Modeling of electrical properties of composites

Composites offer a way to design electromagnetic properties simultaneously with other material properties such as mechanical or thermal properties. This thesis describes studies pertaining to the modeling of effective electrical material properties of artificial composites of two separate material phases. This has practical importance because modeling reduces experimental effort when developing novel composites or improving the existing ones. The focus is on materials which have a disordered microstructure, and the scale of inhomogeneities is much smaller than the wavelength. In this thesis, a historical overview about modeling of electrical properties of composites is given. Then a novel method to reduce the computational cost of numerical modeling is introduced. Results are compared to experiments with polymer/ceramic composites. Some problems related to the modeling of the effective permittivity are also pointed out. Novel analytical methods for the modeling of electrical properties of composites are introduced. An equation, which combines desirable properties of two popular mixing equations, is derived. A new method to calculate the effective permittivity and the tunablity of a composite consisting of linear and non-linear dielectrics is given

Development of a Quasi-Monte Carlo Method for Thermal Radiation

Radiative heat transfer in participating media is among the most challenging computational engineering problems due to the complex nonlinear, nonlocal nature of radiation transport. Many approximate methods have been developed in order to resolve radiative heat transfer in participating media; but approximate methods, by the nature of their approximations, suffer from various shortcomings both in terms of accuracy and robustness. The only methods that can resolve radiative transfer accurately in all configurations are the statistical Monte Carlo-based methods. While the Monte Carlo (MC) method is the most accurate method for resolving radiative heat transfer, it is also notoriously computationally prohibitive in large-scale simulations. To overcome this computational burden, this study details the development of a quasi-Monte Carlo (QMC) method for thermal radiation in participating media with a focus on combustion-related problems. The QMC method employs a low-discrepancy sequence (LDS) in place of the traditional random number sampling mechanism used in Monte Carlo methods to increase computational efficiency. In order to analyze the performance of the QMC method, a systematic comparison of accuracy and computational expense was performed. The QMC method was validated against formal solutions of radiative heat transfer in several one-dimensional configurations and extended to three practical combustion configurations: a turbulent jet flame, a high-pressure industrial gas turbine, and a high-pressure spray combustion chamber. The results from QMC and traditional Monte Carlo are compared against benchmark solutions for each case. It is shown that accuracy of the predicted radiation field from QMC is comparable to MC at lower computational costs. Three different low-discrepancy sequences – Sobol, Halton, and Niederreiter – were examined as part of this work. Finally, recommendations are made in terms of choice of the sequence and the number of the dimensions of the LDS for combustion-relevant configurations. In conclusion, significant improvements in computational costs and accuracy seen in the QMC method makes it a viable alternative to traditional Monte Carlo methods in high-fidelity simulations

Advances in modeling gas adsorption in porous materials for the characterization applications

The dissertation studies methods for mesoporous materials characterization using adsorption at various levels of scale and complexity. It starts with the topic introduction, necessary notations and definitions, recognized standards, and a literature review. Synthesis of novel materials requires tailoring of the characterization methods and their thorough testing. The second chapter presents a nitrogen adsorption characterization study for silica colloidal crystals (synthetic opals). These materials have cage-like pores in the range of tens of nanometers. The adsorption model can be described within a macroscopic approach, based on the Derjaguin-Broekhoff-de Boer (DBdB) theory of capillary condensation. A kernel of theoretical isotherms is built and applied to the solution of the adsorption integral equation to derive the pore-size distribution from experimental data. The technique is validated with a surface modification of the samples so that it changes the interaction but not the pore size. The second chapter deals with the characterization of three-dimensional ordered mesoporous (3DOm) carbons. Similar to opals, these materials have cage-like mesopores, however, these pores are connected with large windows. These windows affect the adsorption process and calculated pore-size distributions. The grand canonical Monte Carlo simulations with derived solid-fluid potentials, which take into account the 3DOm carbons geometry, confirm the critical role of interconnections, their size, and number, for correct interpretation of adsorption data for the PSD calculations. The fourth chapter discusses a method for the pore size estimation that can serve as an alternative to the adsorption isotherms analysis. It is based on measurements of elastic properties of liquid that can be useful for the pore size estimation. A Vycor glass sample, a disordered mesoporous material with channel-like pores having a characteristic size of ca. 6-8 nm, is considered. The changes in longitudinal and shear moduli from the experimental data and molecular simulations are predicted with a near-quantitative agreement. Then, it follows by their relation of the moduli to the pore size, which is promising for characterization. The last fifth chapter considers a promising Monte Carlo method, the Kinetic Monte Carlo (kMC) algorithm. This method is efficient for the vapor-liquid equilibrium prediction in dense regions. This chapter shows a benchmark with conventional Metropolis et al algorithms as well as a parallelization scheme of the kMC algorithm

