Optimization of the Microwave-Assisted Saponification and Extraction of Organic Pollutants from Marine Biota Using Experimental Design and Artificial Neural Networks

Several improvements in sample pretreatment for the determination of organic pollutants (i.e. n-alkanes, polycyclic aromatic hydrocarbons, PAHs) in marine biota (mussels) are presented. The use of liquid nitrogen and homogenization of the samples are shown to be an alternative to the time consuming liophilization step required for the analysis of biota samples. Microwave-assisted hydrolysis and extraction are combined to isolate organic pollutants (19 n-alkanes and 27 PAHs) from biota matrices. Experimental design (ED) and artificial neural networks (ANNs) were used to optimize the experimental conditions. NIST-CRM 2978 was used to test the validity of the developed method which shows a good agreement with certified values.     

高氯酸二水邻菲咯啉合铜配合物的合成和晶体结构

Crystal structure of the title compound， Cu(phen)(H₂O)₂·ClO₄(phen=1，10-phenanthroline)， was deter-mined by X-ray crystallography. It crystallizes in the monoclinic system， space group C2/c with lattice parameters a=1.49071(4)nm， b=1.38594(4)nm， c=0.70292(1)nm， β=108.509(1)° and Z=4; The Cu(Ⅰ) ion is chelated by a phen ligand and two aqua ligands in cis arrangement and assumes a C2 symmetric square-planar geometry with the CuN₂O₂ core. Eight Cu(phen)(H₂O)₂·ClO₄ molecules are interconnected by strong hydrogen bonds between coordinated water molecules and uncoordinated perchlorate anions to form a molecular scale cavities along c axis. The bond distances of Cu-N and Cu-O are 0.2003(4)nm and 0.1973(3)nm， respectively. CCDC： 197600.     

Sequential injection system with higher dimensional electrochemical sensor signals Part 1. Voltammetric e-tongue for the determination of oxidizable compounds

A sequential injection analysis (SIA) system was developed with the aim of obtaining an automatic and versatile way to prepare standards needed in the study of systems with higher dimensional sensor signals. To illustrate this, different analytical techniques were used in determinations of several analytes. Automated potentiometric calibrations of different potentiometric sensors, with and without interference, were carried out. Useful determinations of selectivity coefficients with two degrees of freedom were obtained. Simultaneous voltammetric determinations have also been done. Firstly, simultaneous determinations of lead and cadmium, using epoxy-graphite composite as the working electrode, have enabled a separate calibration for each metal to be obtained. Next, a voltammetric electronic tongue was designed and applied to the determination of oxidizable species. The use of artificial neural networks has solved the overlapped signal of ascorbic acid, 4-aminophenol and 4-acetamidophenol (paracetamol). A set of 63 data points was prepared automatically and has facilitated the training of an electronic tongue for these three analytes. Accurate predictions of test solutions, in the range of 12–410 μM for ascorbic acid, 17–530 μM for 4-aminophenol and 10–420 μM for paracetamol, have been achieved with RMSEs lower than 0.10 μM.     

Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons

This paper presents an optosensor for screening of four polycyclic aromatic hydrocarbons: anthracene (ANT), benzo[a]pyrene (BaP), fluoranthene (FLT), and benzo[b]fluoranthene (Bbf) using a photomultiplier device with an artificial neural network as transducer. The optosensor is based on the on-line immobilization on a non-ionic resin (Amberlite XAD-4) solid support in a continuous flow. The determination was performed in 15 mM H₂PO₄⁻/HPO₄²⁻ buffer solution at pH 7 and 25% of 1,4-dioxane. Feed forward neural networks (multiplayer perceptron) have been trained to quantify the considered Polycyclic aromatic hydrocarbons (PAHs) in mixtures under optimal conditions. The optosensor proposed was also applied satisfactorily to the determination of the considered PAHs in water samples in presence of the other 12 EPA–PAHs.     

Effects of structural imperfections on the dynamic mechanical response of main-chain smectic elastomers

We examine the influence of structural imperfections on mechanical damping in polydomain smectic main-chain liquid crystalline elastomers (MCLCE) subjected to small strain oscillatory shear. The mechanical loss factor tan δ = G″(ω)/G′(ω) exhibits a strong maximum (tan δ ≈ 1.0) near the smectic-isotropic (clearing) transition. “Optimal” elastomers that exhibit minimal equilibrium swelling in a good solvent are compared with highly swelling “imperfect elastomers” that contain higher concentrations of structural imperfections such as pendant chains. For the imperfect elastomers, tan δ is markedly enhanced in the isotropic state because of relaxation of pendant chains and other imperfections. However, within the smectic state, the magnitude of tan δ and its temperature dependence are similar for optimal and imperfect elastomers at ω = 1 Hz. The prominent loss peak near the clearing transition arises from segment-level relaxations that are insensitive to the details of chain connectivity. Smectic MCLCE can be tailored for applications as vibration-damping materials by manipulating the clearing transition temperature through the backbone structure or by deliberate introduction of structural imperfections such as pendant chains. © 2007 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 45: 3267–3276, 2007     

