Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

Biotemplated synthesis of three-dimensional porous MnO/C-N nanocomposites from renewable rapeseed pollen: An anode material for lithium-ion batteries

Abstract: Lithium-ion batteries (LIBs) are currently recognized as one of the most popular power sources available. To construct advanced LIBs exhibiting long-term endurance, great attention has been paid to enhancing their poor cycle stabilities. As the performance of LIBs is dependent on the electrode materials employed, the most promising approach to improve their life span is the design of novel electrode materials. We herein describe the rational design of a three-dimensional (3D) porous MnO/C-N nanoarchitecture as an anode material for long cycle life LIBs based on their preparation from inexpensive, renewable, and abundant rapeseed pollen (R-pollen) via a facile immersion-annealing route. Remarkably, the as-prepared MnO/C-N with its optimized 3D nanostructure exhibited a high specific capacity (756.5 mAh·g−1 at a rate of 100 mA·g−1), long life span (specific discharge capacity of 513.0 mAh·g−1, ~95.16% of the initial reversible capacity, after 400 cycles at 300 mA·g−1), and good rate capability. This material therefore represents a promising alternative candidate for the high-performance anode of next-generation LIBs.

Facile Synthesis of MoS2/Reduced Graphene Oxide@Polyaniline for High-Performance Supercapacitors

Abstract: The molybdenum disulfide/reduced graphene oxide@polyaniline (MoS2/RGO@PANI) was facilely and effectively prepared through a two-stage synthetic method including hydrothermal and polymerized reactions. The rational combination of two components allowed polyaniline (PANI) to uniformly cover the outer face of molybdenum disulfide/reduced graphene oxide (MoS2/RGO). The interaction between the two initial electrode materials produced a synergistic effect and resulted in outstanding energy storage performance in terms of greatest capacitive property (1224 F g–1 at 1 A g–1), good rate (721 F g–1 at 20 A g–1), and cyclic performance (82.5% remaining content after 3000 loops). The symmetric cell with MoS2/RGO@PANI had a good capacitive property (160 F g–1 at 1 A g–1) and energy and power density (22.3 W h kg –1 and 5.08 kW kg–1).

Discrete-Time Terminal Sliding Mode Control Systems Based on Euler's Discretization

Abstract: In this technical note, the dynamical behaviors of discrete-time terminal sliding mode control systems based on Euler's discretization is investigated. Based on a recursive analysis, the boundedness for the steady states of the system is established. Theoretical analysis shows that the discrete-time terminal sliding mode control method can offer a higher output tracking precision than the discrete-time linear sliding mode control method. Simulations are given to verify the theoretical results.

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Abstract: Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at this https URL.