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FeNi-O porous nanosheets were synthesized and they exhibited advanced OER performance that was investigated experimentally and theoretically.

Reducing overpotential and increasing current density remain a great challenge for promoting the application of transition metalsbased layered double hydroxides (LDHs). Herein, Ni nanoparticles (∼10 nm) decorated NiFe LDH ultrathin (thickness: ∼2 nm) nanosheets (Ni NP/NiFe LDH) were successfully synthesized which exhibited advanced electrocatalytic activity and long-term stability on both oxygen evolution reaction (OER) and urea oxidation reaction (UOR). The onset potentials for OER and UOR are 1.50 V and 1.34 V, respectively, both are lower than NiFe LDH under the same conditions. Moreover, the peak current density of UOR was 300 mA/cm 2 , which is much larger than reported precious-metal free UOR catalysts. The excellent catalytic performance of Ni NP/NiFe LDH composite for OER and UOR is proposed to result from the abundant exposed active sites, small charge transfer resistance, and the synergistic effects between NiFe LDH and anchored Ni nanoparticles. This work provides inspiring ideas and helpful guidelines in design of low-cost but highly efficient electrochemical catalysts. The global energy crisis and environmental contamination have prompted intense research into the development of sustainable energy conversion and storage systems.1-10 Water splitting and direct urea fuel cell (DUFC) as promising approaches to produce clean energy recyclable, have been widely investigated recently. Nevertheless, oxygen evolution reaction (OER: 2H 2 O === O 2 + 4H + + 4e − ) suffers from a sluggish kinetics owing to a 4e-transfer process, which is frequently an obstacle to the whole water splitting. Same as OER, urea oxidation reaction (UOR: CO(NH 2 ) 2 + H 2 O === N 2 + 3H 2 + CO 2 ) is also under a multistep proton-coupled electron transfer process, which remains to be a great challenge for DUFC. As a consequence, the sluggish kinetics of OER and UOR lead to considerable overpotential and decrease the efficiency of energy conversion and storage. To achieve efficient energy conversion, much effort has been devoted to the research for robust yet stable electrochemical catalysts with reduced overpotentials. Precious metal based catalysts have shown significant electrocatalytic properties, 11-13 but their high cost and scarcity hamper the commercialization of the technique. Therefore, developing functional nanomaterials composed of cost-effective and abundant elements are on great demand.Owing to the unique physicochemical properties (e.g. tunable metal species/ratios, and exchangeable interlayer spacings, etc.), layered double hydroxides (LDHs) [23][24][25][26][27] and among which, NiFe LDH showed the best performance.28 Nevertheless, reducing the overpotential and increasing the current density remain under intensive pursuit for promoting the application of NiFe LDH. For example, the introduction of conductive materials (graphene, carbon nanotube, etc.) into LDH systems can improve the conductivity of the catalysts 21,22 and hence further enhance their catalytic performance. Herein, we report a bifunctional catalyst ...

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In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers.

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