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 & Graphene. The organization has 59583 authors who have published 56840 publications receiving 753549 citations. The organization is also known as: Shànghǎi Dàxué.

Effects of Sodium on the Catalytic Performance of CoMn Catalysts for Fischer–Tropsch to Olefin Reactions

Abstract: The effects of a sodium (Na) promoter on the catalytic performance of cobalt-manganese (CoMn) catalysts for Fischer–Tropsch to olefin (FTO) reactions were investigated. For the sample without Na, Co0 was found to be the active phase for the traditional Co-based Fischer–Tropsch reaction with low CO2 selectivity. The olefin/paraffin (O/P) ratio was found to be low with a C2–4= selectivity of only 15.4 C%. However, with the addition of Na, cobalt carbide (Co2C) quadrangular nanoprisms with the (101) and (020) facets exposed were formed. The Co2C nanoprisms displayed a high C2–4= selectivity (54.2 C%) as well as a low methane selectivity (5.9 C%) under mild reaction conditions. The O/P ratio for C2–4 reached 23.9, and the product distribution deviated greatly from the classical Anderson–Schulz–Flory (ASF) distribution. Co2C nanoprisms were considered to be an effective FTO active phase with strong facet effects. The Na promoter played a key role in the evolution of the FTO catalysts. The addition of Na, which...

Multi-Agent Consensus With Relative-State-Dependent Measurement Noises

Abstract: In this note, the distributed consensus corrupted by relative- state-dependent measurement noises is considered. Each agent can mea- sure or receive its neighbors' state information with random noises, whose intensity is a vector function of agents' relative states. By investigating the structure of this interaction and the tools of stochastic differential equa- tions, we develop several small consensus gain theorems to give sufficient conditions in terms of the control gain, the number of agents and the noise intensity function to ensure mean square (m.s.) and almost sure (a.s.) consensus and quantify the convergence rate and the steady-state error. Especially, for the case with homogeneous communication and control channels, a necessary and sufficient condition to ensure m.s. consensus on the control gain is given and it is shown that the control gain is independent of the specific network topology, but only depends on the number of nodes and the noise coefficient constant. For symmetric measurement models, the almost sure convergence rate is estimated by the Iterated Logarithm Law of Brownian motions. Index Terms—Distributed consensus, distributed coordination, fading channel, measurement noises, multi-agent system.