Nanometer-size Co-ZIF (zeolitic imidazolate frameworks) catalyst was prepared for selective oxidation of toluene to benzaldehyde under mild conditions. The typical characteristics of the metal-organic frameworks (MOFs) material were affirmed by the XRD, SEM, and TEM, the BET surface area of this catalyst was as high as 924.25 m2/g, and the diameter of particles was near 200 nm from TEM results. The Co metal was coated with 2-methyl glyoxaline, and the crystalline planes were relatively stable. The reaction temperatures, oxygen pressure, mass amount of N-hydroxyphthalimide (NHPI), and reaction time were discussed. The Co-ZIF catalyst gave the best result of 92.30% toluene conversion and 91.31% selectivity to benzaldehyde under 0.12 MPa and 313 K. The addition of a certain amount of NHPI and the smooth oxidate capacity of the catalyst were important factors in the high yield of benzaldehyde. This nanometer-size catalyst showed superior performance for recycling use in the oxidation of toluene. Finally, a possible reaction mechanism was proposed. This new nanometer-size Co-ZIF catalyst will be applied well in the selective oxidation of toluene to benzaldehyde.
Mining influence in evolving entities is an important but challenging task, partly due to complex nature of it. In this paper, we mainly focus on the following problems on it with respect to stock market: (1) How to identify pairs of stocks that influence one another; (2) How to quantify the influence and capture group effects and dynamic nature of influence of each stock; (3) How to adopt approximate approaches so that we can improve the efficiency of the proposed model. To tackle these problems, a novel graph-based mining method, which utilizes time series and volume information collaboratively is proposed, and several optimized algorithms are presented. Besides, two extended metrics to capture the dynamic and group nature of influence based on the model are derived. Furthermore, we also suggest a potential application of the model to stock price prediction. The experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our approach. Some insights on this paper can be the ideas of analyzing the influence of evolving entities using the social network analysis methods.
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