Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling

Abstract

Developing a potentiometric sensor with required target properties is a challenging task. This work explores the potential of quantitative structure-property relationship (QSPR) modeling in the prediction of potentiometric selectivity for plasticized polymeric membrane sensors based on newly synthesized ligands. As a case study, we have addressed sensors with selectivity towards carbonate—an important topic for environmental and biomedical studies. Using the logKsel(HCO3−/Cl−) selectivity data on 40 ionophores available in literature and their substructural molecular fragments as descriptors, we have constructed a QSPR model, which has demonstrated reasonable precision in predicting selectivities for newly synthesized ligands sharing similar molecular fragments with those employed for modeling.

Predicting the Potentiometric Sensitivity of Membrane Sensors Based on Modified Diphenylphosphoryl Acetamide Ionophores with QSPR Modeling

“…In this work, the ISIDA Substructure Molecular Fragments (SMF) were applied for this purpose. The method for obtaining molecular descriptors was similar to that in our previous article [ 16 ]. Briefly, ISIDA is a free software package for QSPR modelling, and can be applied for obtaining SMF [ 21 ].…”

“…Recent studies have shown that QSPR can be successfully applied to modelling the sensing properties of ionophore-based potentiometric sensors, including sensitivity [ 15 ] and selectivity [ 16 , 17 ] parameters. It has also been shown that similar approaches work well with potentiometric sensors, based on inorganic materials, like zeolites [ 18 ].…”

“…It has also been shown that similar approaches work well with potentiometric sensors, based on inorganic materials, like zeolites [ 18 ]. The report [ 16 ] demonstrated that prediction of the selectivity coefficients for newly synthesized carbonate ionophores is possible through QSPR, with reasonable precision. This study aims to extend the application of QSPR in the field of potentiometric sensors with novel tasks—we aim to predict the sensitivity of several new ionophores towards three heavy metal ions: copper, cadmium and lead.…”

Recent developments in ionophore-based potentiometric electrochemical sensors for oceanic carbonate detection

“…94 The current optimal additive to ionophore ratio is reported to be 60 mol% additive to ionophore. 157 Improved selectivity positively impacts the reproducibility and lifetime of the sensor. 180 Therefore, a key component for designing ISEs with a high selectivity is limiting or eliminating ionic impurities in the membrane so the ionic additives can function optimally within the membrane.…”

“…Currently, the optimized composition of a membrane with PVC as the polymer substrate contains 33% PVC, 65% plasticizer, 2% ionophore, and 60 mol% additive relative to the ionophore. 157…”