Amorphous-crystalline cobalt-molybdenum bimetallic phosphide heterostructured nanosheets as Janus electrocatalyst for efficient water splitting

Efficient and sustainable Janus catalysts toward hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are highly desirable for future hydrogen production via water electrolysis. Herein we report an active Janus electrocatalyst of amorphous-crystalline cobalt-molybdenum bimetallic phosphide heterostructured nanosheets on nickel foam (CoMoP/CoP/NF) for efficient electrolysis of alkaline water. As-reported CoMoP/CoP/NF consists of amorphous bimetal phosphide nanosheets doped with crystalline CoMoP/CoP heterostructured nanoparticles on NF. It can efficiently catalyze both HER (η = 127 mV@100 mA cm^?2) and OER (η = 308 mV@100 mA cm^?2) in alkaline electrolyte with long-term durability. Serving as anode and cathode of water electrolyzer, CoMoP/CoP/NF generates electrolytic current of 10, 50 and 100 mA cm^?2 at low voltage of 1.50, 1.59, and 1.67 V, respectively.     

Nickel‒cobalt bimetallic catalysts prepared from hydrotalcite-like compounds for dry reforming of methane

Ni–Co/Mg(Al)O alloy catalysts with different Co/Ni molar ratios have been prepared from Ni- and Co-substituted Mg–Al hydrotalcite-like compounds (HTlcs) as precursors and tested for dry reforming of methane. The XRD characterization shows that Ni–Co–Mg–Al HTlcs are decomposed by calcination into Mg(Ni,Co,Al)O solid solution, and by reduction finely dispersed alloy particles are formed. H₂-TPR indicates a strong interaction between nickel/cobalt oxides and magnesia, and the presence of cobalt in Mg(Ni,Co,Al)O enhances the metal-support interaction. STEM-EDX analysis reveals that nickel and cobalt cations are homogeneously distributed in the HTlcs precursor and in the derived solid solution, and by reduction the resulting Ni–Co alloy particles are composition-uniform. The Ni–Co/Mg(Al)O alloy catalysts exhibit relatively high activity and stability at severe conditions, i.e., a medium temperature of 600 °C and a high space velocity of 120000 mL g^?1 h^?1. In comparison to monometallic Ni catalyst, Ni–Co alloying effectively inhibits methane decomposition and coke deposition, leading to a marked enhancement of catalytic stability. From CO₂-TPD and TPSR, it is suggested that alloying Ni with Co favors the CO₂ adsorption/activation and promotes the elimination of carbon species, thus improving the coke resistance. Furthermore, a high and stable activity with low coking is demonstrated at 750 °C. The hydrotalcite-derived Ni–Co/Mg(Al)O catalysts show better catalytic performance than many of the reported Ni–Co catalysts, which can be attributed to the formation of Ni–Co alloy with uniform composition, proper size, and strong metal-support interaction as well as the presence of basic Mg(Al)O as support.     

CNT-interconnected iron-doped NiP2/Ni2P heterostructural nanoflowers as high-efficiency electrocatalyst for oxygen evolution reaction

Oxygen evolution reaction (OER) plays a decisive role in electrolytic water splitting. However, it is still challengeable to develop low-cost and efficient OER electrocatalysts. Herein, we present a combination strategy via heteroatom doping, hetero-interface engineering and introducing conductive skeleton to synthesize a hybrid OER catalyst of CNT-interconnected iron-doped NiP₂/Ni₂P (Fe-(NiP₂/Ni₂P)@CNT) heterostructural nanoflowers by a simple hydrothermal reaction and subsequent phosphorization process. The optimized Fe-(NiP₂/Ni₂P)@CNT catalyst delivers an ultralow Tafel slope of 46.1 mV dec^?1 and overpotential of 254 mV to obtain 10 mA cm^?2, which are even better than those of commercial OER catalyst RuO₂. The excellent OER performance is mainly attributed to its unique nanoarchitecture and the synergistic effects: the nanoflowers constructed by a 2D-like nanosheets guarantee large specific area and abundant active sites; the highly conductive CNT skeleton and the electronic modulation by the heterostructural NiP₂/Ni₂P interface and the hetero-atom doping can improve the catalytic activity; porous nanostructure benefits electrolyte penetration and gas release; most importantly, the rough surface and rich defects caused by phosphorization process can further enhance the OER performance. This work provides a deep insight to boost catalytic performance by heteroatom doping and interface engineering for water splitting.     

