Original scientific paper

Categorical Modeling of the Flow Pattern of Liquid Organic Compounds Between Blade Electrodes Using Semiempirical and ab initio Quantum Chemical Descriptors

Takahiro Suzuki ; Natural Science Laboratory, Toyo University, 2-11-10 Oka, Asaka, Saitama 351-8510, Japan
Kohei Yoshida ; Department of Chemical Engineering, Faculty of Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Hiroya Onizuka ; Department of Chemical Engineering, Faculty of Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Yoshio Iwai ; Department of Chemical Engineering, Faculty of Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Yasuhiko Arai ; Department of Chemical Engineering, Faculty of Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan
Aynur Aptula ; Department of Chemical Ecotoxicology, UFZ Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, Germany
Ralph Kühne ; Department of Chemical Ecotoxicology, UFZ Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, Germany
Ralf-Uwe Ebert ; Department of Chemical Ecotoxicology, UFZ Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, Germany
Gerrit Schüürmann ; Department of Chemical Ecotoxicology, UFZ Centre for Environmental Research, Permoserstr. 15, D-04318 Leipzig, Germany

Abstract

For a data set of 30 organic fluids, categorical modeling has been employed to predict the flow pattern under an external electric field. To this end, a previously generated data set was augmented by 10 compounds with new experimental results, and quantum chemical methods have been used to characterize the geometric and electronic structure of the molecules on both the semiempirical and ab initio levels of theory. Both linear discriminant analysis (LDA) and binary logistic regression (BLR) have been employed to model the flow rate (high vs. low) and flow direction (left vs. right). For the flow rate, good LDA and BLR calibration statistics using the dipole moment, hydrophobicity and some charged partial surface area (CPSA) descriptors is accompanied with moderate prediction statistics, as evaluated through simulated external validation, and activity scrambling shows that chance correlation is not relevant. Additional neural network analyses yielded no stable models due to constraints imposed by the data set size. For the flow direction, LDA and BLR calibration and prediction statistics show more variation among the different models generated, with an overall performance inferior to the one for the flow rate. Here, besides CPSA descriptors, two parameters characterizing the softness of the electronic structure are involved. In general, BLR is slightly superior to LDA for both properties. The results are discussed in terms of contingency table statistics and with respect to the mechanistic meaning of molecular descriptors.

Keywords

flow pattern; organic liquids; contingency table statistics; linear discriminant analysis; binary logistic regression; quantum chemical descriptors

Hrčak ID:

102731

URI

https://hrcak.srce.hr/102731

Publication date:

31.5.2004.

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