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. 2022 Sep 29;12(10):953.
doi: 10.3390/membranes12100953.

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

Nadezhda Vladimirova  1 , Elena Puchkova  1 , Dmitry Dar'in  1 , Alexander Turanov  2 , Vasily Babain  3 , Dmitry Kirsanov  1
Affiliations

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

Nadezhda Vladimirova et al. Membranes (Basel). .

Abstract

While potentiometric, plasticized membrane sensors are known as convenient, portable and inexpensive analytical instruments, their development is time- and resource-consuming, with a poorly predictable outcome. In this study, we investigated the applicability of the QSPR (quantitative structure-property relationship) method for predicting the potentiometric sensitivity of plasticized polymeric membrane sensors, using the ionophore chemical structure as model input. The QSPR model was based on the literature data on sensitivity, from previously studied, structurally similar ionophores, and it has shown reasonably good metrics in relating ionophore structures to their sensitivities towards Cu2+, Cd2+ and Pb2+. The model predictions for four newly synthesized diphenylphosphoryl acetamide ionophores were compared with real potentiometric experimental data for these ionophores, and satisfactory agreement was observed, implying the validity of the proposed approach.

Keywords: QSPR; heavy metals sensing; ion-selective electrodes; potentiometric sensitivity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) The chemical structure of N2,N2,N6,N6-tetraethylpyridine-2,6-dicarboxamide. (b) Table with several fragments and their counts in the compound.
Figure 2
Figure 2
Measured vs. predicted scatterplot of cadmium sensitivity model, with four latent variables (LVs). The diagonal line shows the ideal correlation.
Figure 3
Figure 3
Regression coefficients plot for cadmium sensitivity PLS model, with marked area of fragments with small contributions. X-axis indicates the number of descriptor (variable).
Figure 4
Figure 4
Fragments with high contributions to cadmium sensitivity.
Figure 5
Figure 5
(a) N,N-dimethylpicolinamide fragment; (b) 4,4′-dibromo-N6,N6′-diethyl- -N6,N6′-bis(4-hexylphenyl)-[2,2′-bipyridine]-6,6′-dicarboxamide with highlighted C-C-C-N-C-C=C (green) and C=C-C=C-C=N (red) fragments.
Figure 6
Figure 6
(a) Potentiometric response curves observed in copper solutions with four different types of sensors. (b) Potentiometric response curves for various metals observed with the sensor based on substance 1.
See this image and copyright information in PMC

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