Graphitic-C3N4/ZnCr-layered double hydroxide 2D/2D nanosheet heterojunction: Mesoporous photocatalyst for advanced oxidation of azo dyes with in situ produced H2O2

In this study the constructional modification of Graphitic carbon nitride nanosheet (GCN-ns) has been made with the aid of ZnCr layered double hydroxide (ZC-LDH) in a unique 2D-2D structure to enhance its visible light absorption. Optical and morphological study presents successful incorporation of ZC-LDH on the surface of GCN-ns. Through adjusting of GCN-ns by ZC-LDH lower recombination rate of e^?/h⁺ pairs, longer lifetimes and an increase in contamination reduction was brought out. The binary nanocomposite was employed to effectively degrade Rhodamine B under UV/vis light irradiation. The improvement in photocatalytic abilities was proven to be related to in situ self-production of H₂O₂ on GCN-ns/ZC-LDH surface by Xe light irradiation which in return accounts for additional hydroxide radical generation. Radical quenching experiments specified the main active species involved while the consequent step-scheme (S-scheme) charge transfer mechanism was proposed.     

SDN中基于KMOBPSO的高可靠性控制器部署算法

针对SDN中控制器系统的单节点故障问题,兼顾系统成本和系统时延,应用N+1冗余备份模型来提高SDN控制器部署的可靠性,并将其抽象为多目标优化问题.同时,提出了一种融合K-means聚类算法和遗传算子的多目标二进制粒子群算法——KMOBPSO算法,以求解SDN控制器高可靠性部署问题的解.仿真结果表明,所提算法具有求解精度高、分布均匀、沿Pareto前沿面覆盖广的特点,能够显著提高SDN中控制器部署的可靠性.     

The significant promotion of g-C3N4 on Pt/CNS catalyst for the electrocatalytic oxidation of methanol

This article reported a series of g–C₃N₄–CNS (g-C₃N₄ and carbon nanosheets) composite carriers formed by the hydrothermal method, and then the ethylene glycol reduction method was used to anchor Pt nanoparticles on the g–C₃N₄–CNS carrier to form the Pt/g–C₃N₄–CNS catalysts. The electrochemical test for the electrocatalytic oxidation of methanol (MOR) shown that the Pt/20%g–C₃N₄–CNS catalyst has the best catalytic performance and stability. These Pt/g–C₃N₄–CNS catalysts were analyzed by TEM, XRD, XPS, and BET characterization. It is discovered that the amount of g-C₃N₄ greatly influenced the structure and chemical properties of Pt/CNS precursor. As the content of g-C₃N₄ increases, the content of pyridine nitrogen and pyrrole nitrogen also increases, and N species can enhance the interaction between Pt nanoparticles and CNS, promote Pt dispersion, and increase the specific surface area of the catalyst. Similarly, an excessive addition of g-C₃N₄ will cause a sharp decline in the conductivity of the catalyst, and then led to the decline of MOR activity.     

Highly dispersed cobalt nanoparticles onto nitrogen-doped carbon nanosheets for efficient hydrogen generation via catalytic hydrolysis of sodium borohydride

Porous carbon nanostructures are promising supports for stabilizing the highly dispersed metal nanoparticles and facilitating the mass transfer during the reaction, which are critical to achieve the high efficiency of hydrogen generation from sodium borohydride dehydrogenation. Herein, the catalytically active porous architectures are simply prepared by using 2-methylimidazole and melamine as reactive sources. The structural and compositional characterizations reveal the coexistence of metallic cobalt and N-doped carbon in porous architectures. Electron microscopy observations indicate that the synthesized products are smartly constructed from the carbon nanosheets with densely dispersed Co nanoparticles. Due to the notable structural features, the prepared Co@NC-600 sample presents the highly efficient activity for catalytic hydrolysis of NaBH₄ with a hydrogen generation rate of 2574 mL min⁻¹ g_cat⁻¹ and an activation energy of 47.6 kJ mol⁻¹. The catalytically active metallic Co and suitable support-effect of N-doped carbon are responsible for catalytic dehydrogenation.     

一种二元域上的快速计算哈希函数

论文从映射的角度,将哈希函数分为三类:严格哈希函数、计算哈希函数和实用哈希函数。在此基础上,根据二元域上二次方程x2 x c=0的部分不可解的性质,构造了一种计算哈希函数,它满足单向性和一一对应性,同时具有运算速度快的优点。     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.     

Configurational and nonconfigurational interactions between odorants in binary mixtures.

Studies on odor mixture perception suggest that although odor components can often be identified in mixtures, mixtures can also give rise to novel perceptual qualities that are not present in the components. Using an olfactory habituation task, the authors evaluated how the perceptual similarity between components in a mixture affects the perceptual quality of the mixture itself. Rats perceived binary mixtures composed of similar components as different from their 2 components, whereas binary mixtures composed of dissimilar components were perceived as very similar to their components. Results show that for both types of mixtures, pretraining to Component A reduces subsequent learning about Component B in rats trained in the presence of A. (PsycINFO Database Record (c) 2010 APA, all rights reserved)