Shanghai University

About: Shanghai University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Microstructure & Catalysis. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Hopping transport through defect-induced localized states in molybdenum disulphide

Abstract: Molybdenum disulphide is a novel two-dimensional semiconductor with potential applications in electronic and optoelectronic devices. However, the nature of charge transport in back-gated devices still remains elusive as they show much lower mobility than theoretical calculations and native n-type doping. Here we report a study of transport in few-layer molybdenum disulphide, together with transmission electron microscopy and density functional theory. We provide direct evidence that sulphur vacancies exist in molybdenum disulphide, introducing localized donor states inside the bandgap. Under low carrier densities, the transport exhibits nearest-neighbour hopping at high temperatures and variable-range hopping at low temperatures, which can be well explained under Mott formalism. We suggest that the low-carrier-density transport is dominated by hopping via these localized gap states. Our study reveals the important role of short-range surface defects in tailoring the properties and device applications of molybdenum disulphide.

Li Storage Properties of Disordered Graphene Nanosheets

Abstract: Graphene has aroused intensive interest because of its unique structure, superior properties, and various promising applications. Graphene nanostructures with significant disorder and defects have been considered to be poor materials because disorder and defects lower their electrical conductivity. In this paper, we report that highly disordered graphene nanosheets can find promising applications in high-capacity Li ion batteries because of their exceptionally high reversible capacities (794−1054 mA h/g) and good cyclic stability. To understand the Li storage mechanism of graphene nanosheets, we have prepared graphene nanosheets with structural parameters tunable via different reduction methods including hydrazine reduction, low-temperature pyrolysis, and electron beam irradiation. The effects of these parameters on Li storage properties were investigated systematically. A key structural parameter, Raman intensity ratio of D bands to G bands, has been identified to evaluate the reversible capacity. The gr...

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

Abstract: The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Abstract: Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.

Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries

Abstract: A passivation layer called the solid electrolyte interphase (SEI) is formed on electrode surfaces from decomposition products of electrolytes. The SEI allows Li+ transport and blocks electrons in order to prevent further electrolyte decomposition and ensure continued electrochemical reactions. The formation and growth mechanism of the nanometer thick SEI films are yet to be completely understood owing to their complex structure and lack of reliable in situ experimental techniques. Significant advances in computational methods have made it possible to predictively model the fundamentals of SEI. This review aims to give an overview of state-of-the-art modeling progress in the investigation of SEI films on the anodes, ranging from electronic structure calculations to mesoscale modeling, covering the thermodynamics and kinetics of electrolyte reduction reactions, SEI formation, modification through electrolyte design, correlation of SEI properties with battery performance, and the artificial SEI design. Multi-scale simulations have been summarized and compared with each other as well as with experiments. Computational details of the fundamental properties of SEI, such as electron tunneling, Li-ion transport, chemical/mechanical stability of the bulk SEI and electrode/(SEI/) electrolyte interfaces have been discussed. This review shows the potential of computational approaches in the deconvolution of SEI properties and design of artificial SEI. We believe that computational modeling can be integrated with experiments to complement each other and lead to a better understanding of the complex SEI for the development of a highly efficient battery in the future.