Characterization of innovative kerfless cutting of monocrystalline silicon wafers for photovoltaic applications

Nowadays renewable energies are a hot research topic, and the goal is to improve cell efficiency and reduce production costs, aiming to make the use of photovoltaics increasingly widespread and convenient. Monocrystalline silicon solar cells are leaders in the photovoltaic market. However, market-established cutting techniques produce a consistent amount of material waste when cutting ingots into wafers. The“Stress-induced LIft-Off Method” (SLIM) is emerging in recent years as an alternative, more sustainable separation technique, which reduces material loss and can lead to obtaining increasingly thinner wafers, further reducing the required amount of silicon. This thesis presents the micro-characterization of the separated wafers with the SLIM technique. The wafers were obtained with a two-step procedure. First, a layer of defects was induced in the silicon using ultra-short medium-infrared laser pulses. Then, the material was deposited on one of the sides and induced stress in the silicon, such as to further weaken it. In this way, only rapid cooling is required for detachment to occur. The obtained results indicate that the SLIM-cut technique halves the minority carriers’ lifetime. There is no amorphization, crystal disorder or high-pressure phases. However, changes in the Raman spectra suggest that tensile stress may have been produced on these surface layers by the separation process. The AFM topography highlights surface irregularities, which may be removed with a polishing step. The surface also shows laser-modified regions, which are evident in SEM images, but not in AFM topographies, suggesting a charging effect due to electron bombardment. Lastly, the electrical characterization by conductive AFM lacks any changes in the conductive behaviour of the material where the laser-modified areas should be located. In conclusion, these preliminary results are promising to carry out a systematic characterization of this technique of this innovative SLIM technique.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Deposizione elettrochimica di esacianoferrato di rame tramite un metodo "a due step"; caratterizzazione e valutazione delle prestazioni

In this thesis we have identified two electrochemical procedures for preparing two compounds of copper hexacyanoferrate (CuHCF) films with different compositions and structures. The deposition were carried out using a “two steps” method consisting in electrochemical oxidation of previously deposited metallic copper on carbon substrates (glassy carbon and graphite foil electrodes) in K3[Fe(CN)6] solution. Both films, CuHCF-methodA and CuHCF-methodB, were characterized by cyclic voltammetry (GC) and their study using XANES spectroscopy revealed evidence of different structures. Additionally, insertion and extraction of different cations (Na+, K+, Mg2+, Al3+ and Cs+) were performed and the results indicate that CuHCF-methodA has slightly better performances and operational stability than CuHCF-methodB. Data from galvanostatic charge-discharge tests confirme the latter observation. An application for amperometric detection of H2O2 and SEM micrographs are also reported for both films (method A and B). Comparing these results with a previous work of our research group, seems that the deposition of two different compounds using methodA and methodB is due to the different stoichiometry of ions Cu2+ e [Fe(CN)6]3– created near electrode surface during the dissolution step.