The low cost and highly efficient construction of electrocatalysts has attracted significant attention owing to the use of clean and sustainable energy technologies.In this work,cobalt nanoparticle decorated N-doped c...The low cost and highly efficient construction of electrocatalysts has attracted significant attention owing to the use of clean and sustainable energy technologies.In this work,cobalt nanoparticle decorated N-doped carbons(Co@NC)are synthesized by the pyrolysis of a cobalt covalent organic framework under an inert atmosphere.The Co@NC demonstrates improved electrocatalytic capabilities compared to N-doped carbon without the addition of Co nanoparticles,indicating the important role of cobalt.The well-dispersed active sites(Co-Nx)and the synergistic effect between the carbon matrix and Co nanoparticles greatly enhance the electrocatalytic activity for the oxygen reduction reaction.In addition,the Co content has a significant effect on the catalytic activity.The resulting Co@NC-0.86 exhibits a superb electrocatalytic activity for the oxygen reduction reaction in an alkaline electrolyte in terms of the onset potential(0.90 V),half-wave potential(0.80 V)and the limiting current density(4.84 mA·cm^(−2)),and a high selectivity,as well as a strong methanol tolerance and superior durability,these results are comparable to those of the Pt/C catalyst.Furthermore,the superior bifunctional activity of Co@NC-0.86 was also confirmed in a home-built Zn-air battery,signifying the possibility for application in electrode materials and in current energy conversion and storage devices.展开更多
With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other...With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other external descriptive attributes(i.e.,travelling mode,activities,etc.).Trajectory recommendation is especially important to users for finding the routes meeting the user’s travel needs quickly.Most existing trajectory recommendation works return the same route to different users given an origin and a destination.However,the users’behavior preferences can be learned from users’historical multi-attributes trajectories.In this paper,we propose two novel personalized trajectory recommendation methods,i.e.,user behavior probability learning based on matrix decomposition and user behavior probability learning based on Kernel density estimation.We transform the route recommendation problem to a shortest path problem employing Bayesian probability model.Combining the user input(i.e.,an origin and a destination),the trajectory query is performed on a behavior graph based on the learned behavior probability automatically.Finally,a series of experiments on two real datasets validate the effectiveness of our proposed methods.展开更多
基金supported by the Natural Science Foundation of Shandong Province(Grant No.ZR2019PB013)the Training Program of Innovation and Entrepreneurship for Undergraduates(Grant No.CXCY2021161)+1 种基金the Natural Science Foundation of Tianjin(Grant No.19JCZDJC37700)the National Natural Science Foundation of China(Grant No.21875118).
文摘The low cost and highly efficient construction of electrocatalysts has attracted significant attention owing to the use of clean and sustainable energy technologies.In this work,cobalt nanoparticle decorated N-doped carbons(Co@NC)are synthesized by the pyrolysis of a cobalt covalent organic framework under an inert atmosphere.The Co@NC demonstrates improved electrocatalytic capabilities compared to N-doped carbon without the addition of Co nanoparticles,indicating the important role of cobalt.The well-dispersed active sites(Co-Nx)and the synergistic effect between the carbon matrix and Co nanoparticles greatly enhance the electrocatalytic activity for the oxygen reduction reaction.In addition,the Co content has a significant effect on the catalytic activity.The resulting Co@NC-0.86 exhibits a superb electrocatalytic activity for the oxygen reduction reaction in an alkaline electrolyte in terms of the onset potential(0.90 V),half-wave potential(0.80 V)and the limiting current density(4.84 mA·cm^(−2)),and a high selectivity,as well as a strong methanol tolerance and superior durability,these results are comparable to those of the Pt/C catalyst.Furthermore,the superior bifunctional activity of Co@NC-0.86 was also confirmed in a home-built Zn-air battery,signifying the possibility for application in electrode materials and in current energy conversion and storage devices.
基金This work was partially supported by the grant from the Natural Science Foundation of Hebei Province(F2021210005)the Hebei Province Innovation Capability Improvement Plan(21550803D)+2 种基金the Outstanding Youth Foundation of Hebei Education Department(BJ2021085)the Fourth Outstanding Youth Foundation of Shijiazhuang Tiedao University,and Training Project for Improving Students of Scientific and Technological Innovation Ability for College and Middle School(DXS202106)Scientific Research Project from China Railway Corporation(2020F026).
文摘With increasing popularity of mobile devices and flourish of social networks,a large number of trajectory data is accumulated.Trajectory data contains a wealth of information,including spatiality,time series,and other external descriptive attributes(i.e.,travelling mode,activities,etc.).Trajectory recommendation is especially important to users for finding the routes meeting the user’s travel needs quickly.Most existing trajectory recommendation works return the same route to different users given an origin and a destination.However,the users’behavior preferences can be learned from users’historical multi-attributes trajectories.In this paper,we propose two novel personalized trajectory recommendation methods,i.e.,user behavior probability learning based on matrix decomposition and user behavior probability learning based on Kernel density estimation.We transform the route recommendation problem to a shortest path problem employing Bayesian probability model.Combining the user input(i.e.,an origin and a destination),the trajectory query is performed on a behavior graph based on the learned behavior probability automatically.Finally,a series of experiments on two real datasets validate the effectiveness of our proposed methods.
