ABSTRACT
Linear quantitative structure-property relationships (QSPRs) for the prediction of diffusion coefficients (logâ Dp ) were developed for organic contaminants in two common passive sampler materials, polydimethylsiloxane (PDMS) and low-density polyethylene (LDPE). Literature data was compiled for both PDMS and LDPE resulting in final data sets of 196 and 79 compounds, respectively. Data sets contained compounds with logâ Dp values that ranged over about 5 log units and 3 log units for PDMS and LDPE, respectively. The quality of logâ Dp prediction using either simple molecular descriptors or quantum-chemical based COSMO-RS sigma moment descriptors was compared for both materials. For PDMS, the sigma moment descriptor QSPR had the best predictivity with a correlation coefficient of R2 =0.85 and root mean square error (RMSE) of 0.36 for logâ Dp . The molecular descriptor QSPR resulted in a correlation coefficient of R2 =0.78 and RMSE of 0.45 for logâ Dp . For LDPE, the molecular descriptor QSPR had the best predictivity, with the final correlation coefficient of R2 =0.86 and RMSE of 0.21 for logâ Dp . The sigma moment descriptor QSPR resulted in a correlation coefficient of R2 =0.66 and RMSE of 0.33 for logâ Dp . The purely electronic structure-based sigma moments are therefore shown to be a viable option for descriptors compared to the more commonly used molecular descriptors for organic contaminants in PDMS. The significance of the descriptors in each QSPR is discussed.