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At least 1,081 records · Page 60

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NASA NTRS2014

FLASH LIDAR Based Relative Navigation

Relative navigation remains the most challenging part of spacecraft rendezvous and docking. In recent years, flash LIDARs, have been increasingly selected as the go-to sensors for proximity operations and docking. Flash LIDARS are generally lighter and require less power that scanning Lidars. Flash LIDARs do not have moving parts, and they are capable of tracking multiple targets as well as generating a 3D map of a given target. However, there are some significant drawbacks of Flash Lidars that must be resolved if their use is to be of long-term significance. Overcoming the challenges of Flash LIDARs for navigation-namely, low technology readiness level, lack of historical performance data, target identification, existence of false positives, and performance of vision processing algorithms as intermediaries between the raw sensor data and the Kalman filter-requires a world-class testing facility, such as the Lockheed Martin Space Operations Simulation Center (SOSC). Ground-based testing is a critical step for maturing the next-generation flash LIDAR-based spacecraft relative navigation. This paper will focus on the tests of an integrated relative navigation system conducted at the SOSC in January 2014. The intent of the tests was to characterize and then improve the performance of relative navigation, while addressing many of the flash LIDAR challenges mentioned above. A section on navigation performance and future recommendation completes the discussion.

Brazzel, Jack↗

DOE OSTI2019

Superconductivity in Oxides Generated by Percolating Electron or Hole Bipolarons

It is remarkable that in over three decades since the discovery of high-temperature superconductivity (HTS) in cuprates, no other class of materials has been found to exhibit this property above the liquid nitrogen boiling point at ambient pressure. Here, we describe the conformation of hole and electron bipolarons in doped cuprates and tungstates. Important similarities and differences between them are discussed. Both hole and electron bipolarons can percolate forming filaments or clusters in the bulk or on the surfaces as well. Upon cooling, these electronic objects can show superconducting properties. It is proposed that oxygen-reduced tungsten oxide WO{sub 3−x} in bulk or thin film form is a promising material to explore the HTS. Such material would be another example of bipolaronic superconductivity similar to cuprates. However, there is also an important difference: in the cuprates, holes predominantly enter the oxygen orbitals and hole bipolarons are composed of three-spin polarons, whereas in the tungstates, doped electrons are located on tungsten ions and W{sup 5+}–O{sup 2−}–W{sup 5+}electron bipolarons form from 5d{sup 1} W{sup 5+} polarons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Novel PtNi single-atom–nanocluster (SA–NC) ensembles promote Tafel kinetics and ampere-class AEM hydrogen evolution

The development of efficient, durable, and low-PGM electrocatalysts for the hydrogen evolution reaction (HER) in alkaline media is critical for next-generation electrolysis technologies. We report a facile two-step synthesis of highly dispersed PtNi and PtNi-nitride nanoclusters (NCs) (~2.3 nm) with ultralow Pt content (0.5 at.%) anchored on N-doped Vulcan carbon. Structural and compositional characterization via XAS, XPS, HAADF-STEM, HRTEM, and EDS mapping established key structure–activity relationships across varying Pt/Ni ratios and pyrolysis temperatures. The Pt0.5Ni0.5/C-750 catalyst, an ensemble of PtNi M-N-C type single-atom (SA) moieties with neighboring PtNi nanoclusters (NC), exhibited superior HER performance in alkaline media, achieving overpotentials of 30, 115, and 210 mV at 10, 100, and 500 mA cm−2, respectively. Despite at a lower Pt content, this novel SA–NC ensemble outperformed commercial Pt/C by ~36%. A standardized literature comparison with contemporary Pt- and Ru-doped analogues reveals the as-prepared Pt0.5Ni0.5/C-750 to sit at the apex of Tafel-limited kinetics and low overpotential at 100 mA cm−2. Tafel-limited Tafel slopes in both alkaline and acidic regimes confirm favorable proton recombination kinetics. Mass activities at 200 mV reached 13.8 and 18.84 A mgPt−1 in alkaline and acidic media, respectively. However, excessive nitridation (e.g., at 650 °C) adversely altered Pt electronic structure and HER kinetics. While Ni enhanced alkaline HER, acidic HER favored Ni-free analogues. Pt0.5Ni0.5/C-750 also demonstrated robust temperature responsiveness and 300-h operational stability at high current densities (0.5–1.0 A cm−2) in MEA tests. This work presents a scalable strategy for designing thermally responsive, durable, and compositionally tunable NC catalysts with neighboring SA moieties for alkaline electrolysis.