Ultracold atomic gases in optical lattices: mimicking condensed matter physics and beyond

We review recent developments in the physics of ultracold atomic and molecular gases in optical lattices. Such systems are nearly perfect realisations of various kinds of Hubbard models, and as such may very well serve to mimic condensed matter phenomena. We show how these systems may be employed as quantum simulators to answer some challenging open questions of condensed matter, and even high energy physics. After a short presentation of the models and the methods of treatment of such systems, we discuss in detail, which challenges of condensed matter physics can be addressed with (i) disordered ultracold lattice gases, (ii) frustrated ultracold gases, (iii) spinor lattice gases, (iv) lattice gases in "artificial" magnetic fields, and, last but not least, (v) quantum information processing in lattice gases. For completeness, also some recent progress related to the above topics with trapped cold gases will be discussed.Comment: Review article. v2: published version, 135 pages, 34 figure

Full-time response of starch subjected to microwave heating

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Experimental and Theoretical Investigation of Macro-Periodic and Micro-Random Nanostructures with Simultaneously Spatial Translational Symmetry and Long-Range Order Breaking

Photonic and plasmonic quasicrystals, comprising well-designed and regularly-arranged patterns but lacking spatial translational symmetry, show sharp diffraction patterns resulting from their long-range order in spatial domain. Here we demonstrate that plasmonic structure, which is macroscopically arranged with spatial periodicity and microscopically constructed by random metal nanostructures, can also exhibit the diffraction effect experimentally, despite both of the translational symmetry and long-range order are broken in spatial domain simultaneously. With strategically pre-formed metal nano-seeds, the tunable macroscopically periodic (macro-periodic) pattern composed from microscopically random (micro-random) nanoplate-based silver structures are fabricated chemically through photon driven growth using simple light source with low photon energy and low optical power density. The geometry of the micro-structure can be further modified through simple thermal annealing. While the random metal nanostructures suppress high-order Floquet spectra of the spatial distribution of refractive indices, the maintained low-order Floquet spectra after the ensemble averaging are responsible for the observed diffraction effect. A theoretical approach has also been established to describe and understand the macro-periodic and micro-random structures with different micro-geometries. The easy fabrication and comprehensive understanding of this metal structure will be beneficial for its application in plasmonics, photonics and optoelectronics.published_or_final_versio

A study of the ionic diffusion under the effect of electric field (computer simulation with reference to biological membrane)

The biophysical studies of the biological system are far from being conclusive. Not only because this science is relatively recent, but also because of the lack of physical data. Also there are a lot of contradicting views among researchers as well as the poor theoretical interpretation of the reported experimental data. However, the advent of computer science with the considerable storage capability and highly vast calculations gives modeling techniques a great advantage and opens a real door to better understanding of the complicated biological phenomena. The present thesis addressed the problem of ionic penetration through biological tissue under the effect of external electric field (DC and AC). This was done by studying the diffusion coefficient D as an indicating parameter for such effects. The work was based on stochastic computer simulation of the problem such that the tissue was considered as a matrix that contains the elements under study. The size of the matrix was up to 30,000 x 30,000. Two dimensional honey comb cellular pattern was simulated such that it allowed six maximum possible element-to-element communications. The diffusants were let to diffuse under different electric field strengths in DC forward and opposite directions, and AC field with different frequencies. The effect of vacancies concentration and annealing time were tested in the absence of electric field. Two different vacancies concentrations were studied under the effect of electric field. Fist, 90% of the tissue was vacant and subjected to DC and AC fields as well as zero field. Second, 50% of the tissue was vacant and investigated under similar conditions. The results showed that for the 90% case, the penetration increased with increasing of electric field strength. While in the 50% case, the penetration increases with increasing the current until a point at which the diffusion is hindered. The DC results of forward current were compared to that of backward direct current and the results showed that the backward direction hindered diffusion. The effect of alternating current shows that penetration was inversely proportional with the frequency which agrees with literature. Comparisons of the effects of sinusoidal and square waves were illustrated. The square waves showed to have more ionic penetration and diffusion coefficient values than the sinusoidal ones. As the frequency of alternating current is decreased, its effect on diffusion became close to that of direct current. Despite the fact that the results obtained by simulation are in essence virtual and based on arbitrary units, yet the effects were clear and indicative

