Sintesi, caratterizzazione e reattività di nuovi Cluster Carbonilici Ni-P

The aim of this work is to explore the chemistry of new heteroatomic molecular compounds never reported in the scientific literature so far: Ni-P carbonyl clusters. First, an attempt was made to illustrate the reasons which brought to the choice of this specific metal-pnictogen couple, such as the interesting properties of Ni-P binary phases, the difficulties related in obtaining structural data for this bulk compounds and the absence of references about nickel-phosphorus molecular compounds (e.g. carbonyl clusters) reported in literature. Then, the general criteria chosen for the reactions between precursors [Ni6(CO)12]2- and PCl3 or POCl3 have been reported. This work has permitted to individuate many new products, of which some have also been isolated and characterised: [Ni11P(CO)18]3-, [Ni23-xP2(CO)30-x]6- (x=0, 1), [HNi31P4(CO)39]5- e [H2Ni31P4(CO)39]4-. Except for the former, a reproducible synthetic path has been refined for all those new Ni-P carbonyl clusters; furthermore some chemical reactivity has been carried out in order to test their characteristics.

Dynamic resolution of alpha-substituted dihydrocinnamic aldehydes. A new asymmetric synthesis of pharmaceutically important amine building blocks.

Crystallization-induced diastereoisomer transformation (CIDT) was successfully employed in the enantioselective synthesis of 2-alkyl-3-aryl-propan-1-amines. These products are seen as potentially useful building blocks in the field of asymmetric organic chemistry, notably for pharmaceutically relevant compounds. The procedure was based on a recently reported protocol for deracemization of dihydrocinnamic aldehydes in which enantiomerically enriched 1-(amino(phenyl)methyl)naphthalen-2-ol (Betti base) is employed as a resolving agent. Additionally, fenpropimorph, a biologically active substance which contains the 2-alkyl-3-aryl-propan-1-amine moiety was synthetized, as an attempt to assess the usefulness of the enantiomerically enriched amines.

Study of a new method for high energy top tagging at the ATLAS experiment

Since its discovery, top quark has represented one of the most investigated field in particle physics. The aim of this thesis is the reconstruction of hadronic top with high transverse momentum (boosted) with the Template Overlap Method (TOM). Because of the high energy, the decay products of boosted tops are partially or totally overlapped and thus they are contained in a single large radius jet (fat-jet). TOM compares the internal energy distributions of the candidate fat-jet to a sample of tops obtained by a MC simulation (template). The algorithm is based on the definition of an overlap function, which quantifies the level of agreement between the fat-jet and the template, allowing an efficient discrimination of signal from the background contributions. A working point has been decided in order to obtain a signal efficiency close to 90% and a corresponding background rejection at 70%. TOM performances have been tested on MC samples in the muon channel and compared with the previous methods present in literature. All the methods will be merged in a multivariate analysis to give a global top tagging which will be included in ttbar production differential cross section performed on the data acquired in 2012 at sqrt(s)=8 TeV in high phase space region, where new physics processes could be possible. Due to its peculiarity to increase the pT, the Template Overlap Method will play a crucial role in the next data taking at sqrt(s)=13 TeV, where the almost totality of the tops will be produced at high energy, making the standard reconstruction methods inefficient.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.