Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Direct Growth of MoO2/Reduced Graphene Oxide Hollow Sphere Composites as Advanced Anode Materials for Potassium‐Ion Batteries

Abstract: Hollow MoO2 /reduced graphene oxide (MoO2 /rGO) sub-microsphere composites have been fabricated through a simple hydrothermal approach followed by a heat treatment process. When employed as an anode material for potassium-ion batteries, the as-synthesized MoO2 /rGO composite can deliver an initial charge specific capacity of 367.2 mAh g-1 at 50 mA g-1 , and its reversible capacity is 218.9 mAh g-1 after 200 cycles. Even when cycled at 500 mA g-1 , a high charge specific capacity of 104.2 mAh g-1 is achieved after 500 cycles. The excellent cycling capability and rate performance may be ascribed to the synergistic effects of the reduced graphene oxide and the hollow MoO2 spheres, which can increase the electrical conductivity of the composite, as well as resisting the strain arising from the repeated discharge-charge processes. These results indicate that the MoO2 /rGO hollow sphere composites are promising negative electrode materials for potassium-ion batteries.

Identifying influential spreaders by gravity model

Abstract: Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.