138 resultados para lattice


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Raman spectra of the uranyl titanate mineral euxenite were analyzed and related to the mineral structure. A comparison is made with the Raman spectra of uranyl oxyhydroxide hydrates. The obsd. bands are attributed to the Ti[n.63743]O and (UO2)2+ stretching and bending vibrations, as well as lattice vibrations of rare-earth ions. The Raman bands of euxenite are in harmony with those of the uranyl oxyhydroxides. The mineral euxenite is metamict as is evidenced by the intensity of the U[n.63743]O stretching and bending modes, which are of lower intensity than expected, and with bands that are significantly broader.

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Raman and infrared spectra of two polymorphous minerals with the chemical formula Fe3+(SO4)(OH)•2H2O, monoclinic butlerite and orthorhombic parabutlerite, are studied and the spectra assigned. Observed bands are attributed to the (SO4)2- stretching and bending vibrations, hydrogen bonded water molecules, stretching and bending vibrations of hydroxyl ions, water librational modes, Fe-O and Fe-OH stretching vibrations, Fe-OH bending vibrations and lattice vibrations. The O-H...O hydrogen bond lengths in the structures of both minerals are calculated from the wavenumbers of the stretching vibrations. One symmetrically distinct (SO4)2- unit in the structure of butlerite and two symmetrically distinct (SO4)2- units in the structure of parabutlerite are inferred from the Raman and infrared spectra. This conclusion agrees with the published crystal structures of both mineral phases.

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The results of pressure-tuning Raman spectroscopic, X-ray powder diffraction and solid-state 13C-NMR studies of selected dicarboxylate anions intercalated in a Mg-Al layered double hydroxide (talcite) lattice are reported. The pressure dependences of the vibrational modes are linear for pressures up to 4.6 GPa indicating that no phase transitions occur. The interlayer spacings show that the oxalate, malonate and succinate dianions are oriented perpendicular to the layers, but the glutarate and adipate are tilted. The solid-state 13C-NMR spectra of these materials show full chemical shift anisotropy and, therefore, the anions are not mobile at room temperature.

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Nanostructured tungsten oxide thin film based gas sensors have been developed by thermal evaporation method to detect CO at low operating temperatures. The influence of Fe-doping and annealing heat treatment on microstructural and gas sensing properties of these films have been investigated. Fe was incorporated in WO3 film by co-evaporation and annealing was performed at 400oC for 2 hours in air. AFM analysis revealed a grain size of about 10-15 nm in all the films. GIXRD analysis showed that as-deposited films are amorphous and annealing at 400oC improved the crystallinity. Raman and XRD analysis indicated that Fe is incorporated in the WO3 matrix as a substitutional impurity, resulting in shorter O-W-O bonds and lattice cell parameters. Doping with Fe contributed significantly towards CO sensing performance of WO3 thin films. A good response to various concentrations (10-1000 ppm) of CO has been achieved with 400oC annealed Fe-doped WO3 film at a low operating temperature of 150oC.

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Continuum, partial differential equation models are often used to describe the collective motion of cell populations, with various types of motility represented by the choice of diffusion coefficient, and cell proliferation captured by the source terms. Previously, the choice of diffusion coefficient has been largely arbitrary, with the decision to choose a particular linear or nonlinear form generally based on calibration arguments rather than making any physical connection with the underlying individual-level properties of the cell motility mechanism. In this work we provide a new link between individual-level models, which account for important cell properties such as varying cell shape and volume exclusion, and population-level partial differential equation models. We work in an exclusion process framework, considering aligned, elongated cells that may occupy more than one lattice site, in order to represent populations of agents with different sizes. Three different idealizations of the individual-level mechanism are proposed, and these are connected to three different partial differential equations, each with a different diffusion coefficient; one linear, one nonlinear and degenerate and one nonlinear and nondegenerate. We test the ability of these three models to predict the population level response of a cell spreading problem for both proliferative and nonproliferative cases. We also explore the potential of our models to predict long time travelling wave invasion rates and extend our results to two dimensional spreading and invasion. Our results show that each model can accurately predict density data for nonproliferative systems, but that only one does so for proliferative systems. Hence great care must be taken to predict density data for with varying cell shape.