AEMWE↗

DOE OSTI2015

A modified Lax-Phillips scattering theory for quantum mechanics

The Lax-Phillips scattering theory is an appealing abstract framework for the analysis of scattering resonances. Quantum mechanical adaptations of the theory have been proposed. However, since these quantum adaptations essentially retain the original structure of the theory, assuming the existence of incoming and outgoing subspaces for the evolution and requiring the spectrum of the generator of evolution to be unbounded from below, their range of applications is rather limited. In this paper, it is shown that if we replace the assumption regarding the existence of incoming and outgoing subspaces by the assumption of the existence of Lyapunov operators for the quantum evolution (the existence of which has been proved for certain classes of quantum mechanical scattering problems), then it is possible to construct a structure analogous to the Lax-Phillips structure for scattering problems for which the spectrum of the generator of evolution is bounded from below.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

ASME Code change proposal to implement new universal high temperature constitutive models for Section III, Division 5

This report completes work on a universal high temperature constitutive model suitable for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules for the design by inelastic analysis of Class A nuclear reactor components. The goals of this work are to provide a simple model form that adequately captures the high temperature response of materials and can be applied to any future Code material. Additionally, the report describes an automated process for calibrating a model against test data. The idea is to simplify the effort required to generate a constitutive model for an arbitrary material, provided test data is available. This will accelerate the process of qualifying new Code materials in the future. In addition, the report provides calibrated models and detailed validation comparisons to test data for five currently-qualified or soon-to-be qualified materials: 316H, Grade 91, Alloy 800H, Alloy 617, and Alloy 709. The report surveys the available data for the remaining two ASME Class Materials --- 2.25Cr-1Mo and 304H --- concluding that there is enough data data to generate a model for 2.25Cr-1Mo steel provided some additional sources of non-public data can be included in the test database, but that a dedicated cyclic test campaign would be needed for 304H. Supplemental material includes the full text of an ASME Code change proposal to incorporate the models for the four currently-qualified Class A material, detailed validation comparisons to test data for the five material models, and input files for reference implementations of the constitutive models in the NEML and NEML2 modeling frameworks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗

DOE OSTI2024

DRDMannTurb: A Python package for scalable, data-driven synthetic turbulence

Synthetic turbulence models (STMs) are used in wind engineering to generate realistic flow fields and are employed as inputs to industrial wind simulations. Examples include prescribing inlet conditions in large eddy simulations that model loads on wind turbines and tall buildings. We are interested in STMs capable of generating fluctuations based on prescribed second-moment statistics since such models can simulate environmental conditions that closely resemble on-site observations. To this end, the widely used Mann model (see Mann, 1994, 1998) is the inspiration for DRDMannTurb. The Mann model is described by three physical parameters: a magnitude parameter influencing the global variance of the wind field and corresponding to the Kolmogorov constant multiplied by the rate of viscous dissipation of the turbulent kinetic energy to the two-thirds, αϵ 2/3 , a turbulence length scale parameter L, and a nondimensional parameter Γ related to the lifetime of the eddies. A number of studies, as well as international standards (e.g., those by the International Electrotechnical Commission (IEC)), include recommended values for these three parameters with the goal of standardizing wind simulations according to observed energy spectra. Yet, having only three parameters, the Mann model faces limitations in accurately representing the diversity of observable spectra. This Python package enables users to extend the Mann model and more accurately fit field measurements through flexible neural network models of the eddy lifetime function. Following Keith et al. (2021), we refer to this class of models as Deep Rapid Distortion (DRD) models. DRDMannTurb also includes a general module implementing an efficient method for synthetic turbulence generation based on a domain decomposition technique. This technique is also described in Keith et al. (2021).

