Throughout their life, plants typically remain in one location utilizing sunlight for the synthesis of carbohydrates, which serve as their sole source of energy as well as building blocks of a protective extracellular matrix, called the cell wall. During the course of evolution, plants have repeatedly adapted to their respective niche,which is reflected in the changes of their body plan and the specific design of cell walls. Cell walls not only changed throughout evolution but also are constantly remodelled and reconstructed during the development of an individual plant, and in response to environmental stress or pathogen attacks. Carbohydrate-rich cell walls display complex designs, which together with the presence of phenolic polymers constitutes a barrier for microbes, fungi, and animals. Throughout evolution microbes have co-evolved strategies for efficient breakdown of cell walls. Our current understanding of cell walls and their evolutionary changes are limited as our knowledge is mainly derived from biochemical and genetic studies, complemented by a few targeted yet very informative imaging studies. Comprehensive plant cell wall models will aid in the re-design of plant cell walls for the purpose of commercially viable lignocellulosic biofuel production as well as for the timber, textile, and paper industries. Such knowledge will also be of great interest in the context of agriculture and to plant biologists in general. It is expected that detailed plant cell wall models will require integrated correlative multimodal, multiscale imaging and modelling approaches, which are currently underway.

The two-dimensional flow over the cylinder is simulated using Ansys Fluent and analyzed using spectral proper orthogonal decomposition (SPOD). The SPOD eigenvalues and eigenvectors are used to get the Strouhal numbers ($St$) and eigenmodes. Circular and square cylinder flows are analyzed at three different Reynolds numbers ($Re$) of 60, 100, and 150. Elliptical cylinder flows are analyzed at $Re$ = 150 with different aspect ratios of 0.25, 0.5, and 4. The $St$ of circular cylinder flow is close to literature data and the ones of square and elliptical cylinder flow have greater differences from literature data. The $St$ is larger at larger $Re$. The modes show symmetric structures at $St$ and 3$St$ and anti-symmetric structures at 2$St$ for all cases. Elliptical cylinder cases are simulated again in a larger domain. The case with AR = 0.25 shows a secondary vortex street structure at the far wake. A secondary frequency at 0.07781 is observed and the modes at that frequency have Karman vortex street structures at the far wake.

Ekman layers over a rough surface are studied using direct numerical simulation (DNS). The roughness takes the form of periodic two-dimensional bumps whose non-dimensional amplitude is fixed at a small value (h+ = 15) and whose mean slope is gentle. The neutral Ekman layer is subjected to a stabilizing cooling flux for approximately one inertial period (2π/ f) to impose the stratification. The Ekman Boundary Layer (EBL) is in a transitionally rough regime and, without stratification, the effect of roughness is found to be mild in contrast to the stratified case. Roughness, whose effect increases with the slope of the bumps, changes the boundary layer qualitatively from the very stable (Mahrt, 1998) regime, which has a strong thermal inversion and a pronounced low-level jet, in the flat case to the stable regime, which has a weaker thermal inversion and stronger surface-layer turbulence, in the rough cases. The flat case exhibits initial collapse of turbulence which eventually recovers, albeit with inertial oscillations in turbulent kinetic energy.

The roughness elements interrupt the initial collapse of turbulence. In the quasi-steady state, the thickness of the turbulent stress profiles and of the near-surface region with sub-critical gradient Richardson number increase in the rough cases. Analysis of the turbulent kinetic energy (TKE) budget shows that, in the surface layer, roughness counteracts the stability-induced reduction of TKE production. The flow component, coherent with the surface undulations, is extracted by a triple decomposition, and leads to a dispersive component of near-surface turbulent fluxes. The significance of the dispersive component increases in the stratified cases. Motivated by the dynamics of the atmospheric boundary layer, we also examines the influence of Coriolis acceleration on wall-bounded turbulence. The large-scale structures of an Ekman boundary layer are compared to those of a channel. The distribution of energy across scales is studied by looking into the spectra of the velocity fluctuations. Linear stability analysis reveals the existence of an unstable range of wavenumbers which sustain the turbulence and lead to transverse “roll” structures observed in instantaneous snapshots of the flow. Finally, additional DNS has been performed to find the effect of the roughness geometry in counteracting the buoyancy. Changing the bump height without changing its aspect ratio has little influence. However, for sufficiently large surface cooling flux, roughness is unable to maintain a turbulent state. Comparison of all the simulated cases shows that the final value of Rib is sufficient to provide guidance on the overall state of the boundary layer and its characterization into the following regimes: (i) weakly stable, (ii) very stable with turbulence collapse and rebirth to a state of global intermittency, or (iii) very stable with turbulence collapse and no recovery.

