QSPRs for Molecular Diffusion Coefficients in Polymeric Passive Samplers: A Comparison of Simple Molecular and...

Linear quantitative structure-property relationships (QSPRs) for the prediction of diffusion coefficients (log D_p ) 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 log D_p values that ranged over about 5 log units and 3 log units for PDMS and LDPE, respectively. The quality of log D_p 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 log D_p . The molecular descriptor QSPR resulted in a correlation coefficient of R² =0.78 and RMSE of 0.45 for log D_p . 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 log D_p . The sigma moment descriptor QSPR resulted in a correlation coefficient of R² =0.66 and RMSE of 0.33 for log D_p . 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.