Porous Ni2P nanoflower supported on nickel foam (Ni2P@Ni foam) electrodes are synthesized via a simple hydrothermal growth strategy accompanied with further phosphating treatment. The prepared electrodes are characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and transmission electron microscopy (TEM). Electro-catalytic performances towards urea electro-oxidation are tested by cyclic voltammetry (CV), chronoamperometry (CA) coupled with electrochemical impedance spectroscopy (EIS). By phosphating Ni(OH)2 precursor, the final obtained Ni2P@Ni foam electrode presents a porous Ni2P nanoflower structure within abundant porosity, and so exposes a large amount of electro-catalytic active sites and electronic transmission channels to accelerate the interfacial reaction. Compared with Ni(OH)2@Ni foam precursor, the Ni2P@Ni foam catalyst exhibits more excellent electro-catalytic activity as well as lower onset oxidation potential. Remarkably, the Ni2P@Ni foam catalyst reaches a peak current density of 750 mA cm?2 with an onset oxidation potential of 0.24 V (vs. Ag/AgCl) accompanied by an excellent stability in 0.60 M urea with 5.00 M KOH solutions. Benefiting from the unique porous nanosheet structure, the as-synthesized Ni2P@Ni foam catalyst performs a highly enhanced catalytic behavior for alkaline urea electro-oxidation, indicating that the material can be hopefully applied in direct urea fuel cells. 相似文献
Complex diagrammatic guide signs (DGSs) are widely used. To study the influence of DGSs with different complexities on drivers’ cognition, four types 相似文献
By virtue of their narrow emission bands, near-unity quantum yield, and low fabrication cost, metal halide perovskites hold great promise in numerous aspects of optoelectronic applications, including solid-state lighting, lasing, and displays. Despite such promise, the poor temperature tolerance and suboptimal quantum yield of the existing metal halide perovskites in their solid state have severely limited their practical applications. Here, a straightforward heterogeneous interfacial method to develop superior thermotolerant and highly emissive solid-state metal halide perovskites is reported and their use as long-lasting high-temperature and high-input-power durable solid-state light-emitting diodes is illustrated. It is found that the resultant materials can well maintain their superior quantum efficiency after heating at a temperature over 150 °C for up to 22 h. A white light-emitting diode (w-LED) constructed from the metal halide perovskite solid exhibits superior temperature sustainable lifetime over 1100 h. The w-LED also displays a highly durable high-power-driving capability, and its working current can go up to 300 mA. It is believed that such highly thermotolerant metal halide perovskites will unleash the possibility of a wide variety of high-power and high-temperature solid-state lighting, lasing, and display devices that have been limited by existing methods. 相似文献
Traditional multi-objective evolutionary algorithms treat each objective equally and search randomly in all solution spaces without using preference information. This might reduce the search efficiency and quality of solutions preferred by decision makers, especially when solving problems with complicated properties or many objectives. Three reference point based algorithms which adopt preference information in optimization progress, e.g., R-NSGA-II, r-NSGA-II and g-NSGA-II, have been shown to be effective in finding more preferred solutions in theoretical test problems. However, more efforts are needed to test their effectiveness in real-world problems. This study conducts a comparison of the above three algorithms with a standard algorithm NSGA-II on a reservoir operation problem to demonstrate their performance in improving the search efficiency and quality of preferred solutions. Under the same calculation times of the objective functions, Pareto optimal solutions of the four algorithms are used in the empirical comparison in terms of the approximation to the preferred solutions. Three performance indicators are then adopted for further comparison. Results show that R-NSGA-II and r-NSGA-II can improve the search efficiency and quality of preferred solutions. The convergence and diversity of their solutions in the concerned region are better than NSGA-II, and the closeness degree to the reference point can be increased by 42.8%, and moreover the number of preferred solutions can be increased by more than 3 times when part of objectives are preferred. By contrast, g-NSGA-II shows worse performance. This study exhibits the performance of three reference point based algorithms and provides insights in algorithm selection for multi-objective reservoir optimization problems.
Although genome mining has advanced the identification, discovery, and study of microbial natural products, the discovery of bacterial diterpenoids continues to lag behind. Herein, we report the identification of 66 putative producers of novel bacterial diterpenoids, and the discovery of the tiancilactone (TNL) family of antibiotics, by genome mining of type II diterpene synthases that do not possess the canonical DXDD motif. The TNLs, which are broad‐spectrum antibiotics with moderate activities, are produced by both Streptomyces sp. CB03234 and Streptomyces sp. CB03238 and feature a highly functionalized diterpenoid skeleton that is further decorated with chloroanthranilate and γ‐butyrolactone moieties. Genetic manipulation of the tnl gene cluster resulted in TNL congeners, which provided insights into their biosynthesis and structure–activity relationships. This work highlights the biosynthetic potential that bacteria possess to produce diterpenoids and should inspire continued efforts to discover terpenoid natural products from bacteria. 相似文献