Despite the outstanding power conversion efficiency (PCE) of perovskite solar cells (PSCs) achieved over the years, unsatisfactory stability and lead toxicity remain obstacles that limit their competitiveness and large-scale practical deployment. In this study, in situ polymerizing internal encapsulation (IPIE) is developed as a holistic approach to overcome these challenges. The uniform polymer internal package layer constructed by thermally triggered cross-linkable monomers not only solidifies the ionic perovskite crystalline by strong electron-withdrawing/donating chemical sites, but also acts as a water penetration and ion migration barrier to prolong shelf life under harsh environments. The optimized MAPbI3 and FAPbI3 devices with IPIE treatment yield impressive efficiencies of 22.29% and 24.12%, respectively, accompanied by remarkably enhanced environmental and mechanical stabilities. In addition, toxic water-soluble lead leakage is minimized by the synergetic effect of the physical encapsulation wall and chemical chelation conferred by the IPIE. Hence, this strategy provides a feasible route for preparing efficient, stable, and eco-friendly PSCs. 相似文献
Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (FeMC)-carbide MXene (Mo2TiC2Tx) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of FeMC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the FeMC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from FeMC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm−2, respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm−2 and 1.86 V at 0.4 A cm−2 in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications. 相似文献
Mobile Networks and Applications - Artificial fish swarm algorithm (AFS) is used in the field of function optimization problems widely. The traditional AFS algorithm has some problems such as long... 相似文献
Wireless Networks - Friendly spectrum jamming is a flexible scheme to establish secure communications among heterogeneous wireless devices without the need of encryption. Previous works have... 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.