Photoluminescence (PL) from semiconductors can be related with the quasi-Fermi level splitting of the electron-holeensemble from which the radiation originates. For the interpretation of the temperature-dependent c-Si:H PL Planck's generalized law is extended and applied to describe the radiative recombination via band-tail states. This substantial refinement of existing approaches allows for the modeling of temperaturedependent spectra over the entire measured temperature range and furthermore provides access to the quasi-Fermi level splitting (qFL) of electron-hole ensembles in the thin film and furthermore yields quantitative information on band-tail energies. In crystalline silicon, an increase in the quasi-Fermi level splitting causes only an increase in the overall PL-intensity, while the spectral shape of the spectrum is unaffected. In contrast, the PL-spectra of c-Si:H differ with varying qFL-splitting both in intensity and shape. Only by introducing variations of qFL-splitting and constructing an overall PL-spectrum that is a superposition from excitation states with different qFLsplittings, the experimental temperature-dependent PL spectra from c-Si:H can be reconstructed for the entire temperature range.Temperature-dependent PL spectra of c-Si:H silicon (symbols) and fits according to our proposed model (lines).
A number of studies have investigated the training of neural networks with synthetic data for applications in the real world. The aim of this study is to quantify how much real world data can be saved when using a mixed dataset of synthetic and real world data. By modeling the relationship between the number of training examples and detection performance by a simple power law, we find that the need for real world data can be reduced by up to 70% without sacrificing detection performance. The training of object detection networks is especially enhanced by enriching the mixed dataset with classes underrepresented in the real world dataset. The results indicate that mixed datasets with real world data ratios between 5% and 20% reduce the need for real world data the most without reducing the detection performance.
In hybrid solar cells consisting of dye sensitizers incorporated in the i-layer of a microcrystalline silicon (μc-Si:H) pin solar cell the dye sensitizer molecules are embedded in the matrix and enhance the overall absorption of the dye-matrix system due to their high absorption coefficient in the spectral range interesting for photovoltaic applications. This contribution investigates the efficiency improvement of hybrid dye-μc-Si:H solar cells compared to pure μc-Si:H solar cells by simulation. The results indicate that, under optimum conditions, the efficiency can be improved by more than a factor of 1.2 compared to a pure μc-Si:H cell. The thickness reduction for the hybrid system can be as large as 50 % for the same efficiency. However, the efficiency improvement also depends on the amount of additionally induced defects in the matrix by the embedded dye molecules. Therefore, the simulations investigate the performance of the hybrid solar cell for different absorption enhancements and defect densities.
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