Search

As a new type of single element direct-bandgap semiconductor, black phosphorus (BP) shows many excellent characteristics due to its unique two-dimensional (2D) structure, which has great potential in the fields of optoelectronics, biology, sensing, information, and so on. In recent years, a series of physical and chemical methods have been developed to modify the surface of 2D BP to inhibit its contact with water and oxygen and improve the stability and physical properties of 2D BP. By doping and coating other materials, the stability of BP applied in the anode of a lithium-ion battery was improved. In this work, the preparation, passivation, and lithium-ion battery applications of two-dimensional black phosphorus are summarized and reviewed. Firstly, a variety of BP preparation methods are summarized. Secondly, starting from the environmental instability of BP, different passivation technologies are compared. Thirdly, the applications of BP in energy storage are introduced, especially the application of BP-based materials in lithium-ion batteries. Finally, based on preparation, surface functionalization, and lithium-ion battery of 2D BP, the current research status and possible future development direction are put forward.

The "FeMo cofactors" in biological nitrogenase play a decisive role in nitrogen reduction. Herein, a novel bionic Fe/Mo bimetallene was applied in photocatalytic nitrogen reduction. The surface coating Fe/Mo bimetallene of Bi 2 Mo 0.3 W 0.7 O 6 (BMWO) nanocrystals could effectively promote the separation and transportation of photogenerated carriers by multi-electron redox reactions and deliver 2.8 times longer photo-carrier lifetime. Consequently, the nitrogen fixation activity of Fe/Mo bimetallene-coated BMWO nanocrystal photocatalyst was obviously enhanced (218.93 μmol g -1 h -1 ), which was about 4.8 times that of unmodified BMWO nanocrystals. This work provides a novel approach to design bionic Fe/Mo bimetallene-modified inorganic semiconductor photocatalysts for nitrogen reduction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Inc. All rights reserved.)

In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC's disadvantages. One model contained the target's prior color information, and the other the target's prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram-Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker's performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.