Mechanochemical solvent-free synthesis of Sn-β Zeolite and Ag2O/TiO2 catalysts

Preparations of heterogeneous catalysts are usually complex processes that involve several procedures as precipitation, crystallization and hydrothermal treatments. This processes are really dependent by the operative conditions such as temperature, pH, concentration etc. Hence the resulting product is extremely affected by any possible variations in these parameters making this synthesis really fragile. With the aim to improve these operations has been decided to exploit a new possible strong environment-respectful process by mechanochemical treatment, which permits to carry out solvent free-solvent synthesis exploiting the Mixer Mill MM400 (Retsch) in order to have reproducible results. Two different systems have been studied in this kind of synthesis: a tin β -zeolite tested in a H-trasnfer reaction of cyclohexanone and a silver on titania catalyst used in the fluorination of 2,2 dimethyl glucaric acid. Each catalyst has been characterized by different techniques in order to understand the transformations involved in the mechanochemical treatment.

Synthetic generation of amyloid PET images by non-linear dimensionality reduction inversion

Privacy issues and data scarcity in PET field call for efficient methods to expand datasets via synthetic generation of new data that cannot be traced back to real patients and that are also realistic. In this thesis, machine learning techniques were applied to 1001 amyloid-beta PET images, which had undergone a diagnosis of Alzheimer’s disease: the evaluations were 540 positive, 457 negative and 4 unknown. Isomap algorithm was used as a manifold learning method to reduce the dimensions of the PET dataset; a numerical scale-free interpolation method was applied to invert the dimensionality reduction map. The interpolant was tested on the PET images via LOOCV, where the removed images were compared with the reconstructed ones with the mean SSIM index (MSSIM = 0.76 ± 0.06). The effectiveness of this measure is questioned, since it indicated slightly higher performance for a method of comparison using PCA (MSSIM = 0.79 ± 0.06), which gave clearly poor quality reconstructed images with respect to those recovered by the numerical inverse mapping. Ten synthetic PET images were generated and, after having been mixed with ten originals, were sent to a team of clinicians for the visual assessment of their realism; no significant agreements were found either between clinicians and the true image labels or among the clinicians, meaning that original and synthetic images were indistinguishable. The future perspective of this thesis points to the improvement of the amyloid-beta PET research field by increasing available data, overcoming the constraints of data acquisition and privacy issues. Potential improvements can be achieved via refinements of the manifold learning and the inverse mapping stages during the PET image analysis, by exploring different combinations in the choice of algorithm parameters and by applying other non-linear dimensionality reduction algorithms. A final prospect of this work is the search for new methods to assess image reconstruction quality.