University of Science and Technology Beijing

About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.

Piezoelectric effect in chemical vapour deposition-grown atomic-monolayer triangular molybdenum disulfide piezotronics

Abstract: High-performance piezoelectricity in monolayer semiconducting transition metal dichalcogenides is highly desirable for the development of nanosensors, piezotronics and photo-piezotransistors. Here we report the experimental study of the theoretically predicted piezoelectric effect in triangle monolayer MoS2 devices under isotropic mechanical deformation. The experimental observation indicates that the conductivity of MoS2 devices can be actively modulated by the piezoelectric charge polarization-induced built-in electric field under strain variation. These polarization charges alter the Schottky barrier height on both contacts, resulting in a barrier height increase with increasing compressive strain and decrease with increasing tensile strain. The underlying mechanism of strain-induced in-plane charge polarization is proposed and discussed using energy band diagrams. In addition, a new type of MoS2 strain/force sensor built using a monolayer MoS2 triangle is also demonstrated. Our results provide evidence for strain-gating monolayer MoS2 piezotronics, a promising avenue for achieving augmented functionalities in next-generation electronic and mechanical-electronic nanodevices.

Scanning Probe Study on the Piezotronic Effect in ZnO Nanomaterials and Nanodevices

Abstract: ZnO nanomaterials with their unique semiconducting and piezoelectric coupled properties have become promising materials for applications in piezotronic devices including nanogenerators, piezoelectric field effect transistors, and diodes. This article will mainly introduce the research progress on piezotronic properties of ZnO nanomaterials investigated by scanning probe microscopy (SPM) and ZnO-based prototype piezotronic nanodevices built in virtue of SPM, including piezoelectric field effect transistors, piezoelectric diodes, and strain sensors. Additionally, nanodamage and nanofailure of ZnO materials and their relevant piezotronic nanodevices will be critically discussed in their safe service in future nanoelectromechanical system (NEMS) engineering.

GaitPart: Temporal Part-Based Model for Gait Recognition

Abstract: Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies. At present, most gait recognition methods take the whole human body as a unit to establish the spatio-temporal representations. However, we have observed that different parts of human body possess evidently various visual appearances and movement patterns during walking. In the latest literature, employing partial features for human body description has been verified being beneficial to individual recognition. Taken above insights together, we assume that each part of human body needs its own spatio-temporal expression. Then, we propose a novel part-based model GaitPart and get two aspects effect of boosting the performance: On the one hand, Focal Convolution Layer, a new applying of convolution, is presented to enhance the fine-grained learning of the part-level spatial features. On the other hand, the Micro-motion Capture Module (MCM) is proposed and there are several parallel MCMs in the GaitPart corresponding to the pre-defined parts of the human body, respectively. It is worth mentioning that the MCM is a novel way of temporal modeling for gait task, which focuses on the short-range temporal features rather than the redundant long-range features for cycle gait. Experiments on two of the most popular public datasets, CASIA-B and OU-MVLP, richly exemplified that our method meets a new state-of-the-art on multiple standard benchmarks. The source code will be available on https://github.com/ChaoFan96/GaitPart.