Teos-based consolidants for stone conservation: evaluation of the effects and testing by x-ray imaging

Natural stones have been widely used in the construction field since antiquity. Building materials undergo decay processes due to mechanical,chemical, physical and biological causes that can act together. Therefore an interdisciplinary approach is required in order to understand the interaction between the stone and the surrounding environment. Utilization of buildings, inadequate restoration activities and in general anthropogenic weathering factors may contribute to this degradation process. For this reasons, in the last few decades new technologies and techniques have been developed and introduced in the restoration field. Consolidants are largely used in restoration and conservation of cultural heritage in order to improve the internal cohesion and to reduce the weathering rate of building materials. It is important to define the penetration depth of a consolidant for determining its efficacy. Impregnation mainly depends on the microstructure of the stone (i.e. porosity) and on the properties of the product itself. Throughout this study, tetraethoxysilane (TEOS) applied on globigerina limestone samples has been chosen as object of investigation. After hydrolysis and condensation, TEOS deposits silica gel inside the pores, improving the cohesion of the grains. X-ray computed tomography has been used to characterize the internal structure of the limestone samples,treated and untreated with a TEOS-based consolidant. The aim of this work is to investigate the penetration depth and the distribution of the TEOS inside the porosity, using both traditional approaches and advanced X-ray tomographic techniques, the latter allowing the internal visualization in three dimensions of the materials. Fluid transport properties and porosity have been studied both at macroscopic scale, by means of capillary uptake tests and radiography, and at microscopic scale,investigated with X-ray Tomographic Microscopy (XTM). This allows identifying changes in the porosity, by comparison of the images before and after the treatment, and locating the consolidant inside the stone. Tests were initially run at University of Bologna, where characterization of the stone was carried out. Then the research continued in Switzerland: X-ray tomography and radiography were performed at Empa, Swiss Federal Laboratories for Materials Science and Technology, while XTM measurements with synchrotron radiation were run at Paul Scherrer Institute in Villigen.

Synthesis of novel cobalt and iron complexes for sustainable catalytic hydrogenations

Aim of the present work of thesis is to synthesize new non-noble metal based complexes to be employ in redox reactions by a metal-ligand cooperative mechanism. The need of replacing toxic and expensive precious metal complexes with more available and benign metals, has led to the development of new compounds based on cobalt and iron, which are the metals investigated in this study. A carbonyl-tetrahydroborato-bis[(2-diisopropylphosphino)ethyl]amine-cobalt complex bearing a PNP-type ligand is synthesized by a three-step route. Optimization attempt of reaction route were assessed in order to lowering reaction times and solvent waste. New cobalt complex has been tested in esters hydrogenation as well as in acceptorless dehydrogenative coupling of ethanol. Other varieties of substrates were also tested in order to evaluate any possible applications. Concerning iron complex, dicarbonyl-(η4-3,4-bis(4-methoxyphenyl)-2,5-diphenylcyclopenta-2,4-dienone)(1,3-dimethyl-ilidene)iron is synthesized by a three steps route, involving transmetallation of a silver complex, derived from an imidazolium salt, to iron complex. In order to avoid solvent waste, optimization is assessed. Studies were performed to assess activity of triscarbonyl iron precursor toward imidazolium salt and silver complexes.

Produzione di alcol furfurilico da furfurale in fase gas con catalizzatori a base di CaO

The hydrogenation of biomass-derived molecules is a key reaction in upgrading these compounds into chemicals and fuels. The use of catalytic transfer hydrogenation, employing alcohols as hydrogen sources, offers an alternative approach to this process, avoiding the use of H2 under high pressure and precious metal catalysts. In this work, the gas-phase conversion of biomass-derived furfural into furfuryl alcohol and 2-methylfuran was studied, using methanol as the H-transfer agent and CaO-based catalysts. The results obtained with this catalyst were compared with those obtained by using MgO, which due to its basic properties and to its high surface area, at present appears to be among the best basic catalysts used for the conversion of biomass-derived molecules. Pure CaO, despite having a very low surface area, compared to MgO catalyst (5 m2/g vs. 172 m2/g), was shown to reduce furfural into its corresponding unsaturated alcohol at 350°C, thus allowing selective H-transfer from methanol to the substrate. These results highlight the potential application of the H-transfer reaction over CaO based catalysts as an efficient process for the selective reduction of biomass-derived molecules.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.