Nano-fabrication of complex functional structures using non- conventional lithography

In Chapter 1 I will present a brief introduction on the state of art of nanotechnologies, nanofabrication techniques and unconventional lithography as a technique to fabricate the novel electronic device as resistive switch so-called memristor is shown. In Chapter 2 a detailed description of the main fabrication and characterization techniques employed in this work is reported. Chapter 3 parallel local oxidation lithography (pLOx) describes as a main technique to obtain accurate patterning process. All the effective parameters has been studied and the optimized condition observed to highly reproducible with excellent patterned nanostructures. The effect of negative bias, calls local reduction (LR) studied. Moreover, the use of AC bias shows faster patterning process respect to DC bias. In Chapter 4 (metal/ e-SiO2/ Si nanojunction) it is shown how the electrochemical oxide nanostructures by using pLOx can be used in the fabrication of novel devices call memristor. We demonstrate a new concept, based on conventional materials, where the lifetime problem is resolved by introducing a “regeneration” step, which restores the nano-memristor to its pristine condition by applying an appropriate voltage cycle. In Chapter 5 (Graphene/ e-SiO2/ Si), Graphene as a building block material is used as an electrode to selectively oxidize the silicon substrate by pLOx set up for the fabrication of novel resistive switch device. In Chapter 6 (surface architecture) I will show another application of pLOx in biotechnology is shown. So the surface functionalization combine with nano-patterning by pLOx used to design a new surface to accurately bind biomolecules with the possibility of studying those properties and more application in nano-bio device fabrication. So, in order to obtain biochips, electronic and optical/photonics devices Nano patterning of DNA used as scaffolds to fabricate small functional nano-components.

Metodi di ottimizzazione morfologica nel progetto preliminare di propulsori aeronautici avanzati

The aim of this Doctoral Thesis is to develop a genetic algorithm based optimization methods to find the best conceptual design architecture of an aero-piston-engine, for given design specifications. Nowadays, the conceptual design of turbine airplanes starts with the aircraft specifications, then the most suited turbofan or turbo propeller for the specific application is chosen. In the aeronautical piston engines field, which has been dormant for several decades, as interest shifted towards turboaircraft, new materials with increased performance and properties have opened new possibilities for development. Moreover, the engine’s modularity given by the cylinder unit, makes it possible to design a specific engine for a given application. In many real engineering problems the amount of design variables may be very high, characterized by several non-linearities needed to describe the behaviour of the phenomena. In this case the objective function has many local extremes, but the designer is usually interested in the global one. The stochastic and the evolutionary optimization techniques, such as the genetic algorithms method, may offer reliable solutions to the design problems, within acceptable computational time. The optimization algorithm developed here can be employed in the first phase of the preliminary project of an aeronautical piston engine design. It’s a mono-objective genetic algorithm, which, starting from the given design specifications, finds the engine propulsive system configuration which possesses minimum mass while satisfying the geometrical, structural and performance constraints. The algorithm reads the project specifications as input data, namely the maximum values of crankshaft and propeller shaft speed and the maximal pressure value in the combustion chamber. The design variables bounds, that describe the solution domain from the geometrical point of view, are introduced too. In the Matlab® Optimization environment the objective function to be minimized is defined as the sum of the masses of the engine propulsive components. Each individual that is generated by the genetic algorithm is the assembly of the flywheel, the vibration damper and so many pistons, connecting rods, cranks, as the number of the cylinders. The fitness is evaluated for each individual of the population, then the rules of the genetic operators are applied, such as reproduction, mutation, selection, crossover. In the reproduction step the elitist method is applied, in order to save the fittest individuals from a contingent mutation and recombination disruption, making it undamaged survive until the next generation. Finally, as the best individual is found, the optimal dimensions values of the components are saved to an Excel® file, in order to build a CAD-automatic-3D-model for each component of the propulsive system, having a direct pre-visualization of the final product, still in the engine’s preliminary project design phase. With the purpose of showing the performance of the algorithm and validating this optimization method, an actual engine is taken, as a case study: it’s the 1900 JTD Fiat Avio, 4 cylinders, 4T, Diesel. Many verifications are made on the mechanical components of the engine, in order to test their feasibility and to decide their survival through generations. A system of inequalities is used to describe the non-linear relations between the design variables, and is used for components checking for static and dynamic loads configurations. The design variables geometrical boundaries are taken from actual engines data and similar design cases. Among the many simulations run for algorithm testing, twelve of them have been chosen as representative of the distribution of the individuals. Then, as an example, for each simulation, the corresponding 3D models of the crankshaft and the connecting rod, have been automatically built. In spite of morphological differences among the component the mass is almost the same. The results show a significant mass reduction (almost 20% for the crankshaft) in comparison to the original configuration, and an acceptable robustness of the method have been shown. The algorithm here developed is shown to be a valid method for an aeronautical-piston-engine preliminary project design optimization. In particular the procedure is able to analyze quite a wide range of design solutions, rejecting the ones that cannot fulfill the feasibility design specifications. This optimization algorithm could increase the aeronautical-piston-engine development, speeding up the production rate and joining modern computation performances and technological awareness to the long lasting traditional design experiences.