Effect of polymerization conditions on o‐phenylenediamine and o‐phenetidine oxidative copolymers

A series of new o‐phenylenediamine (OPD)/o‐phenetidine (PHT) copolymers with partly phenazine‐like structures has been successfully synthesized at three polymerization temperatures by chemically oxidative polymerization in four different polymerization media. The molecular structures and properties of the resulting OPD/PHT polymers were investigated by IR, UV–vis and high‐resolution ¹H NMR spectroscopies, and DSC, in order to ascertain the effect of reaction temperature, comonomer ratio and acid medium. The copolymerization mechanism of OPD with PHT monomers has been proposed. It is found that the statistical OPD/PHT copolymer obtained at a temperature of 118 °C has a higher degree of polymerization than that obtained at 12–17 °C. The OPD content in the copolymers calculated from NMR spectroscopic analysis is higher than that in the feed OPD content, whereas the OPD content calculated from element analysis is slightly lower than the feed OPD content. It can be predicted that denitrogenation takes place in the OPD units during the polymerization process at OPD/PHT molar ratios of 90/10 and 100/0. These OPD/PHT copolymers exhibit a much better solubility than the OPD homopolymer, hence suggesting an incorporation of PHT units into the phenazine structure of the homopolymer. The thermal behavior of the copolymers was also studied. Copyright © 2004 Society of Chemical Industry     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

Exponential Stability of Interval Dynamical Systems with Quadratic Nonlinearity

This article proposes an approach for investigating the exponential stability of a nonlinear interval dynamical system with the nonlinearity of a quadratic type on the basis of the Lyapunov’s direct method. It also constructs an inner estimate of the attraction domain to the origin for the system under consideration.