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.
社会群体优化算法(social group optimization,SGO)是一种基于社会群体学习而提出的一种新型优化算法。针对社会群体优化算法易于陷入局部最优问题,提出了一种多群社会群体学习算法(MPSGO)。本算法采用多子群学习方法,对算法两个阶段的个体学习方法进行改进,在维持群体收敛性能的前提下提高群体多样性,同时对部分个体中引入量子学习,使个体学习的有用信息得以增强;此外,每隔一定代数对子群进行随机重组,既能保证各子群个体充分进化,又维持了子群多样性。在设计算法的基础上,分析了其收敛性和多样性;通过与其他四种算法进行对比实验,验证了改进后算法性能更优。 相似文献