QSPR modeling of potentiometric sensitivity towards heavy metal ions for polymeric membrane sensors
| Autor: | Julia Ashina, Dmitry Kirsanov, Valery Polukeev, Vasily Babain, Andrey Legin, Evgeny Legin, Alexandre Varnek, Vitaly Soloviev |
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| Přispěvatelé: | Chimie de la matière complexe (CMC), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Quantitative structure–activity relationship
Materials science Mean squared error Metal ions in aqueous solution Potentiometric titration Inorganic chemistry Metals and Alloys 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics Electrochemistry 01 natural sciences 0104 chemical sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials Membrane Materials Chemistry Potentiometric sensor Sensitivity (control systems) Electrical and Electronic Engineering 0210 nano-technology Instrumentation [CHIM.CHEM]Chemical Sciences/Cheminformatics |
| Zdroj: | Sensors and Actuators B: Chemical Sensors and Actuators B: Chemical, Elsevier, 2019, 301, pp.126941. ⟨10.1016/j.snb.2019.126941⟩ |
| ISSN: | 0925-4005 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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