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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multicolor polymeric carbon dots: synthesis, separation and polyamide-supported molecular fluorescence

Multicolor carbon dots (CDs) have been developed recently and demonstrate great potential in bio-imaging, sensing, and LEDs. However, the fluorescence mechanism of their tunable colors is still under debate, and efficient separation methods are still challenging. Herein, we synthesized multicolor polymeric CDs through solvothermal treatment of citric acid and urea in formamide. Automated reversed-phase column separation was used to achieve fractions with distinct colors, including blue, cyan, green, yellow, orange and red. This work explores the physicochemical properties and fluorescence origins of the red, green, and blue fractions in depth with combined experimental and computational methods. Three dominant fluorescence mechanism hypotheses were evaluated by comparing time-dependent density functional theory and molecular dynamics calculation results to measured characteristics. We find that blue fluorescence likely comes from embedded small molecules trapped in carbonaceous cages, while pyrene analogs are the most likely origin for emission at other wavelengths, especially in the red. Also important, upon interaction with live cells, different CD color fractions are trafficked to different sub-cellular locations. Super-resolution imaging shows that the blue CDs were found in a variety of organelles, such as mitochondria and lysosomes, while the red CDs were primarily localized in lysosomes. These findings significantly advance our understanding of the photoluminescence mechanism of multicolor CDs and help to guide future design and applications of these promising nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Fast Reactor Methods Development Plan

Fast reactor design and safety analysis methods (computer codes) have been under development for approximately sixty years. During the first forty years, code development was driven by the immediate needs of operational facilities such as Experimental Breeder Reactors I and II and the Fast Flux Test Facility. Development was further driven by programmatic needs to support advanced reactor programs such as the Clinch River Breeder Reactor Project and the Advanced Liquid Metal Reactor Program. By the early 1990s, design and safety analysis codes had been well established to addressed the unique characteristics of fast reactors. During the subsequent twenty years, however, development was nearly stagnant as a result of U.S. Government policy changes that abruptly canceled nearly all of the fast reactor research in the United States. As a result, the state of fast reactor design and safety analysis methods has deteriorated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Biologically inspired approaches for biosurveillance anomaly detection and data fusion

This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large-scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Modeling Salt-Material Interactions in Molten Salt Reactors: Opportunities and Path Forward

A revitalized interest in molten salt reactor technology has resulted in a concerted effort across US based national laboratories and universities to investigate and overcome the remaining scientific and engineering questions facing the industry. This report provides a detailed description of the atomistic modelling techniques that could be employed to investigate the atomic-level forces and behaviors governing salt chemistry and thermodynamics, and the corrosion of container materials in these systems. The molten salt environment is broken down into three unique regions in which atomic behaviors are governed by separate processes and forces. A discussion is given on how each region can be modelled using different computational methods. The objective of the models presented for each region is to understand and quantify the individual atom-to-atom forces driving corrosion processes and changes in salt chemistry. Once these fundamental mechanisms are better understood, larger models, which connect the regions, can be designed to investigate the long-term non-equilibrium behaviors exhibited by these molten salt systems. With a large enough computational effort, employing the modeling techniques discussed in this report to supplement experimental investigations, a greater understanding of the degradation of salt-facing structural materials can be gained, enabling more accurate lifetime prediction, greater safety and regulatory compliance, and faster material innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Stochastic level-set variational implicit-solvent approach to solute-solvent interfacial fluctuations

Recent years have seen the initial success of a variational implicit-solvent model (VISM), implemented with a robust level-set method, in capturing efficiently different hydration states and providing quantitatively good estimation of solvation free energies of biomolecules. The level-set minimization of the VISM solvation free-energy functional of all possible solute-solvent interfaces or dielectric boundaries predicts an equilibrium biomolecular conformation that is often close to an initial guess. In this work, we develop a theory in the form of Langevin geometrical flow to incorporate solute-solvent interfacial fluctuations into the VISM. Such fluctuations are crucial to biomolecular conformational changes and binding process. We also develop a stochastic level-set method to numerically implement such a theory. We describe the interfacial fluctuation through the “normal velocity” that is the solute-solvent interfacial force, derive the corresponding stochastic level-set equation in the sense of Stratonovich so that the surface representation is independent of the choice of implicit function, and develop numerical techniques for solving such an equation and processing the numerical data. We apply our computational method to study the dewetting transition in the system of two hydrophobic plates and a hydrophobic cavity of a synthetic host molecule cucurbit[7]uril. Numerical simulations demonstrate that our approach can describe an underlying system jumping out of a local minimum of the free-energy functional and can capture dewetting transitions of hydrophobic systems. In the case of two hydrophobic plates, we find that the wavelength of interfacial fluctuations has a strong influence to the dewetting transition. In addition, we find that the estimated energy barrier of the dewetting transition scales quadratically with the inter-plate distance, agreeing well with existing studies of molecular dynamics simulations. Our work is a first step toward the inclusion of fluctuations into the VISM and understanding the impact of interfacial fluctuations on biomolecular solvation with an implicit-solvent approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Integrating AI with physics-based hydrological models and observations for insightinto changing climate and anthropogenic impacts

