Heterogeneous catalysts for environmentally sustainable reactions

This PhD work deals with problems of synthetic organic chemistry with particular attention to the development of environmentally friendly processes. In particular, new synthetic strategies have been studied based on the use of low cost heterogeneous catalysts, non-toxic reagents and mild operating conditions that do not involve, when possible, the use of solvents. The catalysts examined are both basic and acids, commercial or prepared by hetereogenization of homogeneous catalysts synthesized by tethering or impregnation. In particular it will be discussed the catalytic activity of oxides (Al2O3 and TiO2), supported sulphonic acids and hydrotalcites for the reactions of selective monoesterificazion of dicarboxylic acids, dehydrogenation of butane in gas phase, esterification of levulinic acid, Friedel-Craft acylations, C-C and C-P coupling. The use of these materials has allowed the development of simple processes with low environmental impact. The operating conditions are in fact mild and reaction times short. The selectivity for the desired products is in all reported cases very high and the catalysts can be recycled maintaining their optimum performances.

Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).