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Perovskite solar cells have become a research hotspot in the field of new energy due to the excellent performance and potential in application, but it still displays some disadvantages such as high defect density and poor stability. In this study, L-arginine, a small molecule of organic matter, was doped into the perovskite precursor solution by comparing the doping effects of various amino acids, and the perovskite solar cells were prepared by the two-element and two-step preparation method. The test results show that L-arginine doping improves the photoelectric performance of the device, and the photoelectric efficiency increases from 18.81% to 21.86%. L-arginine reduces the nonradiative recombination of carriers and increases the average carrier life by reducing the defect density of perovskite layer from 4.83×10 16 cm -1 to 3.45×10 16 cm -1 . In addition, the perovskite grain size increases, grain boundaries decrease, the light absorption ability and stability of the film are enhanced, and the hysteresis effect was also suppressed. Improvement of the photovoltaic performance is due to the passivation of defects by the interaction of various groups of L-arginine with perovskite materials. This study provides a reference method of the optimization of perovskite solar cells.

This paper combines the theory of hesitant fuzzy linguistic term sets (HFLTSs) with two-sided matching decision making (TSMDM). The related definitions of HFLTSs and two-sided matchings (TSMs) are introduced. Then, the problem of TSMDM with HFLTSs is presented. For solving this problem, a model of TSMDM with HFLTSs is developed. The AHP method is used to determine the important degrees of agents of each side. On this base, the model of TSMDM can be changed into a double-goal model with HFLTSs. Then, the double-goal model with HFLTSs is changed into the double-goal model with scores through using the proposed score function. Furthermore, the double-goal model can be changed into a single-goal model by using the linear weighting technique once again. The scheme of TSM can be obtained through solving the single-goal model. At last, an example with sensitive analysis is provided for the illustration of the presented approach of TSM.

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With respect to the two-sided matching problem, where the agents ' preferences