Distribution of micro-amounts of europium in the two-phase water–HCl–nitrobenzene–N,N’-dimethyl-N,N’-diphenyl-2,6-di-picolinamide–hydrogen dicarbollylcobaltate extraction system

Extraction of micro-amounts of europium by a nitrobenzene solution of hydrogen dicarbollylcobaltate (H+B-) in the presence of N,N’-dimethyl-N,N’-diphenyl-2,6-dipicolinamide (MePhDPA, L) was investigated. The equilibrium data were explained assuming that the species HL+, HL+2, HL3+2 and HL3+3 are extracted into the organic phase. The values of the extraction and stability constants of the species in nitrobenzene saturated with water were determined

N,N,N′,N′-Tetra­ethyl­pyridine-2,6-dicarboxamide

The title compound, C15H23N3O2, crystallizes with two mol­ecules in the asymmetric unit which are linked by a C—H⋯N hydrogen bond. In the crystal, mol­ecules are connected via weak C—H⋯O and C—H⋯N hydrogen bonds between the amide O atoms and ethyl chains and between pyridine N atoms and aromatic H atoms in para positions. C—H⋯π inter­actions also occur

N,N′-Diethyl-N,N′-diphenyl­pyridine-2,6-dicarboxamide

The asymmetric unit of the title compound, C23H23N3O2, contains two mol­ecules in both of which, one amide N atom is in a syn position with respect to the pyridine N atom, while the other amide N atom is in an anti position (the syn--anti conformation). There are minor conformational differences between the two mol­ecules, as reflected in the Npyridine—C—C—Namide torsion angles of −44.9 (3) and 136.0 (2)° for one mol­ecule and 43.5 (3) and −131.1 (2)° for the other mol­ecule. However, the two mol­ecules show significant differences in the orientation of an ethyl group, with corresponding C—C—N—C torsion angles of 86.6 (3)° for one mol­ecule and 79.6 (3)° for the other mol­ecule. In the crystal, mol­ecules are linked by weak C—H⋯O hydrogen bonds, forming a three-dimensional supra­molecular network

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

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

Nonlinear Multivariate Regression Algorithms for Improving Precision of Multisensor Potentiometry in Analysis of Spent Nuclear Fuel Reprocessing Solutions

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

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

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

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

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

N,N,N′,N′-Tetraisobutylpyridine-2,6-dicarboxamide

In the title compound, C23H39N3O2, the amide O atoms are displaced by 1.020 (1) and 1.211 (1) Å from the mean plane of the central pyridine ring. In the crystal, molecules are connected by weak C—H...O hydrogen bonds between methylene groups in the isobutyl substituents and the amide O atoms