Regularly dispersed Pt particles on SBA-15 supported catalysts were synthesized with a Pt loading of 5 wt% by a sol-immobilisation method, wherein various Pt particle sizes within 1–5 nm were finely controlled via the adjustment of the addition amount of polyvinyl alcohol (PVA). A high PVA/Pt ratio of the initial solution tended to generate small Pt particles on the SBA-15 support due to intense protection against Pt particle aggregation. In addition, the effect of Pt particle size on naphthalene hydrogenation was investigated in terms of catalytic performance. Compared with the performance of other catalysts with Pt particle sizes greater or less than 3.5 nm, Pt nanoparticles with sizes centered at 3.5 nm exhibited excellent catalytic performance towards decalin. This excellent catalytic performance was mainly attributed to a suitable ratio of the edge sites to flat sites on these Pt nanoparticles, benefitting the rapid adsorption of naphthalene and dissociation of hydrogen.
Graphical Abstract
The Pt/SBA-15 catalysts were prepared by sol-immobilisation method. The highest performance was attributed to the Pt-nanoparticles with suitable flat/edge sites ratio.
Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification. 相似文献
This paper develops a new dynamic model of Cournot–Nash oligopolistic competition that includes production and transportation costs, product differentiation, and quality levels in a network framework. The production costs capture the total quality cost, which, in turn, can also represent the R&D cost. We first present the equilibrium version and derive alternative variational inequality formulations. We then construct the projected dynamical systems model, which provides a continuous-time evolution of the firms’ product shipments and product quality levels, and whose set of stationary points coincides with the set of solutions to the variational inequality problem. We establish stability analysis results using a monotonicity approach and construct a discrete-time version of the continuous-time adjustment process, which yields an algorithm, with closed form expressions at each iteration. The algorithm is then utilized to compute solutions to several numerical examples. The framework can serve as the foundation for the modeling and analysis of competition among firms in industries ranging from food to pharmaceuticals to durable goods and high tech products, as well as Internet services, where quality and product differentiation are seminal. 相似文献