Nanoparticles (NP) offer great merits over controlling thermal, chemical and physical properties when compared to their micro-sized counterparts. The effectiveness of the dispersion of the NP is the key aspect of the applications in nanotechnology. The project studies the characterization and modification of functional NPs aided by the means of large scale molecular thermal dynamic computerized dispersing simulations, in the level of Nanoclusters (NC). Carrying out NP functionality characterisation in fluids can be enhanced, and analysed through computational simulation based on their interactions with fluidic media; in terms of thermo-mechanical, dynamic, physical, chemical and rheological properties. From the engineering perspective, effective characterizations of the nanofluids have also been carried out based on the particles sizes and particle-fluids Brownian motion (BM) theory. The study covered firstly, investigation of the pure CuO NP diffusion in water and hydrocarbon fluids, secondly, examination of the modified CuO NP diffusion in water. In both cases the studies were put under experiments and simulations for data collection and comparison. For simulation the COMPASS forcefield, smoothed particle hydrodynamic potential (SPH) and discrete particle dynamics potential (DPD) were implemented through the system. Excellent prediction of BM, Van der Waals interaction, electrostatic interaction and a number of force-fields in the system were exploited. The experimental results trend demonstrated high coherence with the simulation results. At first the diffusion coefficient was found to be 1.7e-8m2/s in the study of CuO NC in water based fluidic system. Secondly highly concurrent simulation results (i.e. data for viscosity and thermal conductivity) have been computed to experimental coherence. The viscosity trend of MD simulation and experimental results show a high level of convergence for temperatures between 303-323K. The simulated thermal conductivity of the water-CuO nanofluid was between 0.6—0.75W•m−1•K−1, showing a slight increase following a rise in temperature from 303 to 323 K. Moreover, the alkane-CuO nanofluid experimental and simulated work was also carried out, for analysing the thermo-physical quantities. The alkane-CuO nanofluid viscosity was found 0.9—2.7mpas and thermal conductivity is between 0.1—0.4W•m−1•K−1. Finally, the successful modification of the NPs on experimental and simulation platform has been analysed using different characterization variables. Experimental modification data has been quantified by using Fourier Transformation Infrared (FTIR) peak response, from particular ranges of interest i.e. 1667-1609cm-1 and 1668-1557cm-1. These FTIR peaks deduced Carboxylate attachment on the surface of NPs. Later, MD simulation was approached to mimic experimental setup of modification chemistry and similar agglomerations were observed as during experimental conditions. However, this approach has not been presented before; therefore this study has a significant impact on describing the agglomeration of modified NPs on simulation and experimental basis. Henceforth, the methodology established for metal oxide nanoparticle dispersion simulation is a novelty of this work.

A common challenge in materials science is the "inverse design problem," wherein one seeks to use theoretical models to discover the microscopic characteristics (e.g., interparticle interactions) of a system which, if fabricated or synthesized, would yield a targeted material property. Inverse design problems are commonly addressed by stochastic optimization strategies like simulated annealing. Such approaches have the advantage of being general and easy to apply, and they can be effective as long as material properties required for evaluating the objective function of the optimization are feasible to accurately compute for thousands to millions of different trial interactions. This requirement typically means that "exact" yet computationally intensive methods for property predictions (e.g., molecular simulations) are impractical for use within such calculations. Approximate theories with analytical or simple numerical solutions are attractive alternatives, provided that they can make sufficiently accurate predictions for a wide range of microscopic interaction types. We propose a new approach, based on the fine discretization (i.e., terracing) of continuous pair interactions, that allows first-order mean-spherical approximation theory to predict the equilibrium structure and thermodynamics of a wide class of complex fluid pair interactions. We use this approach to predict the radial distribution functions and potential energies for systems with screened electrostatic repulsions, solute-mediated depletion interactions, and ramp-shaped repulsions. We create a web applet for introductory statistical mechanics courses using this approach to quickly estimate the equilibrium structure and thermodynamics of a fluid from its pair interaction. We use the applet to illustrate two fundamental fluid phenomena: the transition from ideal gas-like behavior to correlated-liquid behavior with increasing density in a system of hard spheres, and the water-like tradeoff between dominant length scales with changing temperature in a system with ramp-shaped repulsions. Finally, we test the accuracy of our approach and several other integral equation theories by comparing their predictions to simulated data for a series of different pair interactions. We introduce a simple cumulative structural error metric to quantify the comparison to simulation, and find that according to this metric, the reference hypernetted chain closure with a semi-empirical bridge function is the most accurate of the tested approximations. / text

