The nitrogen-doped porous carbon applied for oxygen reduction reaction (ORR) has aroused extensive interests due to its unique physical and chemical properties. However, the complicated nitrogen-doping strategy and high cost limit its extensive application. In this work, a series of nitrogen-doped porous carbons were prepared by a facile pyrolysis process coupling with subsequent KOH activation using renewable N-enriched biomass potato as carbon source. Effects of activation temperature and KOH amounts on the textural properties and electrocatalytic ORR activities of the final samples were investigated in detail. The KOH activation treatment results in a high specific surface area (SSA) and hierarchical porous structure, which is beneficial for improved ORR performance. The optimized NPC-750 possesses a high SSA of 1134.2 m 2 •g-1 , developed hierarchical pores as well as moderate nitrogen content (1.57at%). It also exhibits a positive onset potential of 0.89 V (vs. RHE) and half-wave potential of 0.79 V (vs. RHE). Simultaneously, the advanced long-time stability and methanol-tolerance capacity were also obtained, implying that these biomass-derived porous carbons are potential low-cost ORR electrocatalysts. Moreover, these porous carbons show great potential in various fields including supercapacitors, adsorption/separation, catalysis and batteries as well.
Network representation learning aims to map nodes in the network into low-dimensional dense vectors, which can be widely used to solve the network analysis tasks. Existing methods mainly focus on single-layer homogeneous networks. However, many real-world networks consist of multiple types of nodes and edges, which are called multilayer networks. The problem of how to capture node information and use multi-type relational information is a major challenge of multilayer network representation learning. To address this problem, we propose a method of random walk of multiple information, called IFMNE, to efficiently preserve and learn node information and multi-type relational information into a unified space. This method combines node structure information with network topology information to obtain the node random walk sequence, and trains the node walk sequence on the neural network model. Experimental results are performed on five real multilayer networks, and the embedding vectors were evaluated by link prediction task. The accuracy was significantly improved on the basis of low time complexity compared with the baseline methods.
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