Paper-based diffusive gradients in thin films technique coupled to energy dispersive X-ray fluorescence spectrometry for the determination of labile Mn, Co, Ni, Cu, Zn and Pb in river water

The diffusive gradients in thin films (DGT) technique has shown enormous potential for labile metal monitoring in fresh water due to the preconcentration, time-integrated, matrix interference removal and speciation analytical features. In this work, the coupling of energy dispersive X-ray fluorescence (EDXRF) with paper-based DGT devices was evaluated for the direct determination of Mn, Co. Ni, Cu, Zn and Pb in fresh water. The DGT samplers were assembled with cellulose (Whatman 3 MM chromatography paper) as the diffusion layer and a cellulose phosphate ion exchange membrane (Whatman P 81 paper) as the binding agent. The diffusion coefficients of the analytes on 3 MM chromatography paper were calculated by deploying the DGT samplers in synthetic solutions containing 500 mu g L-1 of Mn. Co, Ni, Cu, Zn and Pb (4 L at pH 5.5 and ionic strength at 0.05 mol L-1). After retrieval, the DGT units were disassembled and the P81 papers were dried and analysed by EDXRF directly. The 3 MM chromatographic paper diffusion coefficients of the analytes ranged from 1.67 to 1.87 x 10(-6) cm(2) s(-1). The metal retention and phosphate group homogeneities on the P81 membrane was studied by a spot analysis with a diameter of 1 mm. The proposed approach (DGT-EDXRF coupling) was applied to determine the analytes at five sampling sites (48 h in situ deployment) on the Piracicaba river basin, and the results (labile fraction) were compared with 0.45 mu m dissolved fractions determined by synchrotron radiation-excited total reflection X-ray fluorescence (SR-TXRF). The limits of detection of DGT-EDXRF coupling for the analytes (from 7.5 to 26 mu g L-1) were similar to those obtained by the sensitive SR-TXRF technique (3.8 to 9.1 mu g L-1). (C) 2012 Elsevier B.V. All rights reserved.

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.