This dissertation develops an analytical-numerical approach to deterministically predict the space-time evolution of concentrations in heterogeneous geologic media conditioned on measurements of hydraulic conductivities (transmissivities) and/or hydraulic heads. Based on the new conditional Eulerian-Lagrangian transport theory by Neuman, we solve the conditional transport problem analytically at early time, and express it in pseudo-Fickian form at late time. The stochastically derived deterministic pseudo-Fickian mean concentration equation involves a conditional, space-time dependent dispersion tensor. The latter not only depends on properties of the medium and the velocity but also on the available information, and can be evaluated numerically along mean "particle" trajectories. The transport equation lends itself to accurate solution by standard Galerkin finite elements on a relatively coarse grid. This approach allows computing without using Monte Carlo simulation and explicitly the following: Concentration variance/covariance (uncertainty), origin of detected contaminant and associated uncertainty, mass flow rate across a "compliance surface", cumulative mass release and travel time probability distribution across this surface, uncertainty associated with the latter, second spatial moment of conditional mean plume about its center of mass, conditional mean second spatial moment of actual plume about its center of mass, conditional co-variance of plume center of mass, and effect of non-Gaussian velocity distribution. This approach can also account for uncertainty in initial mass and/or concentration when predicting the future evolution of a plume, whereas almost all existing stochastic models of solute transport assume the initial state to be known with certainty. We illustrate this approach by considering deterministic and uncertain instantaneous point and nonpoint sources in a two-dimensional domain with a mildly fluctuating, statistically homogeneous, lognormal transmissivity field. We take the unconditional mean velocity to be uniform, but allow conditioning on log transmissivity and hydraulic head data. Conditioning renders the velocity field statistically nonhomogeneous with reduced variances and correlation scales, renders the predicted plume irregular and non-Gaussian, and generally reduces both predictive dispersion and uncertainty.

Methodologies for estimating hydraulic and solute transport properties of unsaturated, fractured rock are developed. The methodologies are applied to networks of discrete fractures for the purpose of estimating steady fluid flow rates and breakthrough curves of entrained solutes. The formulations employ the boundary integral method to discretize the outer rim of each fracture and to solve a two dimensional flow equation within fracture planes. A three dimensional variant of the two dimensional boundary integral method is used to calculate flow through a permeable matrix with embedded permeable fractures. Exterior and interior surfaces are discretized using boundary elements to account for flow between fractures and the matrix, and between the matrix and fractures and the exterior boundaries. Synthetic fracture networks are created using planar fractures of finite areal extent embedded within a three dimensional rock matrix for the purpose of performing sensitivity studies of network hydraulic conductivity with respect to geometric parameters, such as fracture orientation and density. Results of the sensitivity studies show that: (1) The global hydraulic conductivity is linearly dependent on the product of fracture transmissivity and density for fractures of which fully penetrate the rock volume; (2) The effect of correlation between fracture length and transmissivity is to increase the global hydraulic conductivity; and (3) Results using a three dimensional coupled fracture— matrix flow regime compare favorably with analytic results. Flow through variably saturated fracture networks is modeled by assuming a constant capillary head within individual fractures. A free surface is found using an iterative procedure which locates nodal points at the intersection of constant total head and pressure head contours. The simulated free surface compares favorably with an approximate analytic solution and with laboratory results. Simulations indicate the presence of zones of water under both positive and negative pressure, as well as regions of air—filled voids. Travel times and breakthrough curves are determined by integrating the inverse velocity over a streamline, and then summing over all streamlines. For the fracture network examined, travel times decrease with decreasing fracture saturation. The effects of retardation and matrix diffusion are also examined.

No description available.