17 WIND ENERGY↗

DOE OSTI2019

Ultrafast Spectroscopy of Hydrogenase Enzyme Models

Hydrogenase enzymes are nature’s solution to using molecular hydrogen as an energy source. While the air-sensitivity of these biological catalysts make them impractical for industrial hydrogen generation, synthetic mimics show promise in acting as effective substitutes. Hydrogenase model compounds also enable detailed investigation of the ultrafast dynamics and chemical reaction mechanisms using ultrafast infrared spectroscopy. This chapter reviews the progress in applying ultrafast transient infrared absorption and multidimensional spectroscopy to a range of small molecule hydrogenase model compounds, as well as several macromolecular and larger constructs. The rich vibrational structure and straightforward chemical modularity of these diiron compounds represent an exciting class of molecules that are, at the same time, excellent model systems for fundamental chemical dynamics, and practical molecular catalytic components with direct application to next-generation hydrogen-based energy strategies.

Eckert, Peter↗

DOE OSTI2021

Evaluating causal‐based feature selection for fuel property prediction models

Abstract In‐silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench‐scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation‐based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal‐based feature selection might have on both model performance and identification of key molecular substructures. We found that causal‐based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.

Nguyen, Bernard↗

DOE OSTI2015

Chiral Thirring–Wess model

The vector type of interaction of the Thirring–Wess model was replaced by the chiral type and a new model was presented which was termed as chiral Thirring–Wess model in Rahaman (2015). The model was studied there with a Faddeevian class of regularization. Few ambiguity parameters were allowed there with the apprehension that unitarity might be threatened like the chiral generation of the Schwinger model. In the present work it has been shown that no counter term containing the regularization ambiguity is needed for this model to be physically sensible. So the chiral Thirring–Wess model is studied here without the presence of any ambiguity parameter and it has been found that the model not only remains exactly solvable but also does not lose the unitarity like the chiral generation of the Schwinger model. The phase space structure and the theoretical spectrum of this new model have been determined in the present scenario. The theoretical spectrum is found to contain a massive boson with ambiguity free mass and a massless boson.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Anisotropic stars for spherically symmetric spacetimes satisfying the Karmarkar condition

A new class of solution describing an anisotropic stellar configuration satisfying Karmarkar’s condition i.e. spherically symmetric metric of embedding class 1, is reported. It has been shown that the compact star model is physically well-behaved and meet all the physical requirements for a stable configuration in hydrostatic equilibrium. Our model describes compact stars like Vela X-1 and 4U1608-52 to a very good approximation. - Highlights: • We consider the Einstein field equations for spherically symmetric spacetimes of embedding class one. • We explore the Karmarkar condition for spherically symmetric, bounded matter configurations. • The Karmarkar condition reduces the solution-generating method to the Einstein field equations to a single metric function. • By specifying this metric function we generate physically plausible stellar configurations with anisotropic pressure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

H/D exchange mass spectrometry and statistical coupling analysis reveal a role for allostery in a ferredoxin-dependent bifurcating transhydrogenase catalytic cycle

Recent investigations into ferredoxin-dependent transhydrogenases, a class of enzymes responsible for electron transport, have highlighted the biological importance of flavin-based electron bifurcation (FBEB). FBEB generates biomolecules with very low reduction potential by coupling the oxidation of an electron donor with intermediate potential to the reduction of high and low potential molecules. Bifurcating systems can generate biomolecules with very low reduction potentials, such as reduced ferredoxin (Fd), from species such as NADPH. Metabolic systems that use bifurcation are more efficient and confer a competitive advantage for the organisms that harbor them. Structural models are now available for two NADH-dependent ferredoxin-NADP + oxidoreductase (Nfn) complexes. These models, together with spectroscopic studies, have provided considerable insight into the catalytic process of FBEB. However, much about the mechanism and regulation of these multi-subunit proteins remains unclear. Using hydrogen/deuterium exchange mass spectrometry (HDX-MS) and statistical coupling analysis (SCA), we identified in this work specific pathways of communication within the model FBEB system, Nfn from Pyrococus furiosus , under conditions at each step of the catalytic cycle. HDX-MS revealed evidence for allosteric coupling across protein subunits upon nucleotide and ferredoxin binding. SCA uncovered a network of co-evolving residues that can provide connectivity across the complex. Together, the HDX-MS and SCA data show that protein allostery occurs across the ensemble of iron-sulfur cofactors and ligand binding sites using specific pathways that connect domains allowing them to function as dynamically coordinated units.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

RalGPS2 is involved in tunneling nanotubes formation in 5637 bladder cancer cells