Direct numerical simulations of a stratified shear layer are performed for several different values of Reynolds, bulk Richardson, and Prandtl numbers. Unlike previous numerical studies, the initial perturbations are turbulent. These initial broadband perturbations do not allow the formation of distinct coherent structures such as Kelvin-Helmholtz rollers and streamwise vortices found in previous studies. In the absence of stratification, the shear layer thickness grows linearly and fully developed turbulence is achieved with mean velocities, turbulence intensities, and turbulent kinetic energy budgets that agree well with previous experimental and numerical data. When buoyancy is included, the shear layer grows to an asymptotic thickness, and the corresponding bulk Richardson number, Ri(b), is within the range, 0.32 +/- 0.06, found in previous studies. The apparent scatter in the evolution of Ri(b) is shown to have a systematic dependence on Reynolds and Prandtl numbers. A detailed description of buoyancy effects on turbulence energetics, transport, and mixing is presented. The Reynolds shear stress, , is significantly reduced by buoyancy, thus decreasing the shear production of turbulence. Owing to buoyancy, gradients in the vertical direction tend to be larger than other gradients in the fluctuating velocity and density fields. However, this anisotropy of the gradients is lower when the Reynolds number increases. Coherent finger-like structures are identified in the density field at late time and their vertical extent obtained by a scaling analysis. (C) 2007 American Institute of Physics.

One-dimensional (1-D) nanostructure-based sensors provide better sensitivity as compared to conventional thin film-based sensors due to their comparable dimensions with respect to Debye length. Single-walled carbon nanotubes (SWNTs) are 1-D nanostructures having high electrical mobility, high mechanical strength and high specific surface area that facilitate building of low-power, ultrahigh density sensors within limited space. However, pristine SWNTs posses limited sensitivity and selectivity. This sensitivity issue could be resolved through surface modification of SWNTs with suitable recognition molecules. Choosing the right functional material, sensor performance, in terms of sensitivity, could be enhanced. The selectivity issue could be overcome by making an array of sensors using different functional material and combining them with suitable pattern recognition software and thus, developing an electronic nose.

The overall objective of this dissertation is the development of a high density sensor array using single-walled carbon nanotube (SWNT) hybrid structures as building blocks for the realization of highly sensitive and discriminative sensors for monitoring of pollutants in the environment. To conceive such desired sensor characteristics, SWNTs were functionalized with organic macromolecules like porphyrins and calixarene to target VOCs in the air, and with biomolecules like polyT (a ssDNA) to target mercury ions in water. Detailed analysis of SWNT hybrid formation through different routes such as solvent casting and electrochemical techniques using macromolecules was performed to investigate structure property relations and the effect of macronucleus towards sensor performance when exposed to various analytes. The SWNT-porphyrin hybrids exhibited a discriminating capability of VOCs at room temperature. However, electrochemically modified (as opposed to solvent casting) SWNT-poly(porphyrin) hybrid improved sensor sensitivity further, retaining their discriminating capabilities and providing a sub-ppm limit of detection towards VOCs. The SWNT-poly(metalloporphyrin) hybrids also showed a probable relationship between sensor response and metal present within the porphyrin depending on the metal electronegativity. ssDNA functionalized SWNT hybrid exhibited highly selective and sensitive sensing response towards mercury ions in solvent phase.