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.

A bilateral interfacial passivation strategy promoting efficiency and stability of perovskite quantum dot light-emitting diodes

Abstract: Perovskite quantum-dot-based light-emitting diodes (QLEDs) possess the features of wide gamut and real color expression, which have been considered as candidates for high-quality lightings and displays. However, massive defects are prone to be reproduced during the quantum dot (QD) film assembly, which would sorely affect carrier injection, transportation and recombination, and finally degrade QLED performances. Here, we propose a bilateral passivation strategy through passivating both top and bottom interfaces of QD film with organic molecules, which has drastically enhanced the efficiency and stability of perovskite QLEDs. Various molecules were applied, and comparison experiments were conducted to verify the necessity of passivation on both interfaces. Eventually, the passivated device achieves a maximum external quantum efficiency (EQE) of 18.7% and current efficiency of 75 cd A−1. Moreover, the operational lifetime of QLEDs is enhanced by 20-fold, reaching 15.8 h. These findings highlight the importance of interface passivation for efficient and stable QD-based optoelectronic devices. Perovskite quantum-dots are promising candidates for light-emitting diodes but the defects limit the device performance. Here Xu et al. show a passivation strategy to reduce the defect density at both interfaces, which increases the external quantum efficiency (EQE) and lifetime by more than 2-fold and 20-fold, respectively.

High Photocatalytic Activity of Magnetically Separable Manganese Ferrite–Graphene Heteroarchitectures

Abstract: A simple and straightforward strategy was developed to fabricate magnetically separable MnFe2O4–graphene photocatalysts with differing graphene content. It was found that graphene sheets were fully exfoliated and decorated with MnFe2O4 nanocrystals having an average diameter of 5.65 nm and a narrow particle size distribution. It is very interesting that, although MnFe2O4 alone is photocatalytically inactive under visible light irradiation, the combination of MnFe2O4 nanoparticles with graphene sheets leads to high photocatalytic activity for the degradation of methylene blue under visible light irradiation. The strong magnetic property of MnFe2O4 nanoparticles can be used for magnetic separation in a suspension system, and therefore it does not require additional magnetic components as is the usual case. Consequently, the MnFe2O4–graphene system becomes a dual function photocatalyst. The significant enhancement in photoactivity under visible light irradiation can be ascribed to the reduction of graphene o...

Few-Shot Image Recognition With Knowledge Transfer

Abstract: Human can well recognize images of novel categories just after browsing few examples of these categories. One possible reason is that they have some external discriminative visual information about these categories from their prior knowledge. Inspired from this, we propose a novel Knowledge Transfer Network architecture (KTN) for few-shot image recognition. The proposed KTN model jointly incorporates visual feature learning, knowledge inferring and classifier learning into one unified framework for their optimal compatibility. First, the visual classifiers for novel categories are learned based on the convolutional neural network with the cosine similarity optimization. To fully explore the prior knowledge, a semantic-visual mapping network is then developed to conduct knowledge inference, which enables to infer the classifiers for novel categories from base categories. Finally, we design an adaptive fusion scheme to infer the desired classifiers by effectively integrating the above knowledge and visual information. Extensive experiments are conducted on two widely-used Mini-ImageNet and ImageNet Few-Shot benchmarks to evaluate the effectiveness of the proposed method. The results compared with the state-of-the-art approaches show the encouraging performance of the proposed method, especially on 1-shot and 2-shot tasks.