystal structure. We found that for relatively simple wave functions the predicted lattice distortion was controlled by the choice of single-particle orbitals used to construct the wave function, rather than by subsequent wave function optimization techniques within QMC. By optimizing the orbitals with QMC, we then demonstrate starting-point independence of the trial wave function with respect to the method by which the orbitals were constructed by demonstrating convergence of the energy, spin density, and predicted lattice distortion for two qualitatively different sets of orbitals. The results suggest that orbital optimization is a promising method for accurate many-body calculations of strongly correlated condensed phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Top-down design of protein architectures with reinforcement learning

As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a “top-down” reinforcement learning–based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo–electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.

Science & Technology - Other Topics↗
DOE OSTI2018

Investigation of the Mesostructure of Transition-Metal Monogermanides Synthesized under Pressure

The mesostructure of transition-metal monogermanides Mn{sub 1–} {sub x}Co{sub x}Ge is studied by small-angle neutron scattering in a wide range of concentrations x = 0–0.95. These compounds were synthesized under high pressure and are metastable under normal conditions. The experimental dependences I(Q) obtained for the whole series of samples in the range of transferred momenta (6 × 10{sup –2} nm{sup –1} < Q< 2.5 nm{sup –1}) are described by the power dependence Q{sup –n} with an exponent n = 2.99 ± 0.02, uniquely related to the fractal properties of the system under study. The dependence obtained indicates that the superatomic structure of the samples is characterized by the presence of defects with a spatial organization described by a fractal model with a logarithmic dependence of the correlation function of the defect density. It is interesting to note that such defects are absent in the isostructural FeGe compound, i.e., the experimental dependences of the intensity are described well by the expression Q{sup –n} with an exponent n = 4.1 ± 0.1, which demonstrates the presence of crystallites with a uniform density distribution inside and a sharp boundary characterizing the surface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Light (anti)nuclei production in Pb-Pb collisions at s N N = 5.02 TeV

The measurement of the production of deuterons, tritons and 3 He and their antiparticles in Pb-Pb collisions at $\sqrt{s_{NN}}$= 5.02 TeV is presented in this article. The measurements are carried out at midrapidity (|y| < 0.5) as a function of collision centrality using the ALICE detector. The p T -integrated yields, the coalescence parameters and the ratios to protons and antiprotons are reported and compared with nucleosynthesis models. The comparison of these results in different collision systems at different center-of-mass collision energies reveals a suppression of nucleus production in small systems. In the Statistical Hadronisation Model framework, this can be explained by a small correlation volume where the baryon number is conserved, as already shown in previous fluctuation analyses. However, a different size of the correlation volume is required to describe the proton yields in the same data sets. The coalescence model can describe this suppression by the fact that the wave functions of the nuclei are large and the fireball size starts to become comparable and even much smaller than the actual nucleus at low multiplicities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Soft pions and transport near the chiral critical point

During the expansion of a heavy ion collision, the system passes close to the $\textit{O}$(4) critical point of QCD, and thus the fluctuations of the order parameter $(σ, \overrightarrow{π}$) are expected to be enhanced. In this work, our goal is to compute how these enhanced fluctuations modify the transport coefficients of QCD near the pseudocritical point. We also make a phenomenological estimate for how chiral fluctuations could effect the momentum spectrum of soft pions. We first formulate the appropriate stochastic hydrodynamic equations close to the $\textit{O}$(4) critical point. Then, working in mean field, we determine the correlation functions of the stress tensor and the currents which result from this stochastic real-time theory, and use these correlation functions to determine the scaling behavior of the transport coefficients. The hydrodynamic theory also describes the propagation of pion waves, fixing the scaling behavior of the dispersion curve of soft pions. We present scaling functions for the shear viscosity and the charge conductivities near the pseudocritical point and estimate the absolute magnitude of the critical fluctuations to these parameters and the bulk viscosity. Using the calculated pion dispersion curve, we estimate the expected critical enhancement of soft pion yields, and this estimate provides a plausible explanation for the excess seen in experiment relative to ordinary hydrodynamic computations. Our results motivate further phenomenological and numerical work on the implications of chiral symmetry on real-time properties of thermal QCD near the pseudocritical point.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Combining phase-field crystal methods with a Cahn-Hilliard model for binary alloys

