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Transport properties in dilute UN ( X ) solid solutions ( X = Xe , Kr )
Uranium nitride (UN) is a candidate fuel for current GEN III fission reactors, for which it is investigated as an accident-tolerant fuel, as well as for future GEN IV reactors. In this study, we investigate the kinetic properties of gas fission products (Xe and Kr) in UN. Binding and migration energies are obtained using density functional theory, with an added Hubbard correlation to model f electrons, and the occupation matrix control scheme to avoid metastable states. These energies are then used as input for the self-consistent mean field method which enables to determine transport coefficients for vacancy-mediated diffusion of Xe and Kr on the U sublattice. The magnetic ordering of the UN structure is explicitly taken into account, for both energetic and transport properties. Solute diffusivities are compared with experimental measurements and the effect of various parameters on the theoretical model is carefully investigated. We find that kinetic correlations are very strong in this system, and that despite atomic migration anisotropy, macroscopic solute diffusivities show limited anisotropy. Our model indicates that the discrepancy between experimental measurements probably results from different irradiation conditions, and hence different defect concentrations.
Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation
Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\ models the heat diffusion traces along the entire time scale, so it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.
Using Homogenized Models to Explore the Effect of Fracture Densities on Weathering
Despite its importance, only a few researchers have incorporated the effects of fracturing into models of reactive transport for rock weathering. Here we explore 2D simulations that describe weathering under conditions of diffusive and advective transport within heterogeneous media consisting of rocky blocks and fractures. In our simulations, the Darcy velocities vary in space and time and depend on weathering processes within the rock matrix. We explore simulations with saturated and unsaturated flow for weathering bedrock that consists of blocks separated by inert or weathered material. The simulations show that a simplified homogenized model can approximate exact solutions for some of the simulated columns and hills and can allow exploration of coupling between flow and reaction in fractured rock. These hillslope simulations document that, even in the presence of 2D water flow, i) an increase in fracture density results in faster weathering advance rates; and ii) the water table locates deeper for a rock system that is weathered and fractured rather than weathered and unfractured. Some of these patterns have also been observed for natural systems. But these simulations also highlight how simplified models that do not use appropriate averaging of heterogeneities can be inaccurate in predicting weathering rate for natural systems. For example, if water flows both vertically and laterally through the vadose zone of a hill, then a prediction of the depth of regolith that is based on modeling strictly unidirectional downward infiltration will be unrealistically large. Likewise, if the fracture density observed near the land surface is used in a model to predict depth of weathering for a system where the fracture density decreases downward, the model will overestimate regolith depth. Learning how to develop accurately homogenized models could thus enable better conceptual models and predictions of weathering advance in natural systems.
High Chromium Cast Irons: Destabilized-Subcritical Secondary Carbide Precipitation and Its Effect on Hardness and Wear Properties
This work evaluates the effect of a destabilization treatment combined with a subcritical diffusion (SCD) and a subsequent quenching (Q) steps on precipitation of secondary carbides and their influence on the wear properties of HCCI (16%Cr). The destabilization of the austenite at high temperature leads to a final microstructure composed of eutectic and secondary carbides, with an M{sub 7}C{sub 3} nature, embedded in a martensitic matrix. An improved wear resistance was observed in the SCD + Q samples in comparison with the Q one, which was attributed to the size of secondary carbides.
Interfacial microstructure evolution and weld formation during ultrasonic welding of Al alloy to Cu
Highlights: • Surface oxide layer was fractured and dispersed around the weld interface during ultrasonic welding of Al alloy to Cu. • Mechanical mixing around the weld interface was caused by compressive deformation with ultrasonic vibration. • Once micro-bonds were generated between Al and Cu, shear deformation occurred around the weld interface. • Enhanced Cu diffusion into Al in the severely deformed region significantly contributed to the weld formation. - Abstract: Dissimilar welds of Al alloy 1050 to Cu were prepared via ultrasonic spot welding in order to understand the formation of welds and interfacial microstructures. To observe oxide layer behavior at the weld interface, the anodized Al alloy 1050 was also welded to Cu. Mechanical mixing and material flow during ultrasonic welding broke and dispersed the oxide layers into the Al matrix. This material flow is attributed to compressive deformation that occurred due to ultrasonic vibrations. Direct bonding in the Al/Cu region increased with welding time. Once micro-bonds were generated between Al and Cu, the Al matrix in the vicinity of the weld interface was severely deformed in the direction of the ultrasonic vibrations. Consequently, a recrystallized microstructure with shear texture was formed in the Al matrix. Deformation heating and severe shear deformation formed Al{sub 2}Cu intermetallic compound layers at the weld interface. The growth rate of the Al{sub 2}Cu layer was much faster than had previously been estimated based upon the peak temperature and ultrasonic welding heating time. This is likely due to the increased rate of Cu diffusion into Al in the severely deformed region.
