Researchers have recalibrated polyparameter linear free energy relationships (PP-LFERs) for low-density polyethylene (LDPE) passive samplers. By utilizing a robust regression method and reliable partition coefficients and solute descriptors, the recalibrated models exhibited improved predictive performance, accurately estimating LDPE-water and LDPE-air partition coefficients across a wide range of compounds.
Equilibrium passive sampling using low-density polyethylene (LDPE) film has emerged as a valuable technique for assessing contaminant concentrations in water and air. To enhance the accuracy of partition predictions, researchers from Southeast University in China recalibrated polyparameter linear free energy relationships (PP-LFERs) for LDPE-water and LDPE-air systems (1). By utilizing a robust regression method and reliable partition coefficients and solute descriptors from a wide range of polar and nonpolar compounds, the recalibrated PP-LFERs exhibited improved predictive performance and successfully estimated LDPE-water and LDPE-air partition coefficients across multiple orders of magnitude. The article was published in the Journal of Chromatography A.
This article provides valuable insights related to partitioning mechanisms and the use of passive sampling techniques, which can be relevant in chromatographic studies. The recalibrated polyparameter linear free energy relationships (PP-LFERs) presented in the article contribute to the understanding of partitioning behavior and can potentially aid in the development of more accurate chromatographic methods involving low-density polyethylene (LDPE) passive samplers. Therefore, the findings in the article can indirectly inform and benefit the field of chromatography, particularly in terms of sample preparation and analyte partitioning considerations.
Previous studies on LDPE-water and LDPE-air systems had employed PP-LFERs based on Abraham's solute descriptors. However, the inclusion of unreliable partition coefficients and solute descriptors in these studies had led to unexpected system parameters and reduced predictive accuracy. To address this limitation, the researchers meticulously collected a comprehensive dataset comprising over a hundred compounds with low redundancy from the literature. This reliable dataset, along with the robust regression method, facilitated the recalibration of PP-LFERs for LDPE-water and LDPE-air systems at temperatures ranging from 20 to 25 °C.
The recalibrated PP-LFERs demonstrated excellent performance, with root mean square errors ranging from 0.15 to 0.25 log units. These models successfully predicted partition coefficients (KiLDPEw and KiLDPEa) for a wide range of compounds, spanning over 10 orders of magnitude. The reanalysis of partitioning mechanisms revealed that LDPE has a greater tendency to form dispersion interactions with solutes compared to n-alkanes (C6–C16). However, LDPE presents challenges in forming cavities, indicating lower accessibility for solutes. The distinct constant terms observed in LDPE-water and n-hexadecane-water systems were attributed not only to LDPE's crystallinity but also to other factors.
The improved accuracy and reliability of the recalibrated PP-LFERs for LDPE-water and LDPE-air systems offer valuable tools for accurately predicting partition coefficients in LDPE passive samplers. These advancements contribute to the broader use and application of LDPE-based passive sampling techniques, enabling more precise assessments of contaminants in water and air environments.
(1) Liu, Z.; Sun, X.; Xu, Y. Recalibrating polyparameter linear free energy relationships and reanalyzing mechanisms for partition of nonionic organic compounds to low-density polyethylene passive sampler. J. Chromatogr. A 2023, 1700, 464039. DOI: 10.1016/j.chroma.2023.464039