Highlights: • RalGPS2 is expressed in 5637 cancer cells and is essential for their growth. • RalGPS2 is required for TNT formation. • The overexpression of RalGPS2 PH domain markedly increased the number and length of TNT. • Interaction between RalA and Sec5 is required for TNT formation in 5637 cells. • RalGPS2 interacts with LST1 and RalA leading to TNT formation. RalGPS2 is a Ras-independent Guanine Nucleotide Exchange Factor (GEF) for RalA containing a PH domain and an SH3-binding region and it is involved in several cellular processes, such as cytokinesis, control of cell cycle progression, differentiation, cytoskeleton organization and rearrangement. Up to now, few data have been published regarding RalGPS2 role in cancer cells, and its involvement in bladder cancer is yet to be established. In this paper we demonstrated that RalGPS2 is expressed in urothelial carcinoma-derived 5637 cancer cells and is essential for cellular growth. These cells produces thin membrane protrusions that displayed the characteristics of actin rich tunneling nanotubes (TNTs) and here we show that RalGPS2 is involved in the formation of these cellular protrusions. In fact the overexpression of RalGPS2 or of its PH-domain increased markedly the number and the length of nanotubes, while the knock-down of RalGPS2 caused a strong reduction of these structures. Moreover, using a series of RalA mutants impaired in the interaction with different downstream components (Sec5, Exo84, RalBP1) we demonstrated that the interaction of RalA with Sec5 is required for TNTs formation. Furthermore, we found that RalGPS2 interacts with the transmembrane MHC class III protein leukocyte specific transcript 1 (LST1) and RalA, leading to the formation of a complex which promotes TNTs generation. These findings allow us to add novel elements to molecular models that have been previously proposed regarding TNTs formation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

A Nitrogen Out-of-Plane (NOOP) Mechanism for Imine-Based Light-Driven Molecular Motors

Light-driven molecular motors have generated considerable interest due to their potential applications in material and biological systems. Recently, Greb and Lehn reported a new class of molecular motors, chiral N-alkyl imines, which undergo unidirectional rotation induced by light and heat. The mechanism of unidirectional motion in molecular motors containing a C═N group has been assumed to consist of photoinduced torsion about the double bond. In this work, we present a computational study of the photoisomerization dynamics of a chiral N-alkyl imine motor. We find that the location and energetics of minimal energy conical intersections (MECIs) alone are insufficient to understand the mechanism of the motor. Furthermore, a key part of the mechanism consists of out-of-plane distortions of the N atom (followed by isomerization about the double bond). Dynamic effects and out-of-plane distortions are critical to understand the observed (rather low) quantum yield for photoisomerization. Finally, our results provide hints as to how the photoisomerization quantum yield might be increased, improving the efficiency of this class of molecular motors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Structural basis of the Cope rearrangement and cyclization in hapalindole biogenesis

Hapalindole alkaloids are a structurally diverse class of cyanobacterial natural products defined by their varied polycyclic ring systems and diverse biological activities. These complex metabolites are generated from a common biosynthetic intermediate by the Stig cyclases in three mechanistic steps: a rare Cope rearrangement, 6-exo-trig cyclization, and electrophilic aromatic substitution. In this work, we report the structure of HpiC1, a Stig cyclase that catalyzes the formation of 12-epi-hapalindole U in vitro. The 1.5-Å structure revealed a dimeric assembly with two calcium ions per monomer and with the active sites located at the distal ends of the protein dimer. Mutational analysis and computational methods uncovered key residues for an acid-catalyzed [3,3]-sigmatropic rearrangement, as well as specific determinants that control the position of terminal electrophilic aromatic substitution, leading to a switch from hapalindole to fischerindole alkaloids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Activation Analysis for the Inboard Region of FNSF Using SERPENT

