Synthesis of a novel and stable g-C3N4–Ag3PO4 hybrid nanocomposite photocatalyst and study of the photocatalytic activity under visible light irradiation

A facile and reproducible template free in situ precipitation method has been developed for the synthesis of Ag3PO4 nanoparticles on the surface of a g-C3N4 photocatalyst at room temperature. The g-C3N4–Ag3PO4 organic–inorganic hybrid nanocomposite photocatalysts were characterized by various techniques. TEM results show the in situ growth of finely distributed Ag3PO4 nanoparticles on the surface of the g-C3N4 sheet. The optimum photocatalytic activity of g-C3N4–Ag3PO4 at 25 wt% of g-C3N4 under visible light is almost 5 and 3.5 times higher than pure g-C3N4 and Ag3PO4 respectively. More attractively, the stability of Ag3PO4 was improved due to the in situ deposition of Ag3PO4 nanoparticles on the surface of the g-C3N4 sheet. The improved performance of the g-C3N4–Ag3PO4 hybrid nanocomposite photocatalysts under visible light irradiation was induced by a synergistic effect, including high charge separation efficiency of the photoinduced electron–hole pair, the smaller particle size, relatively high surface area and the energy band structure. Interestingly, the heterostructured g-C3N4–Ag3PO4 nanocomposite significantly reduces the use of the noble metal silver, thereby effectively reducing the cost of the Ag3PO4 based photocatalyst.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.