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In this paper, we describe an analysis for data collected on a three-dimensional spatial lattice with treatments applied at the horizontal lattice points. Spatial correlation is accounted for using a conditional autoregressive model. Observations are defined as neighbours only if they are at the same depth. This allows the corresponding variance components to vary by depth. We use the Markov chain Monte Carlo method with block updating, together with Krylov subspace methods, for efficient estimation of the model. The method is applicable to both regular and irregular horizontal lattices and hence to data collected at any set of horizontal sites for a set of depths or heights, for example, water column or soil profile data. The model for the three-dimensional data is applied to agricultural trial data for five separate days taken roughly six months apart in order to determine possible relationships over time. The purpose of the trial is to determine a form of cropping that leads to less moist soils in the root zone and beyond.We estimate moisture for each date, depth and treatment accounting for spatial correlation and determine relationships of these and other parameters over time.

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Modern technology now has the ability to generate large datasets over space and time. Such data typically exhibit high autocorrelations over all dimensions. The field trial data motivating the methods of this paper were collected to examine the behaviour of traditional cropping and to determine a cropping system which could maximise water use for grain production while minimising leakage below the crop root zone. They consist of moisture measurements made at 15 depths across 3 rows and 18 columns, in the lattice framework of an agricultural field. Bayesian conditional autoregressive (CAR) models are used to account for local site correlations. Conditional autoregressive models have not been widely used in analyses of agricultural data. This paper serves to illustrate the usefulness of these models in this field, along with the ease of implementation in WinBUGS, a freely available software package. The innovation is the fitting of separate conditional autoregressive models for each depth layer, the ‘layered CAR model’, while simultaneously estimating depth profile functions for each site treatment. Modelling interest also lay in how best to model the treatment effect depth profiles, and in the choice of neighbourhood structure for the spatial autocorrelation model. The favoured model fitted the treatment effects as splines over depth, and treated depth, the basis for the regression model, as measured with error, while fitting CAR neighbourhood models by depth layer. It is hierarchical, with separate onditional autoregressive spatial variance components at each depth, and the fixed terms which involve an errors-in-measurement model treat depth errors as interval-censored measurement error. The Bayesian framework permits transparent specification and easy comparison of the various complex models compared.

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Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.

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In this thesis, the author proposed and developed gas sensors made of nanostructured WO3 thin film by a thermal evaporation technique. This technique gives control over film thickness, grain size and purity. The device fabrication, nanostructured material synthesis, characterization and gas sensing performance have been undertaken. Three different types of nanostructured thin films, namely, pure WO3 thin films, iron-doped WO3 thin films by co-evaporation and Fe-implanted WO3 thin films have been synthesized. All the thin films have a film thickness of 300 nm. The physical, chemical and electronic properties of these films have been optimized by annealing heat treatment at 300ºC and 400ºC for 2 hours in air. Various analytical techniques were employed to characterize these films. Atomic Force Microscopy and Transmission Electron Microscopy revealed a very small grain size of the order 5-10 nm in as-deposited WO3 films, and annealing at 300ºC or 400ºC did not result in any significant change in grain size. X-ray diffraction (XRD) analysis revealed a highly amorphous structure of as-deposited films. Annealing at 300ºC for 2 hours in air did not improve crystallinity in these films. However, annealing at 400ºC for 2 hours in air significantly improved the crystallinity in pure and iron-doped WO3 thin films, whereas it only slightly improved the crystallinity of iron-implanted WO3 thin film as a result of implantation. Rutherford backscattered spectroscopy revealed an iron content of 0.5 at.% and 5.5 at.% in iron-doped and iron-implanted WO3 thin films, respectively. The RBS results have been confirmed using energy dispersive x-ray spectroscopy (EDX) during analysis of the films using transmission electron microscopy (TEM). X-ray photoelectron spectroscopy (XPS) revealed significant lowering of W 4f7/2 binding energy in all films annealed at 400ºC as compared with the as-deposited and 300ºC annealed films. Lowering of W 4f7/2 is due to increase in number of oxygen vacancies in the films and is considered highly beneficial for gas sensing. Raman analysis revealed that 400ºC annealed films except the iron-implanted film are highly crystalline with significant number of O-W-O bonds, which was consistent with the XRD results. Additionally, XRD, XPS and Raman analyses showed no evidence of secondary peaks corresponding to compounds of iron due to iron doping or implantation. This provided an understanding that iron was incorporated in the host WO3 matrix rather than as a separate dispersed compound or as catalyst on the surface. WO3 thin film based gas sensors are known to operate efficiently in the temperature range 200ºC-500 ºC. In the present study, by optimizing the physical, chemical and electronic properties through heat treatment and doping, an optimum response to H2, ethanol and CO has been achieved at a low operating temperature of 150ºC. Pure WO3 thin film annealed at 400ºC showed the highest sensitivity towards H2 at 150ºC due to its very small grain size and porosity, coupled with high number of oxygen vacancies, whereas Fe-doped WO3 film annealed at 400ºC showed the highest sensitivity to ethanol at an operating temperature of 150ºC due to its crystallinity, increased number of oxygen vacancies and higher degree of crystal distortions attributed to Fe addition. Pure WO3 films are known to be insensitive to CO, but iron-doped WO3 thin film annealed at 300ºC and 400ºC showed an optimum response to CO at an operating temperature of 150ºC. This result is attributed to lattice distortions produced in WO3 host matrix as a result of iron incorporation as substitutional impurity. However, iron-implanted WO3 thin films did not show any promising response towards the tested gases as the film structure has been damaged due to implantation, and annealing at 300ºC or 400ºC was not sufficient to induce crystallinity in these films. This study has demonstrated enhanced sensing properties of WO3 thin film sensors towards CO at lower operating temperature, which was achieved by optimizing the physical, chemical and electronic properties of the WO3 film through Fe doping and annealing. This study can be further extended to systematically investigate the effects of different Fe concentrations (0.5 at.% to 10 at.%) on the sensing performance of WO3 thin film gas sensors towards CO.

