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QSAR studies on partition coefficients of organic compounds for polydimethylsiloxane of solid-phase microextraction devices

Author:
Chao, K.-P., Wang, V.-S., Liu, C.-W., Lu, Y.-T.
Source:
International journal of environmental science and technology 2018 v.15 no.10 pp. 2141-2150
ISSN:
1735-1472
Subject:
alkanes, aromatic hydrocarbons, chemical species, coatings, computer software, models, octanol-water partition coefficients, polydimethylsiloxane, quantitative structure-activity relationships, regression analysis, solid phase microextraction
Abstract:
The solid-phase microextraction (SPME) technique has been widely applied to sample the environmental matrices for organic compounds. The success of employing SPME requires knowledge of the coating-matrix partition coefficients of analytes. Polydimethylsiloxane (PDMS) is the most widely used polymeric coating for the SPME device. In this study, the quantitative structure activity relationships (QSAR) for the PDMS-water (K fw) and PDMS-gas partition coefficients (K fg) of organic compounds were established using E-Dragon software and multiple linear regression analysis. K fw was significantly correlated to the BLTA96 descriptor, implying that the PDMS-water partition coefficients were primarily determined by the polarity of analyte molecules. In addition, K fg was significantly dependent on the Harary H index, i.e., molecular connectivity index or polarizability, of the organic compounds. If the organic compounds were grouped in alkanes and aromatic hydrocarbons, K fw and K fg were well proportional to their octanol–water partition coefficients. The statistical results of internal and external validation, determined by the square of the coefficient of multiple correlation (R ² ≥ 0.865) and the leave-one-out cross-validation (Q LOO² ≥ 0.751), showed that the QSAR models developed herein have good stability and great predictive power among the molecular descriptors and SPME/PDMS partition coefficients. The results of this study will facilitate the practical applications of SPME as a greener methodology.
Agid:
6138736