3D-printed electrode, gas diffusion electrode (GDE) and gas diffusion layer (GDL) for fuel cell applications
An electrocatalytically active ink composition is used with an additive manufacturing process, such as 3D printing, to produce electrodes having consistent, adaptable, and high surface area structures. The electrocatalytically active ink composition includes a mixed powdered precursor and a polymer matrix. The mixed powdered precursor includes a carbon source, a dopant source, and/or a metal-containing catalyst. The material and electrochemical properties of the ink composition may facilitate 3D printing of electrochemically active electrodes for energy conversion and storage devices, and may allow fine-tuning of macro- and microstructures to develop electrodes having improved activity and efficiency.
Microstructure and microchemistry study of irradiation-induced precipitates in proton irradiated ZrNb alloys
Proton irradiation induced Nb redistribution in Zr-xNb alloys (x = 0.4, 0.5, 1.0 wt%) has been investigated using scanning transmission electron microscopy/energy dispersive X-ray spectroscopy (STEM/EDS). Zr-xNb alloys are mainly composed of Zr matrix, native Zr–Nb–Fe phases, and β-Nb precipitates. After 2 MeV proton irradiation at 350 °C, a decrease of Nb content in native precipitates, as well as irradiation-induced precipitation of Nb-rich platelets (135 69 nm long and 27 12 nm wide) were found. Nb-rich platelets and Zr matrix form the Burgers orientation relationship, [$1\bar{1}1$]//[$2\bar{1}\bar{1}0$] and (011)//(0002). The platelets were found to be mostly coherent with the matrix with a few dislocations near the ends of the precipitate. The coherent strain field has been measured in the matrix and platelets by the 4D-STEM technique. Here, the growth of Nb-rich platelets is mainly driven by coherency and dislocation-induced strain fields. Irradiation may both enhance the diffusion and induce segregation of interstitial Nb to the ends of the irradiation induced platelets, further facilitating their growth.
A Low-Rank Multigrid Method for the Stochastic Steady-State Diffusion Problem
We study a multigrid method for solving large linear systems of equations with tensor product structure here. Such systems are obtained from stochastic finite element discretization of stochastic partial differential equations such as the steady-state diffusion problem with random coefficients. When the variance in the problem is not too large, the solution can be well approximated by a low-rank object. In the proposed multigrid algorithm, the matrix iterates are truncated to low rank to reduce memory requirements and computational effort. The technique is proved convergent with an analytic error bound. Numerical experiments show its effectiveness in solving the Galerkin systems compared to the original multigrid solver, especially when the number of degrees of freedom associated with the spatial discretization is large.
A Statistical Interpolation Code for Ocean Analysis and Forecasting
Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .
RASPA3
RASPA3, a molecular simulation code for computing adsorption and diffusion in nanoporous materials and thermodynamic and transport properties of fluids. It implements force field based classical Monte Carlo/molecular dynamics in various ensembles. RASPA3 is rewritten from the ground up in C++23 with speed and code readability in mind. Transition-matrix Monte Carlo is added to compute the density of states and free energies. The Monte Carlo code for rigid molecules is based on quaternions, and the atomic positions needed in the energy evaluation are recreated from the center of mass position and quaternion orientation. The expanded ensemble methodology for fractional molecules, with a scaling parameter λ between 0 and 1, now also keeps track of analytic expressions of dU/dλ, allowing independent verification of the chemical potential using thermodynamic integration. The source code is freely available under the MIT license on GitHub.
