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.

From core rigidities to dynamic capabilities: the role of external knowledge. A multiple case study

This study focuses on the processes of change that firms undertake to overcome conditions of organizational rigidity and develop new dynamic capabilities, thanks to the contribution of external knowledge. When external contingencies highlight firms’ core rigidities, external actors can intervene in change projects, providing new competences to firms’ managers. Knowledge transfer and organizational learning processes can lead to the development of new dynamic capabilities. Existing literature does not completely explain how these processes develop and how external knowledge providers, as management consultants, influence them. Dynamic capabilities literature has become very rich in the last years; however, the models that explain how dynamic capabilities evolve are not particularly investigated. Adopting a qualitative approach, this research proposes four relevant case studies in which external actors introduce new knowledge within organizations, activating processes of change. Each case study consists of a management consulting project. Data are collected through in-depth interviews with consultants and managers. A large amount of documents supports evidences from interviews. A narrative approach is adopted to account for change processes and a synthetic approach is proposed to compare case studies along relevant dimensions. This study presents a model of capabilities evolution, supported by empirical evidence, to explain how external knowledge intervenes in capabilities evolution processes: first, external actors solve gaps between environmental demands and firms’ capabilities, changing organizational structures and routines; second, a knowledge transfer between consultants and managers leads to the creation of new ordinary capabilities; third, managers can develop new dynamic capabilities through a deliberate learning process that internalizes new tacit knowledge from consultants. After the end of the consulting project, two elements can influence the deliberate learning process: new external contingencies and changes in the perceptions about external actors.

Neural network modelling of the wave-structure interaction processes

This thesis presents a new Artificial Neural Network (ANN) able to predict at once the main parameters representative of the wave-structure interaction processes, i.e. the wave overtopping discharge, the wave transmission coefficient and the wave reflection coefficient. The new ANN has been specifically developed in order to provide managers and scientists with a tool that can be efficiently used for design purposes. The development of this ANN started with the preparation of a new extended and homogeneous database that collects all the available tests reporting at least one of the three parameters, for a total amount of 16’165 data. The variety of structure types and wave attack conditions in the database includes smooth, rock and armour unit slopes, berm breakwaters, vertical walls, low crested structures, oblique wave attacks. Some of the existing ANNs were compared and improved, leading to the selection of a final ANN, whose architecture was optimized through an in-depth sensitivity analysis to the training parameters of the ANN. Each of the selected 15 input parameters represents a physical aspect of the wave-structure interaction process, describing the wave attack (wave steepness and obliquity, breaking and shoaling factors), the structure geometry (submergence, straight or non-straight slope, with or without berm or toe, presence or not of a crown wall), or the structure type (smooth or covered by an armour layer, with permeable or impermeable core). The advanced ANN here proposed provides accurate predictions for all the three parameters, and demonstrates to overcome the limits imposed by the traditional formulae and approach adopted so far by some of the existing ANNs. The possibility to adopt just one model to obtain a handy and accurate evaluation of the overall performance of a coastal or harbor structure represents the most important and exportable result of the work.