Historic and modern alloys: atmospheric corrosion and development of accelerated ageing methodologies

The atmospheric corrosion of modern and historic alloys used in cultural heritage has been investigated by applying specific accelerated ageing methods. Three main research lines were carried out, involving different materials. In the first part, the atmospheric corrosion of a modern Cu-3Si-1Mn bronze was investigated through accelerated ageing tests simulating outdoor runoff conditions. The corrosion processes were evaluated through different analyses, and the results obtained were compared to those of a traditional quaternary bronze. The second line was carried out to characterise historic aluminium alloys used in aeronautics to develop and apply innovative protection strategies for their conservation. Historic wrecks were identified and characterised through micro and macroscale observations. Moreover, accelerated ageing tests were performed on both historic and modern alloys to compare their behaviour and select the best modern substrate to be used for the development of effective coatings. The third research line aimed to develop accelerate sampling and ageing methods to investigate the role of particulate matter (PM) in the atmospheric corrosion of bronzes and metals in general. The first approach consisted in the fine-tuning of an efficient accelerated method for ambient PM sampling on bronze specimens followed by their accelerated ageing, in order to establish a correlation between the PM and the substrate’s corrosion. After the accelerated ageing of the specimens, the corrosion was evaluated by surface characterisation and correlated to the PM features. The second approach consisted in the development of a synthetic PM formulation and of an artificial deposition method, which was performed by spraying mixtures containing the main PM inorganic fractions on a G-85 bronze with an airbrush. The deposition efficiency was assessed, and the effect of synthetic PM on the bronze corrosion was evaluated. The results were compared to those obtained by ambient PM deposition.

Green approach to Palladium Cross-Coupling reactions and development of new methodologies based on highly abundant metals

Transition metal catalyzed cross-coupling reactions represent among the most versatile and useful tools in organic synthesis for the carbon-carbon (C-C) bond formation and have a prominent role in both the academic and pharmaceutical segments. Among them, palladium catalyzed cross-coupling reactions are currently the most versatile. In this thesis, the applications, impact and development of green palladium cross-coupling reactions are discussed. Specifically, we discuss the translation of the Twelve Principles of Green Chemistry and their applications in pharmaceutical organometallic chemistry to stimulate the development of cost-effective and sustainable catalytic processes for the synthesis of active pharmaceutical ingredients (API). The Heck-Cassar-Sonogashira (HCS) and the Suzuki-Miyaura (SM) protocols, using HEP/H2O as green mixture and sulfonated phosphine ligands, allowed to recycle and recover the catalyst, always guaranteeing high yields and fast conversion under mild conditions, with aryl iodides, bromides, triflates and chlorides. No catalyst leakage or metal contamination of the final product were observed during the HCS and SM reactions, respecting the very low limits for metal impurities in medicines established by the International Conference of Harmonization Guidelines Q3D (ICH Q3D). In addition, a deep understanding of the reaction mechanism is very important if the final target is to develop efficient protocols that can be applied at industrial level. Experimental and theoretical studies pointed out the presence of two catalytic cycles depending on the counterion, shedding light on the role of base in catalyst reduction and acetylene coordination in the HCS coupling. Finally, the development of a cross-coupling reaction to form aryldifluoronitriles in the presence of copper is discussed, highlighting the importance of inserting fluorine atoms within biological structures and the use of readily available metals such as copper as an alternative to palladium.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.

Development of molecular-level switches and implementation into novel nanostructures and smart materials

The work presented in this thesis deals with the design, synthesis and investigation of (supra)molecular switches, and their implementation into novel nanostructures and smart devices. Part A deals with investigation of fundamental properties of Donor Acceptor Stenhouse Adducts (DASAs) as well as their implementation into polymer matrices in order to construct novel smart materials. Part B deals with the implementation of azobenzene photoswitches into pseudorotaxanes and the investigation of the effect of light-driven isomerization on the self-assembly and disassembly processes.