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Abstract:The main objectives was to investigate and enhance the short circuit current density, J sc and also to improve the efficiency of silicon solar cell by fabricating a layer of silicon dioxide (SiO 2 ) and silicon nitride (Si 3 N 4 ) coatings on silicon solar cell. This fabrication carried out on high temperature during annealing process from 800-1050°C and variable thickness of antireflection coating (ARC) layer from 50-90 nm thick. The photovoltaic properties of Si 3 N 4 layer have been compared with SiO 2 layer to determine which material is suitable in fabricating single layer ARC. Solar cell simulation could be useful for time saving and cost consumption. Problem statement: The Silvaco software is not widely used in designing the 2D solar cell devices because there are lots of 1D, 2D and 3D-simulation beside Silvaco software such as MicroTec, SCAPS-1D. Approach: The silicon dioxide (SiO 2 ) and silicon nitride (Si 3 N 4 ) coating have been modeled and fabricated on silicon solar cell by using Silvaco software packaging. Results: For SiO 2 results, the FF value is approximately 0.758 and η maximum 9.43%. In annealing process, the temperature becomes higher resulted increasing of pn junction depth. However, not to V oc and J sc values, both parameters were slowly decreased when temperature increased. Meanwhile, when the thickness of SiO 2 layer is increased, the parameters of pn junction depth, J sc , V oc , FF and η were decreased slowly. As for Si 3 N 4 result, the calculated FF approximately 0.758 and η maximum is 9.57%. During annealing process, the temperature increasing constantly follows the increasing of pn junction depth and J sc , meanwhile the V oc is decreased slowly. In variable Si 3 N 4 thickness simulation, the output parameters of pn junction depth, J sc , V oc , FF and η were decreased when the thickness increased 10 nm each simulation. Conclusion: The optimum temperature during annealing process for SiO 2 is 950°C, while for Si 3 N 4 is 1050°C. For the thickness analysis, the optimum ARC thickness for SiO 2 and Si 3 N 4 layer is 50 nm both.

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In many applications materials suffer repeated dynamic strain cycles, including various materials ranging from semiconductors (photovoltaic absorber layers) to fuel cell electrolytes and refractory bricks. These cycles of incident strain cause shockwaves which propagate throughout the bulk. These shockwaves can leave behind defect states in the material causing minor residual strain. However, as the number of defect states increases, this minor residual strain becomes significant [1] . This experiment utilized the mythen position sensitive detector on beamline I11 at Diamond to take very short exposures of the sample (1ms) during strain propagation [2] . Strain was generated in granular aluminazirconia based ceramics using a 125W CO 2 laser set to use a very short burst of light designed to simulate a 'shock'. Both laser and detector were triggered by a pulse generator which allowed for very short tuneable delays (~μs) between shock and exposure. The distance of the exposure site from the shock site was varied in order to monitor the magnitude w.r.t the distance from the shock site. Due to the small number of incident photons available on such short timescales, the resulting 1ms exposures are then summed together. These summed exposures form longer ~1s diffraction patterns which contain appropriate statistics for analysis. Topas academic was used to analyse the patterns using a Pawley-LeBail fit. Preliminary results show that at a shock/exposure time delay of 10μs, the most intense point of strain is not at the shock site, but approximately 1mm away. This indicates that the strain observed is dominated by the shockwave rather than by thermal expansion, which would cause maximum strain at the laser impact site. Further analysis shows preferential expansion of the unit cell in both directions 'a' and 'c' of the monoclinic zirconia phase. The cause of these directions being preferentially effected is to be investigated with further analysis of the data.

This paper presents the time-series identification a variable-amplitude (VA) strain signal on lower arm suspension component in terms of time-series component analysing and correlate to the fatigue damage properties. The identification technique was used is called classical decomposition method, to classify the strain data into trend, cyclical, seasonal and irregular components. The time history plot of a study case showed the fatigue data contains high and low amplitude events and has resulted the highest amplitude for a pavé, highway and campus are 224 μ, 321 μ and 619 μ, respectively. The trend pattern of a fatigue strain data is a nonstationary series in variance and mean, where a campus data produced highest slope of 31.210 -4 compared to the others. By observing the cyclic movement of the moving average plot, the fatigue strain data contained expansion, contraction and random background. The autocorrelation plot is weak in identifying seasonal pattern, but the autocorrelation coefficient, r 1 values are statistically significant and show a positive serial correlation. Based on residual plot in irregular analysis, the residuals pattern is considered random. As to correlate the fatigue characteristic and time-series component, it was found a campus data produced highest value of fatigue damage. This study discovered a slope of a linear trend pattern could be affected to the fatigue damage properties because the fatigue strain data are nonstationary, VA time-series data and have a random background. Thus, the findings of these characteristics are expected for a nonstationary signal.