A multi-element solid-phase extraction method for trace metals determination in environmental samples on Amberlite XAD-2000

A method for the preconcentration of some transition elements at trace level was proposed using a column filled with Amberlite XAD-2000 resin. Metal ions were adsorbed on XAD-2000 as their diethyldithiocarbamate chelates, then analytes retained on the resin were eluted by 1 mol L(-1) nitric acid in acetone and determined by flame atomic absorption spectrometry (FAAS). The influences of some analytical parameters including pH of sample solution, ligand amount, the type, concentration and volume of elution solution, flow rates of the sample and eluent solutions, adsorption capacity of the resin and sample volume on the preconcentration efficiency have been investigated. The influences of some matrix elements were also examined. The detection limit (N=20, 3 sigma) for Mn(II), Fe(II), Co(II), Cu(II), Cd(II), Zn(II), Pb(II) and Ni(II) were found as 0.20, 0.35, 0.25, 0.20, 0.20, 0.15, 0.45 and 0.25 microg L(-1), respectively. The validation of the procedure was carried out by analysis of certified reference materials. The proposed method was applied to natural waters and kale vegetable (Brassica oleracea var. acephala).     

Cluster based wireless sensor network routing using artificial bee colony algorithm

Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. Artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.