Nankai University

About: Nankai University is a education organization based out in Tianjin, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 42964 authors who have published 51866 publications receiving 1127896 citations. The organization is also known as: Nánkāi Dàxué.

Smoothing the energy transfer pathway in quasi-2D perovskite films using methanesulfonate leads to highly efficient light-emitting devices.

Abstract: Quasi-two-dimensional (quasi-2D) Ruddlesden–Popper (RP) perovskites such as BA2Csn–1PbnBr3n+1 (BA = butylammonium, n > 1) are promising emitters, but their electroluminescence performance is limited by a severe non-radiative recombination during the energy transfer process. Here, we make use of methanesulfonate (MeS) that can interact with the spacer BA cations via strong hydrogen bonding interaction to reconstruct the quasi-2D perovskite structure, which increases the energy acceptor-to-donor ratio and enhances the energy transfer in perovskite films, thus improving the light emission efficiency. MeS additives also lower the defect density in RP perovskites, which is due to the elimination of uncoordinated Pb2+ by the electron-rich Lewis base MeS and the weakened adsorbate blocking effect. As a result, green light-emitting diodes fabricated using these quasi-2D RP perovskite films reach current efficiency of 63 cd A−1 and 20.5% external quantum efficiency, which are the best reported performance for devices based on quasi-2D perovskites so far. Owing to large exciton binding energy, quasi-2D perovskite is promising for light-emitting application, yet inhomogeneous phases distribution limits the potential. Here, the authors improve the performance by using MeS additive to regulate the phase distribution and to reduce defect density in the films.

Understanding information inequality: Making sense of the literature of the information and digital divides

Abstract: This paper reviews related research since the early 1990s on the information and digital divides. It shows that, despite their shared concerns with illustrating social inequality through the lens of information resource distribution, the two areas in effect represent two overlapping research communities. The research focus and discourse of the former were primarily shaped by three different theoretical perspectives and were inspired by a fairly strong sense of ethical principles; those of the latter, on the other hand, were shaped primarily by four different political standpoints and were imbued with a fairly strong concern for political and economical interests. The co-existence of multifarious perspectives and standpoints has produced divergent, and sometimes contradictory, research findings and policy recommendations, which inevitably perplex researchers and policy makers. The paper concludes with some suggestions for future research and policy making.

An Iterative and Cooperative Top-Down and Bottom-Up Inference Network for Salient Object Detection

Abstract: This paper presents a salient object detection method that integrates both top-down and bottom-up saliency inference in an iterative and cooperative manner The top-down process is used for coarse-to-fine saliency estimation, where high-level saliency is gradually integrated with finer lower-layer features to obtain a fine-grained result The bottom-up process infers the high-level, but rough saliency through gradually using upper-layer, semantically-richer features These two processes are alternatively performed, where the bottom-up process uses the fine-grained saliency obtained from the top-down process to yield enhanced high-level saliency estimate, and the top-down process, in turn, is further benefited from the improved high-level information The network layers in the bottom-up/top-down processes are equipped with recurrent mechanisms for layer-wise, step-by-step optimization Thus, saliency information is effectively encouraged to flow in a bottom-up, top-down and intra-layer manner We show that most other saliency models based on fully convolutional networks (FCNs) are essentially variants of our model Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of our proposed saliency inference framework