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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geophysical methods reveal the soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie

Abstract Purpose The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. Methods We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Results EMI revealed high spatial heterogeneities in ECa distribution across the land-lake interfaces, with higher values in the wetland and transition zones compared to the upland zone. Soil ECa maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa > 40 mS/m). ERT and GPR showed vertical variation in soil properties with clear stratigraphic boundaries, and correlation of ERT profiles with lithologs from piezometers revealed the stratigraphic units of silt–clay and till sequence down to 3.5 m depth which are consistent with the surficial geology of the study area. Conclusions These results validate the use of multiple geophysical methods for extrapolating soil properties and mapping stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Outcomes of Proton Therapy for the Treatment of Uveal Metastases

Purpose/Objective(s): Radiation therapy can be used to treat uveal metastases with the goal of local control and improvement of quality of life. Proton therapy can be used to treat uveal tumors efficiently and with expectant minimization of normal tissue injury. Here, we report the use of proton beam therapy for the management of uveal metastases. Methods and Materials: A retrospective chart review was made of all patients with uveal metastases treated at our institution with proton therapy between June 2002 and June 2012. Patient and tumor characteristics, fractionation and dose schemes, local control, and toxicities are reported. Results: Ninety patients were identified. Of those, 13 were excluded because of missing information. We report on 77 patients with 99 affected eyes with available data. Patients were 68% female, and the most common primary tumor was breast carcinoma (49%). The median age at diagnosis of uveal metastasis was 57.9 years. Serous retinal detachment was seen in 38% of treated eyes. The median follow-up time was 7.7 months. The median dose delivered to either eye was 20 Gy(relative biological effectiveness [RBE]) in 2 fractions. Local control was 94%. The median survival after diagnosis of uveal metastases was 12.3 months (95% confidence interval, 7.7-16.8). Death in all cases was secondary to systemic disease. Radiation vasculopathy, measured decreased visual acuity, or both was observed in 50% of evaluable treated eyes. The actuarial rate of radiation vasculopathy, measured decreased visual acuity, or both was 46% at 6 months and 73% at 1 year. The 6 eyes with documented local failure were successfully salvaged with retreatment. Conclusions: Proton therapy is an effective and efficient means of treating uveal metastases. Acutely, the majority of patients experience minor adverse effects. For longer-term survivors, the risk of retinal injury with vision loss increases significantly over the first year.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Scientific data interpolation with low dimensional manifold model

Here, we propose to apply a low dimensional manifold model to scientific data interpolation from regular and irregular samplings with a significant amount of missing information. The low dimensionality of the patch manifold for general scientific data sets has been used as a regularizer in a variational formulation. The problem is solved via alternating minimization with respect to the manifold and the data set, and the Laplace–Beltrami operator in the Euler–Lagrange equation is discretized using the weighted graph Laplacian. Various scientific data sets from different fields of study are used to illustrate the performance of the proposed algorithm on data compression and interpolation from both regular and irregular samplings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Synthetic control over orientational degeneracy of spacer cations enhances solar cell efficiency in two-dimensional perovskites

Two-dimensional perovskites have emerged as more intrinsically stable materials for solar cells. Chemical tuning of spacer organic cations has attracted great interest due to their additional functionalities. However, how the chemical nature of the organic cations affects the properties of two-dimensional perovskites and devices is rarely reported. Here we demonstrate that the selection of spacer cations (i.e., selective fluorination of phenethylammonium) affects the film properties of two-dimensional perovskites, leading to different device performance of two-dimensional perovskite solar cells (average n = 4). Structural analysis reveals that different packing arrangements and orientational disorder of the spacer cations result in orientational degeneracy and different formation energies, largely explaining the difference in film properties. This work provides key missing information on how spacer cations exert influence on desirable electronic properties and device performance of two-dimensional perovskites via the weak and cooperative interactions of these cations in the crystal lattice.

