Cadmium Telluride Magic-Sized Clusters and Superstructures

The aim of the present work is to gain new insights into the formation mechanism of CdTe magic-sized clusters (MSCs) at low temperatures, as well as on their evolution towards 1D and 2D nanostructures and assemblies thereof, under mild reaction conditions. The reaction system included toluene as solvent, octylamine as primary alkylamine, trioctylphosphine-Te as chalcogenide precursor and Cd(oleate)2 as metal precursor. UV-Vis absorption spectroscopy and transmission electron microscopy (TEM) were used to analyze samples containing concentrations of octylamine of 0.2, 0.8 and 2 M: well-defined, sharp absorption peaks were observed, with peaks maxima at 449, 417 and 373 nm respectively, and 1D structures with a string-like appearance were displayed in the TEM images. Investigating peaks growth, step-wise peaks shift to lower energies and reverse, step-wise peak shift to higher energies allowed to propose a model to describe the system, based on interconnected [CdTe]x cluster units originating an amine-capped, 1-dimensional, polymer-like structure, in which different degrees of electronic coupling between the clusters are held responsible for the different absorption transitions. The many parameters involved in the synthesis procedure were then investigated, starting from the Cd:Te ratio, the role of the amine, the use of different phosphine-Te and Cd precursors. The results allowed to gain important information of the reaction mechanism, as well as on the different behavior of the species featuring the sharp absorption peaks in each case. Using Cd(acetate)2 as metal precursor, 2D structures were found to evolve from the MSCs solutions over time, and their tendency to self-assemble was then analyzed employing two amines of different alkyl chain length, octylamine (C-8) and oleylamine (C-18). Their co-presence led to the formation of free-floating triangular nanosheets, which tend to readily aggregate if only octylamine is present in solution.

Cosmology from probe combination: synergies between the number counts of galaxy clusters and cosmic voids

This Thesis work concerns the complementary study of the abundance of galaxy clusters and cosmic voids identified in cosmological simulations, at different redshifts. In particular, we focus our analyses on the combination of the cosmological constraints derived from these probes, which can be considered statistically independent, given the different aspects of Universe density field they map. Indeed, we aim at showing the orthogonality of the derived cosmological constraints and the resulting impressive power of the combination of these probes. To perform this combination we apply three newly implemented algorithms that allow us to combine independent probes. These algorithms represent a flexible and user-friendly tool to perform different techniques for probe combination and are implemented within the environment provided by the large set of free software C++/Python CosmoBolognaLib. All the new implemented codes provide simple and flexible tools that will be soon applied to the data coming from currently available and next-generation wide-field surveys to perform powerful combined cosmological analyses.

The relation between mass and concentration in x-ray galaxy clusters at high-redshift.

In this Thesis work we have studied the properties of high-redshift galaxy clusters through the X-ray emission from their intracluster gas. In particular, we have focused on the relation between concentration and mass that is related to the density of the universe at the formation time of the clusters and therefore, it is a powerful cosmological probe. Concentration is expected to be a decreasing function of mass but a complete characterization of this relation has not been reached yet. We have analysed 22 clusters observed withe the Chandra satellite at high redshift and we have investigated the concentration-mass relation.

Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models

Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic search or search with meaning can refer to an understanding of the query, instead of simply finding words matches and, in general, it represents knowledge in a way suitable for retrieval. We chose to investigate a new self-supervised strategy to handle the training of unlabeled data based on the creation of pairs of ’artificial’ queries and the respective positive passages. We claim that by removing the reliance on labeled data, we may use the large volume of unlabeled material on the web without being limited to languages or domains where labeled data is abundant.