Cellulose Structural Polymorphism in Plant Primary Cell Walls Investigated by High-Field 2D Solid-State NMR Spectroscopy and Density Functional Theory Calculations
The native cellulose of bacterial, algal, and animal origins has been well studied structurally using X-ray and neutron diffraction and solid-state NMR spectroscopy, and is known to consist of varying proportions of two allomorphs, Iα and Iβ, which differ in hydrogen bonding, chain packing, and local conformation. In comparison, cellulose structure in plant primary cell walls is much less understood because plant cellulose has lower crystallinity and extensive interactions with matrix polysaccharides. Here we have combined two-dimensional magic-angle-spinning (MAS) solid-state nuclear magnetic resonance (solid-state NMR) spectroscopy at high magnetic fields with density functional theory (DFT) calculations to obtain detailed information about the structural polymorphism and spatial distributions of plant primary-wall cellulose. 2D 13 C– 13 C correlation spectra of uniformly 13 C-labeled cell walls of several model plants resolved seven sets of cellulose chemical shifts. Among these, five sets (denoted a–e) belong to cellulose in the interior of the microfibril while two sets (f and g) can be assigned to surface cellulose. Importantly, most of the interior cellulose 13 C chemical shifts differ significantly from the 13 C chemical shifts of the Iα and Iβ allomorphs, indicating that plant primary-wall cellulose has different conformations, packing, and hydrogen bonding from celluloses of other organisms. 2D 13 C– 13 C correlation experiments with long mixing times and with water polarization transfer revealed the spatial distributions and matrix-polysaccharide interactions of these cellulose structures. Celluloses f and g are well mixed chains on the microfibril surface, celluloses a and b are interior chains that are in molecular contact with the surface chains, while cellulose c resides in the core of the microfibril, outside spin diffusion contact with the surface. Interestingly, cellulose d, whose chemical shifts differ most significantly from those of bacterial, algal, and animal cellulose, interacts with hemicellulose, is poorly hydrated, and is targeted by the protein expansin during wall loosening. To obtain information about the C6 hydroxymethyl conformation of these plant celluloses, we carried out DFT calculations of 13 C chemical shifts, using the Iα and Iβ crystal structures as templates and varying the C5–C6 torsion angle. Comparison with the experimental chemical shifts suggests that all interior cellulose favor the tg conformation, but cellulose d also has a similar propensity to adopt the gt conformation. In conclusion, these results indicate that cellulose in plant primary cell walls, due to their interactions with matrix polysaccharides, and has polymorphic structures that are not a simple superposition of the Iα and Iβ allomorphs, thus distinguishing them from bacterial and animal celluloses.
Overcoming element quality dependence of finite elements with adaptive extended stencil FEM (AES‐FEM)
Summary The finite element methods (FEMs) are important techniques in engineering for solving partial differential equations, but they depend heavily on element shape quality for stability and good performance. In this paper, we introduce the Adaptive Extended Stencil Finite Element Method (AES‐FEM) as a means for overcoming this dependence on element shape quality. Our method replaces the traditional basis functions with a set of generalized Lagrange polynomial basis functions , which we construct using local weighted least‐squares approximations. The method preserves the theoretical framework of FEM and allows imposing essential boundary conditions and integrating the stiffness matrix in the same way as the classical FEM. In addition, AES‐FEM can use higher‐degree polynomial basis functions than the classical FEM, while virtually preserving the sparsity pattern of the stiffness matrix. We describe the formulation and implementation of AES‐FEM and analyze its consistency and stability. We present numerical experiments in both 2D and 3D for the Poisson equation and a time‐independent convection–diffusion equation. The numerical results demonstrate that AES‐FEM is more accurate than linear FEM, is also more efficient than linear FEM in terms of error versus runtime, and enables much better stability and faster convergence of iterative solvers than linear FEM over poor‐quality meshes. Copyright © 2016 John Wiley & Sons, Ltd.