Primary Structure and Solution Conditions Determine Conformational Ensemble Properties of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are a class of proteins that do not exhibit well-defined three-dimensional structures. The absence of structure is intrinsic to their amino acid sequences, which are characterized by low hydrophobicity and high net charge per residue compared to folded proteins. Contradicting the classic structure-function paradigm, IDPs are capable of interacting with high specificity and affinity, often acquiring order in complex with protein and nucleic acid binding partners. This phenomenon is evident during cellular activities involving IDPs, which include transcriptional and translational regulation, cell cycle control, signal transduction, molecular assembly, and molecular recognition. Although approximately 30% of eukaryotic proteomes are intrinsically disordered, the nature of IDP conformational ensembles remains unclear. In this dissertation, we describe relationships connecting characteristics of IDP conformational ensembles to their primary structures and solution conditions. Using molecular simulations and fluorescence experiments on a set of base-rich IDPs, we find that net charge per residue segregates conformational ensembles along a globule-to-coil transition. Speculatively generalizing this result, we propose a phase diagram that predicts an IDP\u27s average size and shape based on sequence composition and use it to generate hypotheses for a broad set of intrinsically disordered regions (IDRs). Simulations reveal that acid-rich IDRs, unlike their oppositely charged base-rich counterparts, exhibit disordered globular ensembles despite intra-chain repulsive electrostatic interactions. This apparent asymmetry is sensitive to simulation parameters for representing alkali and halide salt ions, suggesting that solution conditions modulate IDP conformational ensembles. We refine the ion parameters using a calibration procedure that relies exclusively on crystal lattice properties. Simulations with these parameters recover swollen coil behavior for acid-rich IDRs, but also uncover a dependence on sequence patterning for polyampholytic IDPs. These contributions initiate an endeavor to elucidate general principles that enable prediction of an IDP\u27s conformational ensemble based on primary structure and solution conditions, a goal analogous to structure prediction for folded proteins. Such principles would provide a molecular basis for understanding the roles of IDPs in physiology and pathophysiology, guide development of agents that modulate their behavior, and enable their rational design from chosen specifications

Gradient Particle Magnetohydrodynamics

We introduce Gradient Particle Magnetohydrodynamics (GPM), a new Lagrangian method for magnetohydrodynamics based on gradients corrected for the locally disordered particle distribution. The development of a numerical code for MHD simulation using the GPM algorithm is outlined. Validation tests simulating linear and nonlinear sound waves, linear MHD waves, advection of magnetic fields in a magnetized vortex, hydrodynamical shocks, and three-dimensional collapse are presented, demonstrating the viability of an MHD code using GPM. The characteristics of a GPM code are discussed and possible avenues for further development and refinement are mentioned. We conclude with a view of how GPM may complement other methods currently in development for the next generation of computational astrophysics.Comment: 26 pages, 11 figure

Controlled Folding In Precisely Functionalized Polyethylenes: Designing Nanoscale Lamellar Structures For Ion Transport

There is great societal need for improved energy storage technology, in applications ranging from electric vehicles to grid storage to emergency power systems. New polymeric membranes with enhanced ion conductivities are needed in batteries and fuel cells to improve these technologies. Despite decades of research, these membrane materials are still not adequate for commercial use. The primary metric that requires improvement is ion or proton conductivity of the membrane under desirable operating conditions. Modifying linear polyethylene by the addition of precisely periodic functional groups leads to a rich array of intriguing morphologies and properties. Depending on the functional group chemistry and periodicity, functionalization can (1) increase or decrease the melting point; (2) produce amorphous, semicrystalline, or nearly fully crystalline morphologies; (3) cause hairpin chain folds within the crystalline regions; and (4) exhibit high proton conductivity when hydrated. In this dissertation, we focus on polyethylenes with long-spaced functional groups, which form layered crystallites. Herein, we show that incorporating backbone sulfone groups into polyethylene gives rise to nylon-like semicrystalline morphologies, where the sulfone groups form layers within the crystalline regions and hydrogen bond, increasing the melting temperature. The melting point is proportional to the sulfone concentration. Conversely, when the polyethylene contains pendant carboxylic acid groups, it exhibits hairpin folds at the position of each acid group within the crystalline regions. This produces multiple layers of acid groups whose normal vectors are oriented approximately perpendicular to the normal vector of the lamellar crystallite. Layers with this orientation could provide pathways through crystallites for selective transport of protons, ions, water, or other small molecules, allowing the use of a semicrystalline polymer for various membranes. Replacing carboxylic acid with sulfonic acid produces a nearly fully crystalline morphology containing hydrated acid layers, resulting in high proton conductivity. This is the first time, to our knowledge, that proton or ion conductivity has been reported in this type of morphology. Finally, we show that functionalized polyethylenes with a nearly precise placement of functional group – variable by one backbone methylene group – has a similarly well-ordered morphology as a truly precise polymer, and could provide a more practical route to the morphologies and properties discussed above