Focal Areas: Advanced computational methods that integrate AI, physics, and observations to provide predictive landscape hydrological modeling over large areas (regional, continental, worldwide) while incorporating increasingly available high-resolution data from drones, lidar and satellite. Science Challenge: Landscape data is available at finer scales than can be used in physics-based hydrological (PBH) models for regional or continental terrestrial water modeling. Thus, we throw away observable detail to achieve computability. We argue that integration of AI with PBH models and observed data can be used to provide upscaling for predictive models that are computable, retain physical conservation properties, and represent the fine-scale features that affect complex flow physics through both natural and urban environments. Developing such next-generation capabilities requires outside-the-box thinking that melds the different approaches of AI modeling, PBH modeling, and observation across multiple scales from local drones to satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Energy stable discontinuous Galerkin methods for Maxwell's equations in nonlinear optical media

The propagation of electromagnetic waves in general media is modeled by the time-dependent Maxwell's partial differential equations (PDEs), coupled with constitutive laws that describe the response of the media. In this work, we focus on nonlinear optical media whose response is modeled by a system of first order nonlinear ordinary differential equations (ODEs), which include a single resonance linear Lorentz dispersion, and the nonlinearity comes from the instantaneous electronic Kerr response and the residual Raman molecular vibrational response. To design efficient, accurate, and stable computational methods, we apply high order discontinuous Galerkin discretizations in space to the hybrid PDE-ODE Maxwell system with several choices of numerical fluxes, and the resulting semi-discrete methods are shown to be energy stable. Under some restrictions on the strength of the nonlinearity, error estimates are also established. When we turn to fully discrete methods, the challenge to achieve provable stability lies in the temporal discretizations of the nonlinear terms. To overcome this, novel strategies are proposed to treat the nonlinearity in our model within the framework of the second-order leap-frog and implicit trapezoidal time integrators. The performance of the overall algorithms are demonstrated through numerical simulations of kink and antikink waves, and third-harmonic generation in soliton propagation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Generalized complement operators and applications in some semirings

Generalized complement operators on the semiring of all Boolean matrices as semilattice homomorphisms are considered. Some applications in solving equations on the set Bn of all binary relations are developed. In particular the structure of B3 is investigated by computer methods. Specific properties of the subsemigroup generated by all irregular relations in B3 are found. Stochastic experiments on the monoid Bn were made. The frequency of irregular elements as well as those of solvable equations depending on n is examined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Combined Spin-Flip and IP/EA Approach for Handling Spin and Spatial Degeneracies: Application to Double Exchange Systems

Many multiconfigurational systems, such as single-molecule magnets, are difficult to study using traditional computational methods due to the simultaneous existence of both spin and spatial degeneracies. In this work, a new approach termed n -spin-flip ionization potential/electron affinity ( n SF-IP or n SF-EA) is introduced which combines the spin-flip method of Anna Krylov with particle-number changing IP/EA methods. We demonstrate the efficacy of the approach by applying it to the strongly correlated N 2 + , as well as several double exchange systems. We also demonstrate that when these systems are well-described by a double exchange model Hamiltonian, only 1SF-IP/EA is required to extract the double exchange parameters and accurately predict energies for the low-spin states. This significantly reduces the computational effort for studying such systems. The results of including additional excitations (using a RAS- n SF-IP/EA scheme) are also researched, with particular emphasis on hole and particle excitations.

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

A three dimensional field formulation, and isogeometric solutions to point and line defects using Toupin's theory of gradient elasticity at finite strains