Modelling and prediction of retention in high-performance liquid chromatography by using neural networks

Summary Multi-layer feed-forward neural networks trained with an error back-propagation algorithm have been used to model retention behaviour of liquid chromatography as a function of the composition of the mobile phases. Conventional hydro-organic and micellar mobile phases were considered. Accurate retention modelling and prediction have been achieved using mobile phases defined by two, three and four parameters. With micellar mobile phases, the parameters involved included the concentrations of surfactant and organic modifier, pH and temperature. It is shown that neural networks provide a competitive tool to model varied inherent nonlinear relationships of retention behaviour with respect to the mobile phase parameters. The soft models defined by the weights of the networks are capable of accommodating all types of linear and nonlinear relationships, neural networks being specially useful when the relationships between retention behaviour and the mobile phase parameters are unknown. However, to train neural networks more experimental points than with hard-modelling methods are required, hence the use of the networks is recommended only for those cases where adequate theoretical or empirical models do not exist.     

Model networks of end-linked polydimethylsiloxane chains

Summary Elastomeric networks of high extensibility were prepared by end-linking mixtures of vinyl-terminated polydimethylsiloxane chains having molecular weights of approximately 600 and 11,000 g mol^–1, with silanes chosen to give junction functionalities ranging from 3 to 8. The resulting bimodal networks were studied in elongation, at 25 °C, to their rupture points, and in swelling equilibrium in benzene at room temperature. The elongation moduli [f ^*] were found to be in satisfactory agreement with previous results obtained by end-linking hydroxyl-terminated polydimethylsiloxane chains. Values of [f ^*] at low and moderate deformations gave relatively low values of the ratio of elasticity constants 2C ₂/2C ₁, which is a measure of the extent to which the elongation changes from approximately affine to nonaffine as the elongation increases. The low values obtained for this ratio are presumably due to diminished interpenetration of configurational domains in the case of very short chains. In spite of its small magnitude, 2C ₂/2C ₁ does show some decrease with increase in , as predicted by the recent molecular theory of rubberlike elasticity developed by Flory. The swelling equilibrium results were also found to be in satisfactory agreement with theory. The elongation moduli increased significantly at high elongations, and the values of the elongation at which the upturn was first discernible were very nearly independent of , This is consistent with the interpretation of this anomalous behaviour in terms of limited chain extensibility. The maximum extensibility generally decreased somewhat with increase in and this caused a decrease in both the ultimate strength and the toughness of the elastomer, as measured by the energy required for rupture.     

Artificial neural networks study of the catalytic reduction of resazurin: stopped-flow injection kinetic-spectrophotometric determination of Cu(II) and Ni(II)

An artificial neural network (ANN) procedure was used in the development of a catalytic spectrophotometric method for the determination of Cu(II) and Ni(II) employing a stopped-flow injection system. The method is based on the catalytic action of these ions on the reduction of resazurin by sulfide. ANNs trained by back-propagation of errors allowed us to model the systems in a concentration range of 0.5-6 and 1-15 mg l⁻¹ for Cu(II) and Ni(II), respectively, with a low relative error of prediction (REP) for each cation: REP_Cu(II) = 0.85% and REP_Ni(II) = 0.79%. The standard deviations of the repeatability (s_r) and of the within-laboratory reproducibility (s_w) were measured using standard solutions of Cu(II) and Ni(II) equal to 2.75 and 3.5 mg l⁻¹, respectively: s_r[Cu(II)] = 0.039 mg l⁻¹, s_r[Ni(II)] = 0.044 mg l⁻¹, s_w[Ni(II)] = 0.045 mg l⁻¹ and s_w[Ni(II)] = 0.050 mg l⁻¹. The ANNs-kinetic method has been applied to the determination of Cu(II) and Ni(II) in electroplating solutions and provided satisfactory results as compared with flame atomic absorption spectrophotometry method. The effect of resazurin, NaOH and Na₂S concentrations and the reaction temperature on the analytical sensitivity is discussed.     

Radiation synthesis of n-vinyl 2-pyrrolidone/acrylonitrile interpenetrating polymer networks and their use in uranium recovery from aqueous systems

Interpenetrating Polymer Networks (IPNs) based on Poly n-vinyl 2-pyrrolidone (PVP) and Acrylonitrile (AN) were prepared by irradiating PVP solutions prepared in AN. PVP/AN mixtures were irradiated by ⁶⁰Co-γ rays at room temperature at a dose rate of 0.5 kGy/hour. IPNs were characterized by using FT-IR and Thermal Analysis techniques. The chelating adsorbents containing amidoxime groups were prepared by the reaction of these IPNs with hydroxylamine in aqueous NaOH solution at 50°C. These amidoxime containing adsorbents were used in adsorption studies for the recovery of uranium from aqueous systems. The adsorption capacity of an IPN with equivolume fraction of PVP and amidoximated PAN was found to be 750mg UO₂²⁺/g dry amidoximated IPN.