Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Bismuth Incorporation Stabilized α-CsPbI3 for Fully Inorganic Perovskite Solar Cells

Abstract: All-inorganic CsPbI3 perovskite is emerging to be an alternative light-harvesting material in solar cells owing to the enhanced stability and comparable photovoltaic performance compared to organic–inorganic hybrid perovskites. However, the desirable black phase α-CsPbI3 is not stable at room temperature and degrades rapidly to a nonperovskite yellow phase δ-CsPbI3. Herein, we introduce a compositional engineering approach via incorporating Bi3+ in CsPbI3 to stabilize the α-phase at room temperature. Fully inorganic solar cells based on the Bi-incorporated α-CsPb1–xBixI3 compounds demonstrate a high PCE of 13.21% at an optimal condition (incorporation of 4 mol % Bi3+) and maintain 68% of the initial PCE for 168 h under ambient conditions without encapsulation. This is the first attempt of partial substitution of the “B”-site of the perovskite to stabilize the α-CsPbI3, which paves the way for further developments of such perovskites and other optoelectronic devices.

MemNet: A Persistent Memory Network for Image Restoration

Abstract: Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which results in the prior states/layers having little influence on the subsequent ones. Motivated by the fact that human thoughts have persistency, we propose a very deep persistent memory network (MemNet) that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process. The recursive unit learns multi-level representations of the current state under different receptive fields. The representations and the outputs from the previous memory blocks are concatenated and sent to the gate unit, which adaptively controls how much of the previous states should be reserved, and decides how much of the current state should be stored. We apply MemNet to three image restoration tasks, i.e., image denosing, super-resolution and JPEG deblocking. Comprehensive experiments demonstrate the necessity of the MemNet and its unanimous superiority on all three tasks over the state of the arts. Code is available at this https URL.