Detection and Modeling of Boundary Layer Height

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.

Dye doped, core / shell silica nanoparticles: synthesis, characterization, & biotechnological applications

The aim of this thesis was to design, synthesize and develop a nanoparticle based system to be used as a chemosensor or as a label in bioanalytical applications. A versatile fluorescent functionalizable nanoarchitecture has been effectively produced based on the hydrolysis and condensation of TEOS in direct micelles of Pluronic® F 127, obtaining highly monodisperse silica - core / PEG - shell nanoparticles with a diameter of about 20 nm. Surface functionalized nanoparticles have been obtained in a one-pot procedure by chemical modification of the hydroxyl terminal groups of the surfactant. To make them fluorescent, a whole library of triethoxysilane fluorophores (mainly BODIPY based), encompassing the whole visible spectrum has been synthesized: this derivatization allows a high degree of doping, but the close proximity of the molecules inside the silica matrix leads to the development of self - quenching processes at high doping levels, with the concomitant fall of the fluorescence signal intensity. In order to bypass this parasite phenomenon, multichromophoric systems have been prepared, where highly efficient FRET processes occur, showing that this energy pathway is faster than self - quenching, recovering the fluorescence signal. The FRET efficiency remains very high even four dye nanoparticles, increasing the pseudo Stokes shift of the system, attractive feature for multiplexing analysis. These optimized nanoparticles have been successfully exploited in molecular imaging applications such as in vitro, in vivo and ex vivo imaging, proving themselves superior to conventional molecular fluorophores as signaling units.