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A hierarchically structured tin-cobalt composite with an enhanced electronic effect for high-performance CO_(2) electroreduction in a wide potential range

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Journal of Energy Chemistry 2023年第1期76卷 462-469,I0012页

作者： Xingxing Jiang Xuan Li Yan Kong Chen Deng Xiaojie Li Qi Hu Hengpan Yang Chuanxin He Institute of Microscale Optoelectronics College of Chemistry and Environmental EngineeringShenzhen UniversityShenzhen 518060GuangdongChina

Earth-abundant and nontoxic Sn-based materials have been regarded as promising catalysts for the electrochemical conversion of CO_(2)to C1 products,e.g.,CO and formate.However,it is still difficult for Snbased materials to obtain satisfactory performance at low-to-moderate overpotentials.Herein,a simple and facile electrospinning technique is utilized to prepare a composite of a bimetallic Sn-Co oxide/carbon matrix with a hollow nanotube structure(Sn Co-HNT).Sn Co-HNT can maintain>90%faradaic efficiencies for C1 products within a wide potential range from-0.6 VRHE to-1.2 VRHE,and a highest 94.1%selectivity towards CO in an H-type cell.Moreover,a 91.2%faradaic efficiency with a 241.3 m A cm^(-2)partial current density for C1 products could be achieved using a flow cell.According to theoretical calculations,the fusing of Sn/Co oxides on the carbon matrix accelerates electron transfer at the atomic level,causing electron deficiency of Sn centers and reversible variation between Co^(2+)and Co^(3+)centers.The synergistic effect of the Sn/Co composition improves the electron affinity of the catalyst surface,which is conducive to the adsorption and stabilization of key intermediates and eventually increases the catalytic activity in CO_(2)electroreduction.This study could provide a new strategy for the construction of oxide-derived catalysts for CO_(2)electroreduction.

关键词： hierarchic structure Tin-cobalt bimetallic oxide Electronic effect CO_(2)electroreduction Wide potential range

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Application on job-shop scheduling with genetic algorithm based on the mixed strategy

Application on job-shop scheduling with genetic algorithm ba...

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2009中国控制与决策会议

作者： Liang Xu~1,Wang Shuang~1,Huang Ming~1 1.Software Technology Institute,Dalian Jiao Tong University,Dalian 116028

Adaptive Genetic Algorithm for solving job-shop scheduling problems has the defects of the slow convergence speed on the early stage and it is easy to trap into local optimal solutions,this paper introduces a time operator depending on the time evolution to solve this problem.Its purpose is to overcome the defect of adaptive Genetic Algorithm whose crossover and mutation probability can not make a corresponding adjustment with evolutionary process.Algorithm’s structure is hierarchical,scheduling problems can be fully demonstrated the characteristics by using this strategy,not only improve the convergence rate but also maintain the diversity of the population,furthermore avoid premature.The population in the same layer evolve with two goals-time optimal and cost optimal at the same time,the basic Genetic Algorithm is applied between layers.The improved algorithm was tested by Muth and Thompson benchmarks,the results show that the optimized algorithm is highly efficient and improves both the quality of solutions and speed of convergence.

关键词： Adaptive Time operator hierarchic structure

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