A potentiometric E-tongue system based on low-selective polymeric membrane and chalcogenide-glass electrodes is employed to monitor the taste-and-odor-causing pollutants, geosmin (GE) and 2-methyl-isoborneol (MIB), in drinkable water. The developed approach may permit a low-cost monitoring of these compounds in concentrations near the odor threshold concentrations (OTCs) of 20 ng/L. The experiments demonstrate the success of the E-tongue in combination with partial least squares (PLS) regression technique for the GE/MIB concentration prediction, showing also the possibility to discriminate tap water samples containing these compounds at two concentration levels: the same OTC order from 20 to 100 ng/L and at higher concentrations from 0.25 to 10 mg/L by means of PLS-discriminant analysis (DA) method. Based on the results, developed multisensory system can be considered a promising easy-to-handle tool for express evaluation of GE/MIB species and to provide a timely detection of alarm situations in case of extreme pollution before the drinkable water is delivered to end users.

We report on the development of a simple and cost-effective potentiometric sensor array that is based on manual “drawing” on the polymeric support with the pencils composed of graphite and different types of zeolites. The sensor array demonstrates distinct sensitivity towards a variety of inorganic ions in aqueous media. This multisensor system has been successfully applied to quantitative analysis of 100 real-life surface waters sampled in Mahananda and Hooghly rivers in the West Bengal state (India). Partial least squares regression has been utilized to relate responses of the sensors to the values of different water quality parameters. It has been found that the developed sensor array, or electronic tongue, is capable of quantifying total hardness, total alkalinity, and calcium content in the samples, with the mean relative errors below 18%.

This work aims to discuss quantification of rare earth metals in a complex mixture using the novel multi-ionophore approach based on potentiometric sensor arrays. Three compounds previously tested as extracting agents in reprocessing of spent nuclear fuel were applied as ionophores in polyvinyl chloride (PVC)-plasticized membranes of potentiometric sensors. Seven types of sensors containing these ionophores were prepared and assembled into a sensor array. The multi-ionophore array performance was evaluated in the analysis of Ln³⁺ mixtures and compared to that of conventional monoionophore sensors. It was demonstrated that a multi-ionophore array can yield RMSEP (root mean-squared error of prediction) values not exceeding 0.15 logC for quantification of individual lanthanides in binary mixtures in a concentration range 5 to 3 pLn³⁺.

Potentiometric multisensor systems were shown to be very promising tools for the quantification of numerous analytes in complex radioactive samples deriving from spent nuclear fuel reprocessing. Traditional multivariate calibration for these multisensor systems is performed with partial least squares regression—an intrinsically linear regression method that can provide suboptimal results when handling potentiometric signals from very complex multi-component samples. In this work, a thorough investigation was performed on the performance of a multisensor system in combination with non-linear multivariate regression models for the quantification of analytes in the PUREX (Plutonium–URanium EXtraction) process. The multisensor system was composed of 17 cross-sensitive potentiometric sensors with plasticized polymeric membranes containing different lipophilic ligands capable of heavy metals, lanthanides, and actinides binding. Regression algorithms such as support vector machines (SVM), random forest (RF), and kernel-regularized least squares (KRLS) were tested and compared to the traditional partial least squares (PLS) method in the simultaneous quantification of the following elements in aqueous phase samples of the PUREX process: U, La, Ce, Sm, Zr, Mo, Zn, Ru, Fe, Ca, Am, and Cm. It was shown that non-linear methods outperformed PLS for most of the analytes.

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 logK_sel(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.

There is not much known on the stability of plasticized polymeric sensor membranes against ionizing radiation. While recent studies have indicated the applicability of potentiometric sensors with such membranes for quantification of actinides and lanthanides in spent nuclear fuel reprocessing solutions, the real industrial application of such sensors will require their stability in ionizing radiation fields. The present study explores this problem and evaluates the stability of potentiometric sensitivity towards lanthanides and actinides for a variety of plasticized polymeric membranes based on different neutral ligands. We demonstrate that most of the studied sensor compositions retain their sensitivity up to 50–100 kGy of the absorbed gamma radiation dose. The higher doses lead to the gradual loss of sensitivity due to the radiolysis of ligands and a polymer membrane matrix as confirmed by electrochemical impedance and nuclear magnetic resonance studies.