Characterization of shale using Helium and Argon at high pressures
In order to estimate the shale gas in place and the eventual recovery during shale gas operations, one of the key requirements is to accurately characterize the shale’s petrophysical and transport properties such as porosity, permeability, diffusivity, and storage capacity. Despite the many efforts reported in the technical literature aiming to provide an improved understanding of the complex pore structures and the associated fluid flow in gas shales, complete characterization of organic-rich shale samples still poses a big challenge. Here, we have characterized mass transfer and sorption in shale at different length scales using Helium (He) and Argon (Ar) as probe gases. Thermogravimetric analysis (TGA) with a shale cube of ~1 cm 3 in size and gas expansion experiments with a full-diameter core (3.5” in diameter) were used to measure sorption kinetics/isotherms and mass transfer, respectively. Both samples are from the same depth/location in the Marcellus shale formation. The TGA steady-state technique was initially used to generate excess sorption isotherms for Ar, while dynamic TGA experiments were used to study its sorption kinetics. The TGA experiments demonstrate that Ar, which has a similar sorption potential as Methane, but is generally assumed to be inert, adsorbs onto the surfaces of the mesoporous and microporous regions of the shale samples according to a Langmuir-type behavior. Helium expansion experiments, on the full-diameter core, were used to measure the overall porosity, on the basis that He is a non-sorbing and inert gas as compared to Ar. The He expansion experiments, furthermore, allow us to delineate the mass transfer of gas across the inherent hierarchy of pore sizes, including macropores (macro- and microcracks), mesopores and micropores. Similar expansion experiments were also performed with Ar to study the combined impact of mass transfer and sorption. A triple-porosity model (TPM) was utilized to interpret the He expansion experiments with the shale core and to extract (estimate) relevant transport parameters. We report and compare here the diffusivities and permeabilities of the whole core for both He and Ar, as calculated from the modeling and fitting of the experimental data. On the premise that the shale cube is representative of the matrix region of the core, the Ar sorption kinetics from the cube experiments were subsequently combined with the transport parameters extracted from the He experiments to predict the behavior of the Ar expansion test with the full-diameter core. An excellent agreement is observed between the model predictions and the experimental data. The experimental observations and their interpretation indicate that one must be cautious when using Ar to estimate the true porosity and permeability of shales. In addition, we demonstrate that He and Ar probe gases, when used in tandem, can be employed effectively as a tool to characterize shales in terms of mass transfer and sorption dynamics across scales.
Signal-to-noise assessment for diffusion tensor imaging with single data set and validation using a difference image method with data from a multicenter study
Purpose: To describe a quantitative method for determination of SNR that extracts the local noise level using a single diffusion data set. Methods: Brain data sets came from a multicenter study (eight sites; three MR vendors). Data acquisition protocol required b = 0, 700 s/mm{sup 2}, fov = 256 × 256 mm{sup 2}, acquisition matrix size 128 × 128, reconstruction matrix size 256 × 256; 30 gradient encoding directions and voxel size 2 × 2 × 2 mm{sup 3}. Regions-of-interest (ROI) were placed manually on the b = 0 image volume on transverse slices, and signal was recorded as the mean value of the ROI. The noise level from the ROI was evaluated using Fourier Transform based Butterworth high-pass filtering. Patients were divided into two groups, one for filter parameter optimization (N = 17) and one for validation (N = 10). Six white matter areas (the genu and splenium of corpus callosum, right and left centrum semiovale, right and left anterior corona radiata) were analyzed. The Bland–Altman method was used to compare the resulting SNR with that from the difference image method. The filter parameters were optimized for each brain area, and a set of “global” parameters was also obtained, which represent an average of all regions. Results: The Bland–Altman analysis on the validation group using “global” filter parameters revealed that the 95% limits of agreement of percent bias between the SNR obtained with the new and the reference methods were −15.5% (median of the lower limit, range [−24.1%, −8.9%]) and 14.5% (median of the higher limits, range [12.7%, 18.0%]) for the 6 brain areas. Conclusions: An FT-based high-pass filtering method can be used for local area SNR assessment using only one DTI data set. This method could be used to evaluate SNR for patient studies in a multicenter setting.
Limiting Retained Austenite Decomposition in Quenched and Tempered Steels: Influences of Rapid Tempering and Silicon
Tempering reactions are critical to microstructure and property control in martensitic steels. Here, retained austenite decomposition and cementite precipitation are monitored using Mössbauer spectroscopy in 4340 and 300-M steel under conventional and rapid tempering conditions. Tempering times are compared at a constant tempered hardness by increasing tempering temperatures associated with short time conditions to achieve equivalent matrix softening to that of longer tempering times. Time-temperature combinations that provide equivalent tempered hardness generated microstructures with similar dislocation densities and cementite precipitation fractions; these mechanisms are controlled by self-diffusion. However, systematic differences in retained austenite content were observed at a given degree of softening, where shorter tempering times exhibited higher levels of retained austenite compared to more conventional conditions. At low temperatures, the differences in retained austenite preservation between explored time-temperature conditions are attributed to corresponding differences in carbon diffusion distance (in austenite), the controlling diffusional process of retained austenite decomposition. At higher temperatures, retained austenite decomposition exhibits C-curve kinetic behavior in 4340. Thus, reduced thermodynamic driving force for cementite and ferrite formation at higher temperature is believed to play a role in restricting retained austenite decomposition within some short-time, high temperature tempering regimes. The addition of silicon pushes cementite precipitation and retained austenite decomposition to higher temperatures, although retained austenite decomposition is suppressed to a greater extent than cementite precipitation. Potential is illustrated for coupling rapid tempering with silicon alloying to produce appreciably tempered martensite (~490 HV) with relatively less retained austenite decomposition compared to conventional tempering conditions.
