As one of the most promising photovoltaic materials, the efficiency of inorganic–organic hybrid halide perovskite solar cells (PSCs) has reached 25.5% in 2020. However, the stability and hysteresis remain primary challenges before it can become a commercial photovoltaic technology. Therefore, those issues have drawn significant attention for photovoltaic applications. In this work, a study of the PSCs hysteresis improvement is presented based on a combination of first-principles simulations, scanning electron microscopy images, and time-dependent photocurrent measurements. It indicates the hysteresis led by the ion migration and accumulation is mainly localized at the two interfaces: one is between electron transport layer and active layer, and the other is between active layer and hole transport layer. Considering the massive defects at the grain boundaries (GBs), they lower the potential barriers significantly. The defect density at GBs is therefore reduced via the in situ passivation of PbI2 crystals. The hysteresis index is decreased from 22.43% down to 1.04%, and results in an improvement in efficiency from 17.12% up to 20.10%. Following the understanding of defect-induced hysteresis, an approach to improve the hysteresis is provided, which can be integrated into the fabrication process and widely applied to enhance the performance of PSCs. 相似文献
International Journal of Computer Vision - Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset... 相似文献
As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students’ learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students’ graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student’s performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students’ features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.
Neural Computing and Applications - Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this... 相似文献