This thesis describes experimental studies of colloidal particles dispersed in solvents which themselves have phase transitions. One common definition of soft matter is: a material characterized by a mesoscopic length scale. This length scale is, for example, the colloid size or the ordered domain size. Here we combine a complex host with one characteristic length scale with dispersed particles that have a different size. It might be anticipated that new behaviour will occur. Two limits of particle characteristics are probed: the case of dilute sterically-stabilized particles and the case of a weak gel of attractive particles. The two systems are polymer particles dispersed in a phaseseparating microemulsion and silica nanoparticles dispersed in a low molecular weight liquid crystal. In each system a temperature driven phase transition plays a crucial role. In the microemulsion case we observe how transitional and pre-transitional phenomena create effective interactions between particles and how new behaviour emerges in the host solvent in the late stage of the phase separation. We show that the pre-transitional clustering of the PMMA particles is due to an adsorbed layer of dodecane. Subsequently heterogeneous nucleation of the gas phase is seen. After phase separation has occurred in off-critical samples the particles remain in either the continuous or dispersed phase depending on the original microemulsion composition. In the late stage of the phaseseparation the coalescence and coarsening behaviour changes significantly, after more material exchange between the phases has taken place. This behaviour is reminiscent of viscoelastic phase separation in polymer based samples. In the liquid crystal case we discover the anisotropy of the liquid crystal persists over large length scales and modifies the local dynamics of the gel. Using electron microscopy and scattering techniques we demonstrate that the silica embedded in the liquid crystal forms agglomerates with a fractal structure. Rheological characterization demonstrates that the resulting composite is a gel. Investigation of the composite’s local dynamics using x-ray photon correlation spectroscopy shows anisotropy and intermittency in the dynamics on significant length scales. In both systems we have studied new behaviour seen due to the influence of one component on the dynamic characteristics of the other The pre- and post- phase transition phenomena are only crucial in the microemulsion case where the particles have purely repulsive interactions. Our results illustrate the subtle balances that occur in soft composite systems.

This works examines the question whether a vapour-liquid phase transition exists in systems of particles with purely dipolar interactions, a topic which has been the subject of a longstanding debate. Monte Carlo simulation results for two modi operandi to tackle this issue are presented. One approach examines the phase behaviour of fluids of charged hard dumbbells (CHD), each made up of two oppositely charged hard spheres with diameters σ and separation d. In the limit d/σ → 0, and with the temperature scaled accordingly, the system corresponds to dipolar hard spheres (DHS) while for larger values of d ionic interactions are dominant. The crossover between ionic and dipolar regimes is examined and a linear variation of the critical temperature T*c in dipolar reduced units as a function of d is observed, giving rise to an extrapolated T*cDHS ≈ 0:15. The second approach focuses on the dipolar Yukawa hard sphere (DYHS)fluid, which is given by a dipolar hard sphere and an attractive isotropic interaction Y of the Yukawa tail form. In this case, the DHS limit is obtained for Y → 0. It is found that T*c depends linearly on the isotropic interaction strength Y over a wide range, coinciding with the results for the CHD model and extrapolating to a similar value of T*c;DHS. However, with the use of specially adapted biased Monte Carlo techniques which are highly efficient, it is shown that the linear variation of T*c is violated for very small values of the Yukawa interaction strength, almost two orders of magnitude smaller than the characteristic dipolar interaction energy. It is found that phase separation is not observable beyond a critical value of the Yukawa energy parameter, even though in thermodynamic and structural terms, the DYHS and DHS systems are very similar. It is suggested that either some very subtle physics distinguishes the DYHS and DHS systems, or the observation of a phase transition in DHSs is precluded by finite-size effects. In the context of phase separation in highly correlated fluids, new flat-histogram Monte Carlo simulation techniques based on the Wang-Landau algorithm are evaluated and shown to be useful tools. This work presents a general and unifying framework for deriving Monte Carlo acceptance rules which facilitate flat histogram sampling. The framework yields uniform sampling rules for thermodynamic states given either by the mechanically extensive variables appearing in the Hamiltonian or, equivalently, uniformly sample the thermodynamic fields which are conjugate to these mechanical variables.

Metalworking fluids (MWFs) are petroleum emulsions employed for metal machining processes as coolants and lubricants. To date, they have been irreplaceable in modern heavy and manufacturing industries, with annual usage exceeding two billion litres worldwide. However, the large amount of MWFs, the highly concentrated complex recalcitrant and toxic petroleum components contained in them continue to cause significant concern in terms of sustainable routes of end-of-life treatment and disposal. Compared with other treatment methods, the anaerobic treatment method has significant advantages, such as the low capital, operating and maintenance costs and energy recovery. This latter factor has the potential benefit of generating bio-energy from waste organic matter whilst aerobic route leads to CO₂ emission. However, the bio-toxicity of MWFs is a huge challenge in terms of employing bio-treatment of waste MWFs. In this study, the anaerobic biodegradability of a typical MWF was investigated employing an activated sludge experimental system. Furthermore, the toxic effects of the MWF on the anaerobic ecosystem, particularly on methanogen species, were investigated using bio-molecular analytical methods and a biosensor. In order to overcome its toxicity, the indigenous anaerobic bacteria isolated from spent MWFs were employed in the treatment of the MWF since they were assumed to be acclimated to the conditions. The major findings include: (1) approximately 80% of the MWF (5,000mgCOD/L) was found to be anaerobically biodegradable, with around 35% of the biodegraded COD could be converted to methane; (2) the MWF appeared to be toxic to the anaerobic ecosystem, especially to methanogen species; and (3) however, treatment employing the anaerobic bacteria successfully reduced the toxicity of the MWF and enhanced the methane production in the process.