14 SOLAR ENERGY↗
DOE OSTI2020

Unveiling the strong interaction among hadrons at the LHC

One of the key challenges for nuclear physics today is to understand from first principles the effective interaction between hadrons with different quark content. First successes have been achieved using techniques that solve the dynamics of quarks and gluons on discrete space-time lattices. Experimentally, the dynamics of the strong interaction have been studied by scattering hadrons off each other. Such scattering experiments are difficult or impossible for unstable hadrons and so high-quality measurements exist only for hadrons containing up and down quarks. Here we demonstrate that measuring correlations in the momentum space between hadron pairs produced in ultrarelativistic proton–proton collisions at the CERN Large Hadron Collider (LHC) provides a precise method with which to obtain the missing information on the interaction dynamics between any pair of unstable hadrons. Specifically, we discuss the case of the interaction of baryons containing strange quarks (hyperons). We demonstrate how, using precision measurements of proton–omega baryon correlations, the effect of the strong interaction for this hadron–hadron pair can be studied with precision similar to, and compared with, predictions from lattice calculations. The large number of hyperons identified in proton–proton collisions at the LHC, together with accurate modelling of the small (approximately one femtometre) inter-particle distance and exact predictions for the correlation functions, enables a detailed determination of the short-range part of the nucleon-hyperon interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Ultrafast Photofragmentation of Ln(hfac)3 with a Proposed Mechanism for forming High Mass Fluorinated Products

Abstract The photo-induced dissociative-ionization of lanthanide complexes Ln(hfac) 3 (Ln = Pr, Er, Yb) is studied using intense ultrafast transform limited (TL) and linearly chirped laser pulses in a time-of-flight (TOF) mass spectrometry setup. Various fluorine and Ln-containing high-mass fragments were observed in this experiment, including the molecular parent ion, which have not been seen with previous studies relying on relatively long-duration laser pulses (i.e., ns or longer). These new high-mass observations provide important formerly missing information for deducing a set of photo-fragmentation mechanistic pathways for Ln(hfac) 3 . An overall ultrafast control mechanism is proposed by combining insights from earlier studies and the fragments observed in this research to result in three main distinct photo-fragmentation processes: (a) ligand-metal charge transfer, (b) CF 3 elimination, and (c) C-C bond rotation processes. We conclude that ultrafast dissociative-ionization could be a promising technique for generating high-mass fragments for potential use in material science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Quantum confinement in self-assembled two-dimensional nanoporous honeycomb networks at close-packed metal surfaces

Quantum confinement of a two-dimensional electron gas by supramolecular nanoporous networks is investigated using the boundary elements method based on Green’s functions for finite geometries and electron plane wave expansion for periodic systems. The “particle in a box” picture was analyzed for cases with selected symmetries that model previously reported architectures constructed from organic and metal-organic scattering centers confining surface state electrons of Ag(111) and Cu(111). First, by analyzing a series of cases with systematically defined parameters (scattering geometry, potentials, and effective broadening), we demonstrate how the scattering processes affect the properties of the confined electrons. For the features of the local density of states reported by scanning tunneling spectroscopy (STS), we disentangle the contributions of lifetime broadening and splitting of quantum well states due to coupling of neighboring quantum dots. For each system, we analyze the local electron density distribution and relate it to the corresponding band structure as calculated within the plane-wave expansion framework. Then, we address two experimental investigations, where in one case only STS data and in the other case mainly angle-resolved photoemission spectroscopy (ARPES) data were reported. In both cases, the experimental findings can be successfully simulated. Furthermore, the missing information can be complemented because our approach allows to correlate the information obtained by STS with that of ARPES. The combined analysis of several observations suggests that the scattering potentials created by the network originate primarily from the adsorbate-induced changes of the local surface dipole barrier.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Imaging outside the box: Resolution enhancement in X-ray coherent diffraction imaging by extrapolation of diffraction patterns

Coherent diffraction imaging is a high-resolution imaging technique whose potential can be greatly enhanced by applying the extrapolation method presented here. We demonstrate the enhancement in resolution of a non-periodical object reconstructed from an experimental X-ray diffraction record which contains about 10% missing information, including the pixels in the center of the diffraction pattern. A diffraction pattern is extrapolated beyond the detector area and as a result, the object is reconstructed at an enhanced resolution and better agreement with experimental amplitudes is achieved. The optimal parameters for the iterative routine and the limits of the extrapolation procedure are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler

Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate. We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Experimental evidence for nonspherical magnetic form factor in Ru 3 +

The Mott insulator ..alpha..-RuCl3 has generated great interest in the community due to its possible field-induced Kitaev quantum spin liquid state. Despite enormous effort spent trying to obtain the form of the low-energy Hamiltonian, there is currently no agreed upon set of parameters which is able to explain all of the data. A key piece of missing information lies in the determination of the magnetic form factor of Ru3+, particularly for a true quantitative treatment of inelastic neutron scattering data. Here we present the experimentally derived magnetic form factor of Ru3+ in the low spin 4d5 state using polarized neutron diffraction within the paramagnetic regime on high-quality single crystals of ..alpha..-RuCl3. We observe strong evidence of an anisotropic form factor, expected of the spin-orbit coupled jeff=12 ground state. We model the static magnetization density in increasing complexity from simple isotropic cases, to a multipolar expansion, and, finally, ab initio calculations of the generalized jeff=12 ground state. Comparison of both single ion models and inclusion of Cl- anions support the presence of hybridization of Ru3+ with the surrounding Cl- ligands.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Model Assessment Wizard (MAW)