In this study, we present a field formulation for defects that draws from the classical representation of the cores as force dipoles. We write these dipoles as singular distributions. Exploiting the key insight that the variational setting is the only appropriate one for the theory of distributions, we arrive at universally applicable weak forms for defects in nonlinear elasticity. Remarkably, the standard, Galerkin finite element method yields numerical solutions for the elastic fields of defects that, when parameterized suitably, match very well with classical, linearized elasticity solutions. The true potential of our approach, however, lies in its easy extension to generate solutions to elastic fields of defects in the regime of nonlinear elasticity, and even more notably for Toupin's theory of gradient elasticity at finite strains (Toupin Arch. Ration. Mech. Anal., 11 (1962) 385). In computing these solutions we adopt recent numerical work on an isogeometric analytic framework that enabled the first three-dimensional solutions to general boundary value problems of Toupin's theory (Rudraraju et al. Comput. Methods Appl. Mech. Eng., 278 (2014) 705). We first present exhaustive solutions to point defects, edge and screw dislocations, and a study on the energetics of interacting dislocations. Then, to demonstrate the generality and potential of our treatment, we apply it to other complex dislocation configurations, including loops and low-angle grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A spectroscopic, structural, and computational study of Ag–oxo interactions in Ag + /UO 2 2+ complexes

For this work, twelve novel Ag + /UO 2 2+ heterometallic complexes have been prepared and characterized via structural, spectroscopic, and computational methods to probe the effects of Ag–oxo interactions on bonding and photophysical properties of the uranyl cation. Structural characterization reveals Ag–oxo interaction distances ranging from 2.475(3) Å to 4.287(4) Å. These interactions were probed using luminescence and Raman spectroscopy which displayed little effect on the luminescence intensity and the energy of the Raman active U═O symmetric stretch peak as compared to previously reported Pb–oxo interactions. Computational efforts via density functional theory-based natural bond orbital analysis revealed that the highest stabilization energy associated with the Ag–oxo interaction had a value of only 11.03 kcal mol –1 and that all other energy values fell at 7.05 kcal mol –1 or below indicating weaker interactions relative to those previously reported for Pb 2+ /UO 2 2+ heterometallic compounds. In contrast, quantum theory of atoms in molecules analysis of bond critical point electron density values indicated higher electron density in Ag–oxo interactions as compared to Pb–oxo interactions which suggests more covalent character with the Ag + . Overall, this data indicates that Ag + has a less significant effect on UO 2 2+ bonding and photophysical properties as compared to other Pb 2+ , likely due to the high polarizability of the cation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Develop and Connect TRISO Failure Analysis and Uncertainty Quantification to Fission Product Release Calculation Capability

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Stress-based failure probability has been developed and analyzed to assess the integrity of tri-structural isotropic (TRISO) fuel particles during fuel life cycles. While simple, stress-based approaches to failure probability leveraging the Weibull statistical distribution entails a number of drawbacks when stress concentration occurs near crack tips, including finite element mesh size dependency. In this report, we use an interaction integral approach to the computation of stress intensity factors in functionally graded materials (FGM) for axisymmetric models. The inner pyrolytic carbon (IPyC) cracking induced silicon carbide (SiC) failure is one of the dominated failure modes in TRISO failure analysis. In this study, we consider a crack in the IPyC layer perpendicular to the SiC layer. The interface between these two TRISO layers is considered to be porous, which we simulate considering a transition of mechanical properties over the porous length. These aspects are considered in the computation of stress intensity factor (SIF) from a fracture mechanics approach and compared with the known stress-based failure probability approach.

42 ENGINEERING↗
DOE OSTI2022

PCP consensus protein/peptide alphavirus antigens stimulate broad spectrum neutralizing antibodies

Vaccines based on proteins and peptides may be safer and if calculated based on many sequences, more broad-spectrum than those designed based on single strains. Physicochemical Property Consensus (PCP con ) alphavirus (AV) antigens from the B-domain of the E2 envelope protein were designed, synthesized recombinantly and shown to be immunogenic (i.e. sera after inoculation detected the antigen in dotspots and ELISA). Antibodies in sera after inoculation with B-region antigens based on individual AV species (eastern or Venezuelan equine encephalitis (EEEV con , VEEV con ), or chikungunya (CHIKV con ) bound only their cognate protein, while those designed against multiple species (Mosaik con and EVC con ) recognized all three serotype specific antigens. The VEEV con and EEEV con sera only showed antiviral activity against their related strains (in plaque reduction neutralization assays (PRNT 50/80 ). Peptides designed to surface exposed areas of the E2-A-domain of CHIKV con were added to CHIKV con inocula to provide anti-CHIKV antibodies. EVC con , based on three different alphavirus species, combined with E2-A-domain peptides from AllAV con , a PCPcon of 24 diverse AV, generated broad spectrum, antiviral antibodies against VEEV, EEEV and CHIKV, AV with less than 35% amino acid identity to each other (>65% diversity). This is a promising start to a molecularly defined vaccine against all AV. A further study with these antigens can illuminate what areas are most important for a robust immune response, resistant to mutations in rapidly evolving viruses. The validated computational methods can also be used to design broad spectrum antigens against many other pathogen families.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hydrogen-Bonded Organic Frameworks: A Rising Class of Porous Molecular Materials