Evaluating the oxidative, photothermal and electrical stability of colloidal nanocrystal solids
IV-VI quantum dot (QD) solids are a novel class of granular electronic materials with great technological potential (e.g., in photodetectors, field-effect transistors (FETs), and solar cells), but their oxidative and thermal instability present a barrier to practical applications [1]. Poor stability is a fundamental issue facing many nanoscale materials due to high surface area and surface energy. Basic studies are needed to elucidate the most important mechanisms of degradation and develop robust countermeasures if QD materials are to become technologically important. This project determined the degradation mechanisms of IV-VI QD solids (primarily PbSe and PbS) and introduced new chemical strategies to drastically improve their performance, stability, and operating lifetimes [2-5]. Our approach was based on (1) detailed testing of QD thin film materials (principally FETs and solar cells) as a function of oxidative and thermal stress, and (2) the use of organic and inorganic approaches to link the QDs into strongly electronically coupled, high-mobility films, prevent their oxidation, and eliminate internal degrees of freedom that lead to film instability and degradation in response to electrical and thermal stress. Stability against oxidation and thermal degradation was the major focus of this project. We have evaluated the stability of QD thin films and interfaces at temperatures less than 100°C (the regime most relevant to solar and transistor applications). Low-temperature oxidation and sintering of QD films have been investigated using optical absorption spectroscopy, Fourier transform infrared spectroscopy (FTIR), current-voltage scanning of transistors and solar cells, X-ray photoelectron spectroscopy, and scanning Kelvin probe microscopy (SKPM). SKPM was used to map the potential profiles of operating QD FETs and solar cells as a function of bias and illumination, which provides detailed information on how the work functions, potential drops and electric field within these devices determine device operation, and set the stage for future studies targeted at understanding and preventing device failure. We pursued two strategies to fabricate QD films with stable electrical characteristics: (1) the use of robust molecular surface ligands [2], and (2) “matrix engineering,” i.e., infilling the QD solid with metal oxide or metal sulfide matrices by low-temperature atomic layer deposition (ALD) to passivate surface states, prevent oxidation, lock the QDs into position, inhibit diffusion, and tune the height and width of the inter-QD potential barriers that govern charge transport [5,6]. Poor stability is a common feature of nanoscale electronic materials, yet stability is all too rarely the focus of basic research. Fundamental studies are therefore needed to elucidate the most important mechanisms of degradation and develop simple yet effective countermeasures. By revealing both how QD solids degrade in response to environmental stresses (oxidative, photothermal, and electric) and how to prevent this degradation, the project has greatly improved our ability to develop stable, high-performance QD materials for real-world applications.
Sylvester-preconditioned adaptive-rank implicit time integrators for advection-diffusion equations with variable coefficients
Here, we consider the adaptive-rank integration of multi-dimensional time-dependent advection-diffusion partial differential equations (PDEs) with variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with high-order diagonally implicit Runge-Kutta temporal schemes. The discrete equation is a generalized Sylvester equation (GSE), which we solve with a projection-based adaptive-rank algorithm structured around two key strategies: (i) constructing dimension-wise subspaces using a novel atypical extended Krylov strategy, and (ii) efficiently solving the basis coefficient matrix with a preconditioned GMRES solver. The low-rank decomposition is performed in 2D using SVD and with high-order SVD (HOSVD) in 3D to represent the tensor in a compressed Tucker format. For d-dimensional problems (here, d = 2 or 3), the computational complexity and memory storage of the approach are found numerically to scale as and $\mathscr{O}(Nr^2) + \mathscr{O} (r^{d+1})$ and $\mathscr{O}(Nr) + \mathscr{O} (r^{d})$, respectively, with the one-dimensional resolution and the maximal rank during the Krylov iteration (which we find to be largely independent of on our numerical examples). We present numerical examples that illustrate the advertised properties of the algorithm.