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The elastic properties of 1D nanostructures such as nanowires are often measured experimentally through actuation of the nanowire at its resonance frequency, and then relating the resonance frequency to the elastic stiffness using elementary beam theory. In the present work, we utilize large scale molecular dynamics simulations to report a novel beat phenomenon in [110]oriented Ag nanowires. The beat phenomenon is found to arise from the asymmetry of the lattice spacing in the orthogonal elementary directions of the [110] nanowire, i.e. the [-110] and [001] directions, which results in two different principal moments of inertia. Because of this, actuations imposed along any other direction are found to decompose into two orthogonal vibrational components based on the actuation angle relative to these two elementary directions, with this phenomenon being generalizable to <110> FCC nanowires of different materials (Cu, Au, Ni, Pd and Pt). The beat phenomenon is explained using a discrete moment of inertia model based on the hard sphere assumption, the model is utilized to show that surface effects enhance the beat phenomenon, while the effect is reduced with increasing nanowires cross-sectional size or aspect ratio. Most importantly, due to the existence of the beat phenomena, we demonstrate that in resonance experiments only a single frequency component is expected to be observed, particularly when the damping ratio is relatively large or very small. Furthermore, for a large range of actuation angles, the lower frequency is more likely to be detected than the higher one, which implies that experimental predictions of Young’s modulus obtained from resonance may in fact be under predictions. The present study therefore has significant implications for experimental interpretations of Young’s modulus as obtained via resonance testing.

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Dual-mode vibration of nanowires has been reported experimentally through actuation of the nanowire at its resonance frequency, which is expected to open up a variety of new modalities for the NEMS that could operate in the nonlinear regime. In the present work, we utilize large scale molecular dynamics simulations to investigate the dual-mode vibration of <110> Ag nanowires with triangular, rhombic and truncated rhombic cross-sections. By incorporating the generalized Young-Laplace equation into Euler-Bernoulli beam theory, the influence of surface effects on the dual-mode vibration is studied. Due to the different lattice spacing in principal axes of inertia of the {110} atomic layers, the NW is also modeled as a discrete system to reveal the influence from such specific atomic arrangement. It is found that the <110> Ag NW will under a dual-mode vibration if the actuation direction is deviated from the two principal axes of inertia. The predictions of the two first mode natural frequencies by the classical beam model appear underestimated comparing with the MD results, which are found to be enhanced by the discrete model. Particularly, the predictions by the beam theory with the contribution of surface effects are uniformly larger than the classical beam model, which exhibit better agreement with MD results for larger cross-sectional size. However, for ultrathin NWs, current consideration of surface effects is still experiencing certain inaccuracy. In all, for all different cross-sections, the inclusion of surface effects is found to reduce the difference between the two first mode natural frequencies. This trend is observed consistent with MD results. This study provides a first comprehensive investigation on the dual-mode vibration of <110> oriented Ag NWs, which is supposed to benefit the applications of NWs that acting as a resonating beam.

