High performance gold nanorods and silver nanocubes in surface-enhanced Raman spectroscopy of pesticides

The behavior of Au nanorods and Ag nanocubes as analytical sensors was evaluated for three different classes of herbicides. The use of such anisotropic nanoparticles in surface-enhanced Raman scattering (SERS) experiments allows the one to obtain the spectrum of crystal violet dye in the single molecule regime, as well as the pesticides dichlorophenoxyacetic acid (2,4-D), trichlorfon and ametryn. Such metallic substrates show high SERS performance at low analyte concentrations making them adequate for use as analytical sensors. Density functional theory (DFT) calculations of the geometries and vibrational wavenumbers of the adsorbates in the presence of silver or gold atoms were used to elucidate the nature of adsorbate-nanostructure bonding in each case and support the enhancement patterns observed in each SERS spectrum.

Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy

A simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.

Green Chemistry-Sensitive Analytical Procedure for Photometric Determination of Orthophosphate in River and Tap Water by Use of a Simple LED-Based Photometer

In the current work a Green Analytical Chemistry (GAC) procedure for photometric determination of orthophosphate in river water at mu g L-1 concentration level is described. The flow system module and the LED-based photometer were assembled together to constitute a compact unit in order to allow that a flow cell with optical path-length of 100mm was coupled to them. The photometric procedure based on the molybdenum blue method was implemented employing the multicommuted flow injection analysis approach, which provided facilities to allow reduction of reagent consumption and as well as waste generation. Aiming to prove the usefulness of the system, orthophosphate in river and tap waters was determined. Accuracy was ascertained by spiking samples with orthophosphate solution yielding recoveries ranging from 96% up to 107%. Other profitable features such as a wide linear response range between 10 to 800 mu g L-1 [image omitted]; a detection limit (3 sigma criterion) of 2.4 mu g L-1 [image omitted]; a relative standard deviation (n=7) of 2% using a typical water sample with concentration of 120 mu g L-1 [image omitted]; reagent consumption of 3.0mg ammonium molybdate, 0.3mg hydrazine sulfate, and 0.03mg stannous chloride per determination; a waste generation of 2.4mL per determination; and a sampling throughput of 20 determination per hours were also achieved.

A flow-based analytical procedure for salbutamol determination exploiting chemiluminescence in a liquid-core waveguide

Salbutamol is a bronchodilator whose use is restricted due to its anabolic effects. A flow-based procedure for salbutamol determination based on the inhibition of chemiluminescence of the luminol/hypochlorite system was developed. A flow cell constructed with a liquid-core waveguide was employed to constrain the emitted radiation, minimizing losses during transport to detector. Linear response was observed within 2.5 x 10(-6) and 1.0 x 10(-5) mol L-1 with a detection limit estimated as 1 x 10(-7) mol L-1 at the 99.7% confidence level. The coefficient of variation (n = 20), sampling rate, and luminol consumption per determination were estimated as 2.8%, 164 determinations h(-1), and 50 mu g, respectively. Results for pharmaceutical samples were in agreement with those obtained by reference procedures at the 95% confidence level.

Sequential injections as an alternative to gradient exploitation for implementing differential kinetic analysis in a flow injection system

A novel flow-based strategy for implementing simultaneous determinations of different chemical species reacting with the same reagent(s) at different rates is proposed and applied to the spectrophotometric catalytic determination of iron and vanadium in Fe-V alloys. The method relies on the influence of Fe(II) and V(IV) on the rate of the iodide oxidation by Cr(VI) under acidic conditions, the Jones reducing agent is then needed Three different plugs of the sample are sequentially inserted into an acidic KI reagent carrier stream, and a confluent Cr(VI) solution is added downstream Overlap between the inserted plugs leads to a complex sample zone with several regions of maximal and minimal absorbance values. Measurements performed on these regions reveal the different degrees of reaction development and tend to be more precise Data are treated by multivariate calibration involving the PLS algorithm The proposed system is very simple and rugged Two latent variables carried out ca 95% of the analytical information and the results are in agreement with ICP-OES. (C) 2010 Elsevier B V. All rights reserved.

