QSPR modeling of potentiometric sensitivity towards heavy metal ions for polymeric membrane sensors

• Published in: Sensors and actuators. B, Chemical Chemical, 2019-12, Vol.301, p.126941, Article 126941
• Main Authors: Soloviev, Vitaly, Varnek, Alexandre, Babain, Vasily, Polukeev, Valery, Ashina, Julia, Legin, Evgeny, Legin, Andrey, Kirsanov, Dmitry
• Format: Article
• Language: English
• Subjects: Chemical Sciences; Cheminformatics; Consensus model; Copper; Electrochemical sensitivity; Ensemble structure-property modeling; Feasibility studies; Heavy metals; Ionophores; Ligands; Membranes; Modelling; Molecular fragment descriptors; Organic chemistry; Potentiometric sensors; QSPR; Sensitivity; Sensors
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2019.126941

•QSPR modeling is applied to study polymeric sensor membranes.•Potentiometric sensitivity correlates with ionophore chemical structure.•Importance of structural molecular fragments in agreement with general considerations. Potentiometric electrodes with plasticized membranes containing various ligands are widely employed as ion-selective sensors and as cross-sensitive sensors in multisensor systems. The design and testing of the appropriate ligands to make the sensors with required properties is a long and tedious process, which is not always successful. The concept of quantitative structure-property relationship (QSPR) seems to be an attractive complement to the ordinary ligand testing and design in potentiometric sensing. In this study we explore the feasibility of QSPR as a tool for in silico prediction of sensor performance of various ligands in PVC-plasticized potentiometric sensor membranes. The data on potentiometric sensitivity towards Cu2+, Zn2+, Cd2+, Pb2+ of membranes based on 35 nitrogen-containing ligands were employed for QSPR modeling. In spite of the limited dataset the derived models relating the chemical structures of the ligands with their electrochemical sensitivities have reasonable precision of sensitivity prediction with root mean squared errors RMSE around 5 mV/dec and squared determination coefficient R2det about 0.8 in external 10-fold cross-validation for zinc, cadmium and lead. This shows a good promise for further research in this area.