Hydrogen-bonded organic frameworks (HOFs) are a class of porous molecular materials that rely on the assembly of organic building blocks by means of hydrogen-bonding interactions to form two-dimensional (2D) and three-dimensional (3D) crystalline networks. The reversible nature of the hydrogen-bond formation endows HOFs with the attributes of solution processability and simple regeneration. High-quality single crystals of HOFs can be grown easily for unambiguous superstructure determination by single-crystal X-ray diffraction, which is crucial for the elucidation of superstructure–property relationships. During the past decade, considerable progress has been achieved in realizing stable HOFs with permanent porosities by focusing on the design of molecular building blocks in order to introduce rigidity, auxiliary [π···π] interactions, and interpenetration of their frameworks to sustain the extended networks. The applications of HOFs are far-reaching, spanning catalysis, energy, and biomedical products as well as the storage and separation of fine chemicals. In this paper, we, first of all, provide an overview of the chronological development of HOFs, starting from the seminal work by Marsh and Duchamp in 1969 on the crystal superstructure of the hydrogen-bonded networks of trimesic acid. We identify the development of novel hydrogen-bonding motifs such as diaminotriazine (DTA), the introduction of the concept of molecular tectonics, and the establishment of permanent porosity in HOFs as being some of the milestones, which incentivized the current burgeoning research endeavors on developing HOFs as multifunctional materials. This Account is focused primarily on surveying the strategies for constructing porous 3D HOFs based on organic building blocks with peripheral carboxyl groups. These strategies are presented in the following categories: (1) the polycatenation of 2D networks by trigonal building blocks to form global 3D frameworks, (2) the utilization of building blocks with 3D geometries—tetrahedral and trigonal prismatic—that are predisposed to form 3D networks, and (3) the docking by shape-fitting of geometrically labile building blocks. We emphasize how the molecular geometry of the building blocks plays an important role in modulating the superstructures of extended frameworks so as to address specific applications. Recognizing that the in silico design of HOFs is the ultimate goal of researchers in this field, we also discuss the recent advances in superstructure prediction that lead to the formation of porous supramolecular crystals and assess the complications in implementing computational methods for HOFs with complex superstructures. We hope this Account will inspire the development of new supramolecular designs and creative approaches to crystal engineering that aid and abet the assembly of multifunctional HOFs with customizable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Modeling Mercury in Proteins

Mercury (Hg) is a naturally occurring element that is released into the biosphere both by natural processes and anthropogenic activities. Although its reduced, elemental form Hg(0) is relatively non-toxic, other forms such as Hg2+ and, in particular, its methylated form, methylmercury, are toxic, with deleterious effects on both ecosystems and humans. Microorganisms play important roles in the transformation of mercury in the environment. Inorganic Hg2+ can be methylated by certain bacteria and archaea to form methylmercury. Conversely, bacteria also demethylate methylmercury and reduce Hg2+ to relatively inert Hg(0). Transformations and toxicity occur as a result of mercury interacting with various proteins. Clearly, then, understanding the toxic effects of mercury and its cycling in the environment requires characterization of these interactions. Computational approaches are ideally suited to studies of mercury in proteins because they can provide a detailed picture and circumvent issues associated with toxicity. Here we describe computational methods for investigating and characterizing how mercury binds to proteins, how inter- and intra-protein transfer of mercury is orchestrated in biological systems, and how chemical reactions in proteins transform the metal. We describe quantum chemical analyses of aqueous Hg(II), which reveal critical factors that determine ligand binding propensities. We then provide a perspective on how we used chemical reasoning to discover how microorganisms methylate mercury. We also highlight our combined computational and experimental studies of the proteins and enzymes of the mer operon, a suite of genes that confers mercury resistance in many bacteria. Lastly, we place work on mercury in proteins in the context of what is needed for a comprehensive multi-scale model of environmental mercury cycling.

Smith, Jeremy C↗
DOE OSTI2020

Thermal tomography 3D imaging of additively manufactured metallic structures

Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with the laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer and through reconstruction of data obtained from experimental measurements. Thermal tomography reconstructions of structure shape and dimensions are shown for the Inconel 718 AM structure which has variations in the horizontal plane but is uniform along the depth dimension. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and the Inconel 718 plate is produced with the LPBF method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBHs have the same sizes as the imprinted defects in the LPBF specimens but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects and allow for estimation of their sizes and locations. Results of this study demonstrate that thermal tomography can be used for visualization and quality control in AM.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Quantum-inspired computational imaging

Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. Finally, these methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.

97 MATHEMATICS AND COMPUTING↗