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Moving fronts of cells are essential features of embryonic development, wound repair and cancer metastasis. This paper describes a set of experiments to investigate the roles of random motility and proliferation in driving the spread of an initially confined cell population. The experiments include an analysis of cell spreading when proliferation was inhibited. Our data have been analysed using two mathematical models: a lattice-based discrete model and a related continuum partial differential equation model. We obtain independent estimates of the random motility parameter, D, and the intrinsic proliferation rate, λ, and we confirm that these estimates lead to accurate modelling predictions of the position of the leading edge of the moving front as well as the evolution of the cell density profiles. Previous work suggests that systems with a high λ/D ratio will be characterized by steep fronts, whereas systems with a low λ/D ratio will lead to shallow diffuse fronts and this is confirmed in the present study. Our results provide evidence that continuum models, based on the Fisher–Kolmogorov equation, are a reliable platform upon which we can interpret and predict such experimental observations.

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The structure of Cu-ZSM-5 catalysts that show activity for direct NO decomposition and selective catalytic reduction of NOx by hydrocarbons has been investigated by a multitude of modern surface analysis and spectroscopy techniques including X-ray photoelectron spectroscopy, thermogravimetric analysis, and in situ Fourier transform infrared spectroscopy. A series of four catalysts were prepared by exchange of Na-ZSM-5 with dilute copper acetate, and the copper loading was controlled by variation of the solution pH. Underexchanged catalysts contained isolated Cu2+OH-(H2O) species and as the copper loading was increased Cu2+ ions incorporated into the zeolite lattice appeared. The sites at which the latter two copper species were located were fundamentally different. The Cu2+OH-(H2O) moieties were bound to two lattice oxygen ions and associated with one aluminum framework species. In contrast, the Cu2+ ions were probably bound to four lattice oxygen ions and associated with two framework aluminum ions. Once the Cu-ZSM-5 samples attained high levels of exchange, the development of [Cu(μ-OH)2Cu]n2+OH-(H2O) species along with a small concentration of Cu(OH)2 was observed. On activation in helium to 500°C the Cu2+OH-(H2O) species transformed into Cu2+O- and Cu+ moieties, whereas the Cu2+ ions were apparently unaffected by this treatment (apart from the loss of ligated water molecules). Calcination of the precursors resulted in the formation of Cu2+O2- and a one-dimensional CuO species. Temperature-programmed desorption studies revealed that oxygen was removed from the latter two species at 407 and 575°C, respectively. © 1999 Academic Press.

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The structure and composition of reaction products between Bi-Sr-Ca-Cu-oxide (BSCCO) thick films and alumina substrates have been characterized using a combination of electron diffraction, scanning electron microscopy and energy dispersive X-ray spectrometry (EDX). Sr and Ca are found to be the most reactive cations with alumina. Sr4Al6O12SO4 is formed between the alumina substrates and BSCCO thick films prepared from paste with composition close to Bi-2212 (and Bi-2212 + 10 wt.% Ag). For paste with composition close to Bi(Pb)-2223 + 20 wt.% Ag, a new phase with f.c.c. structure, lattice parameter about a = 24.5 A and approximate composition Al3Sr2CaBi2CuOx has been identified in the interface region. Understanding and control of these reactions is essential for growth of high quality BSCCO thick films on alumina. (C) 1997 Elsevier Science S.A.

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The microstructure of Bi-Sr-Ca-Cu-oxide (BSCCO) thick films on alumina substrates has been characterized using a combination of X-ray diffractometry, scanning electron microscopy, transmission electron microscopy of sections across the film/substrate interface and energy-dispersive X-ray spectrometry. A reaction layer formed between the BSCCO films and the alumina substrates. This chemical interaction is largely responsible for off-stoichiometry of the films and is more significant after partial melting of the films. A new phase with fee structure, lattice parameter a = 2.45 nm and approximate composition Al3Sr2CaBi2CuOx has been identified as reaction product between BSCCO and Al2O3.