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Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling. / Vladimirova, Nadezhda; Polukeev, Valery; Ashina, Julia; Babain, Vasily; Legin, Andrey; Kirsanov, Dmitry.

в: Chemosensors, Том 10, № 2, 43, 02.2022.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Vladimirova, N, Polukeev, V, Ashina, J, Babain, V, Legin, A & Kirsanov, D 2022, 'Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling', Chemosensors, Том. 10, № 2, 43. https://doi.org/10.3390/chemosensors10020043

APA

Vladimirova, N., Polukeev, V., Ashina, J., Babain, V., Legin, A., & Kirsanov, D. (2022). Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling. Chemosensors, 10(2), [43]. https://doi.org/10.3390/chemosensors10020043

Vancouver

Vladimirova N, Polukeev V, Ashina J, Babain V, Legin A, Kirsanov D. Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling. Chemosensors. 2022 Февр.;10(2). 43. https://doi.org/10.3390/chemosensors10020043

Author

Vladimirova, Nadezhda ; Polukeev, Valery ; Ashina, Julia ; Babain, Vasily ; Legin, Andrey ; Kirsanov, Dmitry. / Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling. в: Chemosensors. 2022 ; Том 10, № 2.

BibTeX

@article{c6dc656d7231493db6cc563e7e70ce2f,
title = "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.",
keywords = "Carbonate sensing, Ion-selective electrode, QSPR, Selectivity",
author = "Nadezhda Vladimirova and Valery Polukeev and Julia Ashina and Vasily Babain and Andrey Legin and Dmitry Kirsanov",
note = "Vladimirova, N.; Polukeev, V.; Ashina, J.; Babain, V.; Legin, A.; Kirsanov, D. Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling. Chemosensors 2022, 10, 43. https://doi.org/10.3390/chemosensors10020043",
year = "2022",
month = feb,
doi = "10.3390/chemosensors10020043",
language = "English",
volume = "10",
journal = "Chemosensors",
issn = "2227-9040",
publisher = "MDPI AG",
number = "2",

}

RIS

TY - JOUR

T1 - Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling

AU - Vladimirova, Nadezhda

AU - Polukeev, Valery

AU - Ashina, Julia

AU - Babain, Vasily

AU - Legin, Andrey

AU - Kirsanov, Dmitry

N1 - Vladimirova, N.; Polukeev, V.; Ashina, J.; Babain, V.; Legin, A.; Kirsanov, D. Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling. Chemosensors 2022, 10, 43. https://doi.org/10.3390/chemosensors10020043

PY - 2022/2

Y1 - 2022/2

N2 - 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.

AB - 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.

KW - Carbonate sensing

KW - Ion-selective electrode

KW - QSPR

KW - Selectivity

UR - http://www.scopus.com/inward/record.url?scp=85124326377&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/bf90cb4e-07a8-3217-8f0c-787b29bb3fcb/

U2 - 10.3390/chemosensors10020043

DO - 10.3390/chemosensors10020043

M3 - Article

AN - SCOPUS:85124326377

VL - 10

JO - Chemosensors

JF - Chemosensors

SN - 2227-9040

IS - 2

M1 - 43

ER -

ID: 94819997