This study describes an application of the SERPENT 2 code with the TENDL-2017 nuclear data library and the latest available model features of the Fusion Energy System Studies–Fusion Nuclear Science Facility (FNSF), to evaluate the activation of components after shutdown at 1, 10, and 100 years, assuming a plant lifetime of 8.5 full-power years. The primary parameters evaluated include the specific activity, decay heat, and waste disposal rating (WDR). The specific activity and decay heat are calculated with SERPENT 2 using a 360-deg model of the FNSF, while the WDR is calculated and classified based on the waste disposal limits established by the U.S. Nuclear Regulatory Commission under 10 CFR 61.55 as well as by using the Fetter approach. A python-based script developed for a previous high-level waste classification and analysis study was implemented and adapted to this research to calculate the WDR by comparing nuclide concentrations to the values established in 10 CFR 61.55 to generate a waste classification for each component surveyed. As only three short-lived isotopes have limitations for classifications beyond Class A, of which only 63Ni is present in appreciable quantities, there is a limit to the amount that short-lived isotopes contribute to the most significant waste analyzed here. In most cases, a handful of long-lived isotopes can be problematic, such as 59Ni and 94Nb, for example, which are solely responsible for multiple Class C classifications. The results herein reported heavily depend on the specific materials and mass/volume fractions in the specific model used in this study, which has changed and evolved since the inception of the FNSF concept and past studies. Furthermore, the more significant contributions of this study may be the development of a modeling and simulation toolkit and a strategy to perform these calculations, so to help evaluate and optimize future fusion facilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

$\Sigma ^{+}$ production in pp collisions at $\sqrt{\textit{s}}=13$ TeV

The measurement of $\Sigma ^{+}$ production in pp collisions at $\sqrt{s}=13$ TeV is presented. The measurement is performed at midrapidity in both minimum-bias and high-multiplicity pp collisions at $\sqrt{s}=13$ TeV. The $\Sigma ^{+}$ is reconstructed via its weak-decay topology in the decay channel $\Sigma ^{+} \rightarrow \mathrm{{p}} + \pi ^{0}$ with $\pi ^{0} \rightarrow \gamma + \gamma$. In a novel approach, the neutral pion is reconstructed by combining photons that convert in the detector material with photons measured in the calorimeters. The transverse-momentum $(p_{\textrm{T}})$ distributions of the $\Sigma ^{+}$ and its rapidity densities dN/dy in both event classes are reported. The $p_{\textrm{T}}$ spectrum in minimum-bias collisions is compared to QCD-inspired event generators. The ratio of $\Sigma ^{+}$ to previously measured Λ baryons is in good agreement with calculations from the Statistical Hadronization Model. The high efficiency and purity of the novel reconstruction method for $\Sigma ^{+}$ presented here will enable future studies of the interaction of $\Sigma ^{+}$ with protons in the context of femtoscopic measurements, which could be crucial for understanding the equation of state of neutron stars.

Abualrob, I. J. [University of Houston] (ORCID:000↗

DOE OSTI2014

The OME Framework for genome-scale systems biology

The life sciences are undergoing continuous and accelerating integration with computational and engineering sciences. The biology that many in the field have been trained on may be hardly recognizable in ten to twenty years. One of the major drivers for this transformation is the blistering pace of advancements in DNA sequencing and synthesis. These advances have resulted in unprecedented amounts of new data, information, and knowledge. Many software tools have been developed to deal with aspects of this transformation and each is sorely needed. However, few of these tools have been forced to deal with the full complexity of genome-scale models along with high throughput genome-scale data. This particular situation represents a unique challenge, as it is simultaneously necessary to deal with the vast breadth of genome-scale models and the dizzying depth of high-throughput datasets. It has been observed time and again that as the pace of data generation continues to accelerate, the pace of analysis significantly lags behind. It is also evident that, given the plethora of databases and software efforts, it is still a significant challenge to work with genome-scale metabolic models, let alone next-generation whole cell models. We work at the forefront of model creation and systems scale data generation. The OME Framework was borne out of a practical need to enable genome-scale modeling and data analysis under a unified framework to drive the next generation of genome-scale biological models. Here we present the OME Framework. It exists as a set of Python classes. However, we want to emphasize the importance of the underlying design as an addition to the discussions on specifications of a digital cell. A great deal of work and valuable progress has been made by a number of communities towards interchange formats and implementations designed to achieve similar goals. While many software tools exist for handling genome-scale metabolic models or for genome-scale data analysis, no implementations exist that explicitly handle data and models concurrently. The OME Framework structures data in a connected loop with models and the components those models are composed of. This results in the first full, practical implementation of a framework that can enable genome-scale design-build-test. Over the coming years many more software packages will be developed and tools will necessarily change. However, we hope that the underlying designs shared here can help to inform the design of future software.

59 BASIC BIOLOGICAL SCIENCES↗