Just as conventional mechatronic systems rely on switches and relays, machines that are soft and elastically deformable will require compliant materials that can support field-controlled reconfiguration. In this dissertation, I present several novel approaches to shape programmability that primarily rely on condensed soft matter and are stimulated by electric or magnetic fields. I begin with electric-field-driven methods for achieving shape programmability of elastomer-based systems. These include dielectric elastomer actuators and electrostatic beams that undergo extreme stretch. Classical theories in elasticity and electrostatics are used to examine the mechanical responses and instabilities of these soft, hyperelastic systems. Such modeling techniques are also used to examine another switching mode based on the snap through behavior of a buckled ferromagnetic beam under magnetic load. I will then discuss a unique approach to shape programmability that is based on electrochemistry and exploits the coalescence and separation of anchored liquid metal drops. In this case, electrical signals under 10V are utilized to manipulate surface energies and transition between bi-stable states. Experiments and Surface Evolver simulations show that oxidation and reduction on opposing poles of the coalesced drops create an interfacial tension gradient that eventually leads to limit-point instability. Theory derived from bipolar electrochemistry and vertical electrical sounding predicts droplet motion and separation based on geometry and bath conductivity, facilitating the optimization of reconfigurable devices using this phenomenon. I conclude with the application of the bi-stable droplets to a simple toggle switch capable of changing circuit conductivity by over three orders of magnitude.

Dans ce travail de thèse, nous avons développé deux méthodologies permettant d'acquérir rapidement et facilement des propriétés physico-chimiques, cinétiques et thermodynamiques de fluides complexes. Nous nous sommes focalisés sur le rôle de la concentration sur ces propriétés. Les deux méthodes développées sont basées sur la concentration en continu d'une solution aqueuse par évaporation contrôlée du solvant. Le premier outil est une goutte de quelques microlitres confinée entre deux plaques dont la hauteur est de 100µm. Dans cette géométrie à deux dimensions, l'évaporation est entièrement décrite par un modèle que nous avons développé. L'observation du séchage de la goutte couplée à des mesures locales de concentration par spectroscopie Raman, permet d'accéder quantitativement au diagramme de phase d'une solution de copolymères, et de mesurer l'activité ainsi que d'estimer le coefficient d'interdiffusion de la solution. Le second outil est une puce microfluidique permettant de concentrer des solutions aqueuses grâce à la pervaporation de l'eau à travers une membrane. Cet outil permet avec quelques microgrammes de soluté, de bâtir un gradient de concentration stationnaire le long d'un microcanal. Les techniques de spectroscopie Raman et de diffusion des rayons X aux petits angles permettent à nouveau de mesurer des propriétés physico-chimiques de la solution mais également de mettre en évidence le caractère discontinu du coefficient d'interdiffusion en fonction de la concentration, dépendant des mésophases présentes. / In this work, we developed two methods in order to access rapidly and easily physico-chemical, thermodynamic and kinetic properties of complex fluids. We focused on the role of the concentration on these properties. The two methods that we developed are based on the continuous concentration of an aqueous solution thanks to the evaporation of the solvent. The first tool is a microliter droplet confined between two circular plates with a cell height of about 100 µm. Within this two dimensional cylindrical geometry, the evaporation of the droplet is totally described by a model that we developed. The observation of the droplet evaporation combined to local Raman spectroscopy measurements permits us to build a quantitative phase diagram, to measure the activity of the solution and to estimate its mutual diffusion coefficient. The second tool is a microfluidic chip in which water is removed through a thin membrane. This device permits us to build with a few micrograms of solutes a stationary concentration gradient along a microchannel. Raman confocal spectroscopy and small angle X-ray scattering give access to the quantitative phase diagram and also permit to evidence that the mutual diffusion coefficient is discontinuous at some of the phase boundaries.