Glycerol oxidehydration to acrylic acid on complex mixed-metal oxides

The project of this Ph.D. thesis is based on a co-supervised collaboration between Università di Bologna, ALMA MATER STUDIORUM (Italy) and Instituto de Tecnología Química, Universitat Politècnica de València ITQ-UPV (Spain). This Ph.D. thesis is about the synthesis, characterization and catalytic testing of complex mixed-oxide catalysts mainly related to the family of Hexagonal Tungsten Bronzes (HTBs). These materials have been little explored as catalysts, although they have a great potential as multifunctional materials. Their peculiar acid properties can be coupled to other functionalities (e.g. redox sites) by isomorphous substitution of tungsten atoms with other transition metals such as vanadium, niobium and molybdenum. In this PhD thesis, it was demonstrated how it is possible to prepare substituted-HTBs by hydrothermal synthesis; these mixed-oxide were fully characterize by a number of physicochemical techniques such as XPS, HR-TEM, XAS etc. They were also used as catalysts for the one-pot glycerol oxidehydration to acrylic acid; this reaction might represent a viable chemical route to solve the important issue related to the co-production of glycerin along the biodiesel production chain. Acrylic acid yields as high as 51% were obtained and important structure-reactivity correlations were proved to govern the catalytic performance; only fine tuning of acid and redox properties as well as the in-framework presence of vanadium are fundamental to achieve noteworthy yields into the acid monomer. The overall results reported herein might represent an important contribution for future applications of HTBs in catalysis as well as a general guideline for a multifaceted approach for their physicochemical characterization.

Novel developments of atropisomeric organic molecules. Design of atropisomeric drugs, fluorescent compounds and investigation of reaction mechanisms.

In recent years, the study of restricted rotation bonds in organic compounds has aroused increasing interest. The reason is that this characteristic can lead to obtaining new properties in organic compounds. In this research thesis, an intense investigation was carried out using DFT calculations and experimental evaluation of the barriers to rotational energies, in order to discover new properties deriving from the restricted rotation bonds. Research has been developed in various fields of organic chemistry, ranging from drugs (the atropisomeric atorvastatin in Chapter 3) to luminescent compounds (aryls amino borane in Chapter 4). Furthermore, an organocatalytic central to axial conversion mechanism was investigated through DFT calculations, finding out interesting outcomes (Chapter 5). Finally, a project in collaboration with Dr. Farran and Prof. Vanthuyne of the Aix-Marseille University was done to investigate the interactions in transition states of rotational barriers.

Supervised and weakly supervised counting-by-segmentation: the fluorescent microscopy use case

This thesis focuses on automating the time-consuming task of manually counting activated neurons in fluorescent microscopy images, which is used to study the mechanisms underlying torpor. The traditional method of manual annotation can introduce bias and delay the outcome of experiments, so the author investigates a deep-learning-based procedure to automatize this task. The author explores two of the main convolutional-neural-network (CNNs) state-of-the-art architectures: UNet and ResUnet family model, and uses a counting-by-segmentation strategy to provide a justification of the objects considered during the counting process. The author also explores a weakly-supervised learning strategy that exploits only dot annotations. The author quantifies the advantages in terms of data reduction and counting performance boost obtainable with a transfer-learning approach and, specifically, a fine-tuning procedure. The author released the dataset used for the supervised use case and all the pre-training models, and designed a web application to share both the counting process pipeline developed in this work and the models pre-trained on the dataset analyzed in this work.