Analytical calculation of the transition to complete phase synchronization in coupled oscillators

Here we present a system of coupled phase oscillators with nearest neighbors coupling, which we study for different boundary conditions. We concentrate at the transition to the total synchronization. We are able to develop exact solutions for the value of the coupling parameter when the system becomes completely synchronized, for the case of periodic boundary conditions as well as for a chain with fixed ends. We compare the results with those calculated numerically.

DFT and electrochemical studies on nortriptyline oxidation sites

A study on the possible sites of oxidation and epoxidation of nortriptyline was performed using electrochemical and quantum chemical methods; these sites are involved in the biological responses (for example, hepatotoxicity) of nortriptyline and other similar antidepressants. Quantum chemical studies and electrochemical experiments demonstrated that the oxidation and epoxidation sites are located on the apolar region of nortriptyline, which will useful for understanding the molecule`s activity. Also, for the determination of the compound in biological fluids or in pharmaceutical formulations, we propose a useful analytical methodology using a graphite-polyurethane composite electrode, which exhibited the best performance when compared with boron-doped diamond or glassy carbon surfaces.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

An algorithm for computerized automatic tuning of power system stabilizers

The main objective of this paper is to relieve the power system engineers from the burden of the complex and time-consuming process of power system stabilizer (PSS) tuning. To achieve this goal, the paper proposes an automatic process for computerized tuning of PSSs, which is based on an iterative process that uses a linear matrix inequality (LMI) solver to find the PSS parameters. It is shown in the paper that PSS tuning can be written as a search problem over a non-convex feasible set. The proposed algorithm solves this feasibility problem using an iterative LMI approach and a suitable initial condition, corresponding to a PSS designed for nominal operating conditions only (which is a quite simple task, since the required phase compensation is uniquely defined). Some knowledge about the PSS tuning is also incorporated in the algorithm through the specification of bounds defining the allowable PSS parameters. The application of the proposed algorithm to a benchmark test system and the nonlinear simulation of the resulting closed-loop models demonstrate the efficiency of this algorithm. (C) 2009 Elsevier Ltd. All rights reserved.

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coil is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO. (C) 2009 Elsevier Ltd. All rights reserved.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification

This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder-Mead algorithm (Simplex method) is presented; consequently, the convergence of the PSOS becomes independent of the heuristic constants and its stability and confidence are enhanced. The formulated hybrid method performs better in different benchmark functions than the Simulated Annealing (SA) and the basic PSO (PSO(b)). Two damage identification problems, taking into consideration the effects of noisy and incomplete data, were studied: first, a 10-bar truss and second, a cracked free-free beam, both modeled with finite elements. In these cases, the damage location and extent were successfully determined. Finally, a non-linear oscillator (Duffing oscillator) was identified by PSOS providing good results. (C) 2009 Elsevier Ltd. All rights reserved

Analytical formulae for electromechanical effective properties of 3-1 longitudinally porous piezoelectric materials

A unidirectional fiber composite is considered here, the fibers of which are empty cylindrical holes periodically distributed in a transversely isotropic piezoelectric matrix, The empty-fiber cross-section is circular and the periodicity is the same in two directions at an angle pi/2 or pi/3. Closed-form formulae for all electromechanical effective properties of these 3-1 longitudinally periodic porous piezoelectric materials are presented. The derivation of such expressions is based on the asymptotic homogenization method as a limit of the effective properties of two-phase transversely isotropic parallel fiber-reinforced composites when the fibers properties tend to zero. The plane effective coefficients satisfy the corresponding Schulgasser-Benveniste-Dvorak universal type of relations, A new relation among the antiplane effective constants from the solutions of two antiplane strains and potential local problems is found. This relation is valid for arbitrary shapes of the empty-fiber cross-sections. Based on such a relation, and using recent numerical results for isotropic conductive composites, the antiplane effective properties are computed for different geometrical shapes of the empty-fiber cross-section. Comparisons with other analytical and numerical theories are presented. (c) 2008 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Analytical solutions for geometrically nonlinear trusses

This paper presents an analytical method for analyzing trusses with severe geometrically nonlinear behavior. The main objective is to find analytical solutions for trusses with different axial forces in the bars. The methodology is based on truss kinematics, elastic constitutive laws and equilibrium of nodal forces. The proposed formulation can be applied to hyper elastic materials, such as rubber and elastic foams. A Von Mises truss with two bars made by different materials is analyzed to show the accuracy of this methodology.

Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.