A novel two-step electrodeposition method is presented to fabricate a high-performance CoS/graphene hybrid network with a nanosheet structure on Ni foam.
Nickel-cobalt oxides/hydroxides have been considered as promising electrode materials for a high-performance supercapacitor. However, their energy density and cycle stability are still very poor at high current density. Moreover, there are few reports on the fabrication of mixed transition-metal oxides with multishelled hollow structures. Here, we demonstrate a new and flexible strategy for the preparation of hollow Ni-Co-O microspheres with optimized Ni/Co ratios, controlled shell porosity, shell numbers, and shell thickness. Owing to its high effective electrode area and electron transfer number (n(3/2) A), mesoporous shells, and fast electron/ion transfer, the triple-shelled Ni-Co1.5-O electrode exhibits an ultrahigh capacitance (1884 F/g at 3A/g) and rate capability (77.7%, 3-30A/g). Moreover, the assembled sandwiched Ni-Co1.5-O//RGO@Fe3O4 asymmetric supercapacitor (ACS) retains 79.4% of its initial capacitance after 10 000 cycles and shows a high energy density of 41.5 W h kg(-1) at 505 W kg(-1). Importantly, the ACS device delivers a high energy density of 22.8 W h kg(-1) even at 7600 W kg(-1), which is superior to most of the reported asymmetric capacitors. This study has provided a facile and general approach to fabricate Ni/Co mixed transition-metal oxides for energy storage.
In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback. In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in building high-quality JRSs. One main challenge lies on the design of recommendation strategies since different job applicants may have different characteristics. To address the aforementioned challenge, we develop an online JRS, iHR, which groups users into different clusters and employs different recommendation approaches for different user clusters. As a result, iHR has the capability of choosing the appropriate recommendation approaches according to users' characteristics. Empirical results demonstrate the effectiveness of the proposed system.
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