Diffusion-induced phase transitions typically change the lattice symmetry of the host material. In battery electrodes, for example, Li ions (diffusing species) are inserted between layers in a crystalline electrode material (host). This diffusion induces lattice distortions and defect formations in the electrode. The structural changes to the lattice symmetry affect the host material’s properties. We propose a 2D theoretical framework that couples a Cahn-Hilliard (CH) model, which describes the composition field of a diffusing species, with a phase-field crystal (PFC) model, which describes the host-material lattice symmetry. We couple the two continuum models via coordinate transformation coefficients. We introduce the transformation coefficients in the PFC method to describe affine lattice deformations. These transformation coefficients are modeled as functions of the composition field. Using this coupled approach, we explore the effects of coarse-grained lattice symmetry and distortions on a diffusion-induced phase transition process. In this paper, we demonstrate the working of the CH-PFC model through three representative examples: First, we describe base cases with hexagonal and square symmetries for two composition fields. Next, we illustrate how the CH-PFC method interpolates lattice symmetry across a diffuse phase boundary. Finally, we compute a Cahn-Hilliard type of diffusion and model the accompanying changes to lattice symmetry during a phase transition process.

25 ENERGY STORAGE↗
DOE OSTI2018

Phase stability and interlayer interaction of blue phosphorene

In this work, we study the interlayer interactions between sheets of blue phosphorus with quantum Monte Carlo (QMC) methods. Here, we find that as previously observed in black phosphorus, interlayer binding of blue phosphorus cannot be described by van der Waals (vdW) interactions alone within the density functional theory framework. Specifically, while some vdW density functionals produced reasonable binding curves, none of them could provide a correct, even qualitatively, description of charge redistribution due to interlayer binding. We also show that small systematic errors in common practice QMC calculations, such as the choice of optimized geometry and finite-size corrections, are non-negligible given the energy and length scales of this problem. We mitigate some of the major sources of error and report QMC-optimized lattice constant, stacking, and interlayer binding energy for blue phosphorus. Finally, it is strongly suggested that these considerations are important and quite general in the modeling of two-dimensional phosphorus allotropes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Functional Characterization of Cell-Free Expressed OprF Porin from Pseudomonas aeruginosa Stably Incorporated in Tethered Lipid Bilayers

OprF has a central role in Pseudomonas aeruginosa virulence and thus provides a putative target for either vaccines or antibiotic cofactors that could overcome the bacterium’s natural resistance to antibiotics. Here we describe a procedure to optimize the production of highly pure and functional OprF porins that are then incorporated into a tethered lipid bilayer. This is a stable biomimetic system that provides the capability to investigate structural aspects and function of OprF using and neutron reflectometry and electrical impedance spectroscopy. The recombinant OprF produced using the optimized cell-free procedure yielded a quantity of between 0.5 to 1.0 mg/mL with a purity ranging from 85 to 91% in the proteoliposomes. The recombinant OprF is capable of binding IFN-γ and is correctly folded in the proteoliposomes. Because OprF proteins form pores the biomimetic system allowed the measurement of OprF conductance using impedance spectroscopy. The neutron reflectometry measurements showed that the OprF protein is incorporated into the lipid bilayer but with parts of the protein in both the regions above and below the lipid bilayer. Those structural aspects are coherent with the current assumed structure of a transmembrane N-terminal domain composed by eight stranded beta-barrels and a globular C-terminal domain located in the periplasm. Currently there are no crystal structures available for OprF. The experimental model system that we describe provides a basis for further fundamental studies of OprF and particularly for the ongoing biotechnological development of OprF as a target for antibacterial drugs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dependence of emittance on the length of an electron bunch during laser-plasma acceleration in guiding structures

We report a theoretical analysis and numerical simulation of the dynamics of transverse emittance of an electron bunch during its acceleration in wake fields generated by a laser pulse in a weakly nonlinear mode. Analytical expressions are obtained for the main factors affecting the emittance growth during acceleration and the case is considered when the characteristic transverse size of the injected bunch exceeds the matched radius determined by the focusing force at the injection point, the initial emittance and the electron bunch energy, and the resulting value of emittance is much larger than the initial one. The dynamics of the emittance growth during acceleration as a function of the length of the electron bunch is described, and the length of the bunch is found at which there occurs a complete phase mixing of betatron oscillations of electrons and the emittance increases to its maximum value determined by the bunch parameters and the focusing force at the injection point. Analytical expressions are in good agreement with the results of numerical simulation. (paper)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