Studio e sviluppo di tecniche per la produzione di nanocompositi a matrice di alluminio

Foundry aluminum alloys play a fundamental role in several industrial fields, as they are employed in the production of several components in a wide range of applications. Moreover, these alloys can be employed as matrix for the development of Metal Matrix Composites (MMC), whose reinforcing phases may have different composition, shape and dimension. Ceramic particle reinforced MMCs are particular interesting due to their isotropic properties and their high temperature resistance. For this kind of composites, usually, decreasing the size of the reinforcing phase leads to the increase of mechanical properties. For this reason, in the last 30 years, the research has developed micro-reinforced composites at first, characterized by low ductility, and more recently nano-reinforced ones (the so called metal matrix nanocomposite, MMNCs). The nanocomposites can be obtained through several production routes: they can be divided in in-situ techniques, where the reinforcing phase is generated during the composite production through appropriate chemical reactions, and ex situ techniques, where ceramic dispersoids are added to the matrix once already formed. The enhancement in mechanical properties of MMNCs is proved by several studies; nevertheless, it is necessary to address some issues related to each processing route, as the control of process parameters and the effort to obtain an effective dispersion of the nanoparticles in the matrix, which sometimes actually restrict the use of these materials at industrial level. In this work of thesis, a feasibility study and implementation of production processes for Aluminum and AlSi7Mg based-MMNCs was conducted. The attention was focused on the in-situ process of gas bubbling, with the aim to obtain an aluminum oxide reinforcing phase, generated by the chemical reaction between the molten matrix and industrial dry air injected in the melt. Moreover, for what concerns the ex-situ techniques, stir casting process was studied and applied to introduce alumina nanoparticles in the same matrix alloys. The obtained samples were characterized through optical and electronic microscopy, then by micro-hardness tests, in order to evaluate possible improvements in mechanical properties of the materials.

A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.