Interpenetrating polymer networks of poly(carbonate-urethane) and polyvinyl pyridine

Simultaneous interpenetrating polymer networks (IPN's), pseudo IPN's, and liner blends of aliphatic poly(carbonate-urethane) (PCU) and polyvinyl pyridine (PVP) have been prepared and characterized by DSC, DMA, and TEM. The full IPN's of PCU and PVP had a single phase morphology only above 50 wt % PCU, as determined by both DSC and DMA and confirmed by transmission electron microscopy (TEM). However, in both pseudo IPN's of PCU and PVP and in their linear blends there exist multiple glass transitions and melting points seen by DSC and DMA indicating phase incompatibility. The full IPN's exhibited superior ultimate mechanical properties and solvent resistance as compared to the pseudo IPN's, liner blends, and the pure crosslinked PCU and PVP networks.     

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

Self-assembled,wormlike-cylinder network of polystyrene-block-poly(ethylene oxide) micelles in solution

We report the formation of a highly entangled and interconnected, self-assembled, wormlike-cylinder network of polystyrene-block-poly(ethylene oxide) in N, N-dimethylformamide/water. In this system, N,N-dimethylformamide was a common solvent and water was a selective solvent for the poly(ethylene oxide) blocks. The degrees of polymerization of the polystyrene and poly(ethylene oxide) blocks were 962 and 227, respectively. The network was formed at copolymer concentrations higher than 0.4 wt % and consisted of self-assembled, wormlike cylinders that were interconnected by Y-shaped, T-shaped, and multiple junctions. The network morphology was visualized with transmission electron microscopy. Capillary viscometry measurements revealed an order-of-magnitude increase in the inherent viscosity of the colloidal system upon the formation of the network. A similar effort to obtain a wormlike-cylinder network in an N,N-dimethylformamide/acetonitrile system, in which acetonitrile was a selective solvent for the poly(ethylene oxide) blocks, was unsuccessful even at high copolymer concentrations; instead, the wormlike cylinders showed a tendency to align. The viscosity measurements also did not show a substantial increase in the inherent viscosity. Thus, the solvent played a critical role in determining the formation of the self-assembled, wormlike-cylinder network. This formation of the network resulted from an interplay between the end-capping energy, bending energy (curvature), and configurational entropy of the self-assembled, wormlike-cylinder micelles that minimized the free energy. © 2006 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 3605–3611, 2006     

压力和温度对4-甲基二苯并噻吩和二苯并噻吩加氢脱硫反应的影响

研究了4-甲基二苯并噻吩(4-MDBT)和二苯并噻吩(DBT)在CoMo/γ-Al2O3上的加氢脱硫反应产物分布及其可能的反应网络,通过反应压力和温度对产物分布影响的研究,揭示了加氢脱硫反应的可能机理。研究发现4-MDBT在CoMo/γ-Al2O3上的加氢脱硫反应主要通过直接氢解路径和加氢路径进行,且两者反应速率相当；DBT在 CoMo/γ-Al2O3上的加氢脱硫反应主要通过直接氢解路径进行。4-MDBT分子位于4位的甲基阻碍其在催化剂表面通过硫原子的端连吸附,从而降低了其直接氢解脱硫路径的反应速率,因而也降低了其总的加氢脱硫转化率。反应压力降低,DBT和4-MDBT加氢脱硫反应中加氢路径反应速率明显下降,而其对氢解路径影响较小,但效果却与加氢路径相反,反应压力对4-MDBT转化率的影响大于DBT。反应温度对DBT和4-MDBT加氢脱硫反应中加氢路径和氢解路径都有明显影响,但是对DBT加氢脱硫反应中氢解路径的影响小于加氢路径,而对4-MDBT加氢脱硫反应中氢解路径的影响稍高于加氢路径,4-MDBT分子中甲基的供电子作用有利于相连苯环的加氢反应。     

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.