We report on low-temperature (4 K) photoluminescence of an 8.3% efficient Cu2ZnSnS4 photovoltaic device. Measurements were recorded as a function of excitation intensity, and the evolution of the resulting spectra is discussed. The spectra indicate that the radiative recombination is characteristic of heavily compensated material with a high quasi donor-acceptor pair density, as determined by the relationship between peak height, peak position, and excitation intensity, as well as the carrier lifetimes at different wavelengths. The blue-shift of the defect-derived peak position is used to estimate the quasi donor-acceptor pair spacing and density. The data indicate an average pair spacing of roughly 3.3 nm, yielding an overall total radiative-defect density of ∼1.3 × 1019 cm−3.
With Al2O3 passivation on the surface of Cu(In,Ga)Se2, the integrated photoluminescence intensity can achieve two orders of magnitude enhancement due to the reduction of surface recombination velocity. The photoluminescence intensity increases with increasing Al2O3 thickness from 5 nm to 50 nm. The capacitance-voltage measurement indicates negative fixed charges in the film. Based on the first principles calculations, the deposition of Al2O3 can only reduce about 35% of interface defect density as compared to the unpassivated Cu(In,Ga)Se2. Therefore, the passivation effect is mainly caused by field effect where the surface carrier concentration is reduced by Coulomb repulsion.
We examined the 4 K photoluminescence spectra of over a dozen Cu2ZnSnS4 films and eight devices. We show that samples deficient in zinc show on average a higher quasi donor-acceptor pair (QDAP) density. However, the QDAP density in samples with the same metal composition also varies widely. Devices prepared with similar metal compositions show different open-circuit voltages and fill factors. These metrics are correlated with the concentration of QDAPs in the absorbers. One additional device with insufficient zinc showed the empirically observed low-efficiency expected for this composition. This sample also showed the highest quasi donor-acceptor pair density of all the devices measured.
In this study, we proposed a smart detection method for abnormal breasts in digital mammography. Firstly, preprocessing was carried out to deaden noises, enhance images, and remove background and pectoral muscles. Secondly, fractional Fourier entropy was employed to extract global features. Thirdly, the Welch’s t-test was utilized to select important features. Fourthly, the multi-layer perceptron was used as the classifier. Finally, we proposed a novel chaotic adaptive real-coded biogeography-based optimization to train the classifier. We implemented 10-fold cross-validation for statistical analysis. The experimental results showed our method selected in total 23 distinguishing features, and yielded a sensitivity of 92.54%, a specificity of 92.50%, a precision of 92.50%, and an accuracy of 92.52%. This proposed system performs better than five state-of-the-art methods. It is effective in abnormal breast detection.
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