SAND2026-18710O The Model Assessment Wizard (MAW) is a tool for evaluating ontologies and provides users with a comprehensive workbench for analysis. MAW features sub-modules for visualization, alignment, Shapes Constraint Language (SHACL) and Web Ontology Language (OWL) constraints, and simplification. Users can upload data, identify missing information, visualize ontologies, and update constraints. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Murdock, Jaimie [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2026

The ABCs of phase retrieval: Connecting the acronyms of scanning transmission electron microscopy

High-resolution scanning transmission electron microscopy (S/TEM) is an indispensable tool for characterizing the structure and properties of materials down to the atomic scale. Conventional S/TEM imaging, however, is limited by the phase problem, whereby the phase of the electron exit wave is lost upon detection. Recent advances in diffractive imaging and 4D-STEM have enabled a range of phase-retrieval techniques that computationally reconstruct the missing information encoded in the phase of the transmission function. These approaches offer improved dose efficiency and enhanced sensitivity to weakly scattering signals, extending quantitative imaging to beam-sensitive materials composed of light elements. In this work, we introduce the phase problem in electron microscopy and survey the diverse landscape of phase-retrieval techniques used in the field. Despite their many acronyms and algorithmic variations, these techniques share a common physical and mathematical foundation. We present a unified framework that connects these seemingly distinct methods, from parallax imaging and tilt-corrected bright-field (tcBF-STEM), to aberration-corrected bright-field (acBF-STEM), optimum bright-field (OBF-STEM) and single-sideband (SSB) ptychography, as well as first-moment integrated center of mass techniques (iCOM) and iterative ptychographic algorithms. Based on these insights, we discuss the opportunities and practical limitations of applying these methods across different materials systems, detector designs, and microscope configurations.Graphical abstractRepresentative electron microscopy configurations used for phase retrieval and diffractive imaging in S/TEM: (a) Zernike phase-contrast transmission electron microscopy (TEM), (b) small-convergence-angle four-dimensional scanning transmission electron microscopy (4D-STEM) for nanobeam-based phase reconstruction methods, and (c) large-convergence-angle 4D-STEM for ptychographic and related diffractive imaging techniques reviewed in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Algorithms for X-Ray Imaging of Single Particles

Our group develops image reconstruction algorithms and applies them to data collected with x-ray sources (synchrotron, XFEL) and electron microscopes. Some of the challenges we face are (1) extremely low signal levels and noise, (2) missing information (phase, sample orientation), and (3) large, very sparse datasets. We have made significant progress in all three of these areas in our DOE project. This final report highlights two results produced during the extension of the project that proved to be spectacular successes. Publications and software products generated over the entire project period are listed at the end of the report.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Modeling Electric Double Layer Effects on Charge Transfer at Flow Battery Electrode/Electrolyte Interfaces

The proposal aims to model interfacial processes associated with redox flow batteries (RFB) for grid/stationary storage, with the ultimate goal of helping design new electrolytes, electrodes, redox species, and interfaces. It addresses the linked problems of electric double layer (EDL) structure and electron transfer at model electrode/electrolyte interfaces. Fast electron transfer partly determines which redox species are viable for flow batteries (along with solubility, viscosity, etc.). Commercial systems apply aqueous, vanadium-based complexes, but other choices and organic electrolytes with larger voltage windows are the subject of active research at Sandia and elsewhere. The high salt concentration present in flow battery electrolytes yields non-trivial EDL consisting of solvents, counter-ions, co-ions, and redox species (at different charge states at the charging voltage threshold). EDL strongly influences electron transfer, especially when redox “mediators” are used. Modeling this key missing information is the main challenge. Our proposal seeks to apply Sandia’s LAMMPS molecular dynamics code. As a proof of principle, we study minimal model systems (graphite electrode, with ferrocene and fluornone as redox-active species). These model "catholyte" and "anolyte" molecules exhibit low reorganization energies (in the sense of Marcus theory) and are most amenable to EDL simulations which involve switching of redox states to mimic electron transfer. This study will enable future modeling and design of redox species relevant to flow batteries and redox mediators used at Sandia for other purposes.

25 ENERGY STORAGE↗