QSAR and machine learning models of redox potentials of some organic pigments
Аутори
Stevanović, KristinaMaksimović, Jelena
Senćansk, Jelen
Pagnacco, Maja
Senćanski, Milan
Остала ауторства
Marković, SmiljaКонференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The organic pigments offer promising opportunities for developing new sustainable electrode materials for lithium batteries. Some of them have been identified as cathode material with very encouraging reversible lithium ion storage characteristics. One of them is a naturally occurring purpurin extracted from the Madder plant (Rubia tinctorum) for which we confirmed this good electrochemical behavior by cyclic voltammetry. One of the strategies towards obtaining materials with even better characteristics is a structural modification of already existing pigments. Building a theoretical model that could predict the redox properties of these new compounds can be very useful towards achieving that goal. In order to build a 3D QSAR (quantitative structure–activity relationship) model for material redox potential prediction, 9 organic pigments with known redox potentials were extracted from the literature. Based on... molecular interaction field (MIF) probes we calculated standard GRIND (grid-independent) descriptors and constructed following principal PLS (partial least squares) model. By validation with the literature data, but also with the obtained experimental data for purpurin, this model proved very reliable in predicting the redox potential. A comparison was also made with the machine learning model that was formed in parallel.
Извор:
Twenty -first young researchers’ conference materials science and engineering, 2023, 35-35Издавач:
- Belgrade : Institute of Technical Sciences of SASA
Напомена:
- Book of abstract: Twenty-FirstYoung Researchers Conference –Materials Science and EngineeringNovember 29 –December 1, 2023, Belgrade, Serbia
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Stevanović, Kristina AU - Maksimović, Jelena AU - Senćansk, Jelen AU - Pagnacco, Maja AU - Senćanski, Milan PY - 2023 UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2222 AB - The organic pigments offer promising opportunities for developing new sustainable electrode materials for lithium batteries. Some of them have been identified as cathode material with very encouraging reversible lithium ion storage characteristics. One of them is a naturally occurring purpurin extracted from the Madder plant (Rubia tinctorum) for which we confirmed this good electrochemical behavior by cyclic voltammetry. One of the strategies towards obtaining materials with even better characteristics is a structural modification of already existing pigments. Building a theoretical model that could predict the redox properties of these new compounds can be very useful towards achieving that goal. In order to build a 3D QSAR (quantitative structure–activity relationship) model for material redox potential prediction, 9 organic pigments with known redox potentials were extracted from the literature. Based on molecular interaction field (MIF) probes we calculated standard GRIND (grid-independent) descriptors and constructed following principal PLS (partial least squares) model. By validation with the literature data, but also with the obtained experimental data for purpurin, this model proved very reliable in predicting the redox potential. A comparison was also made with the machine learning model that was formed in parallel. PB - Belgrade : Institute of Technical Sciences of SASA C3 - Twenty -first young researchers’ conference materials science and engineering T1 - QSAR and machine learning models of redox potentials of some organic pigments EP - 35 SP - 35 UR - https://hdl.handle.net/21.15107/rcub_imagine_2222 ER -
@conference{ author = "Stevanović, Kristina and Maksimović, Jelena and Senćansk, Jelen and Pagnacco, Maja and Senćanski, Milan", year = "2023", abstract = "The organic pigments offer promising opportunities for developing new sustainable electrode materials for lithium batteries. Some of them have been identified as cathode material with very encouraging reversible lithium ion storage characteristics. One of them is a naturally occurring purpurin extracted from the Madder plant (Rubia tinctorum) for which we confirmed this good electrochemical behavior by cyclic voltammetry. One of the strategies towards obtaining materials with even better characteristics is a structural modification of already existing pigments. Building a theoretical model that could predict the redox properties of these new compounds can be very useful towards achieving that goal. In order to build a 3D QSAR (quantitative structure–activity relationship) model for material redox potential prediction, 9 organic pigments with known redox potentials were extracted from the literature. Based on molecular interaction field (MIF) probes we calculated standard GRIND (grid-independent) descriptors and constructed following principal PLS (partial least squares) model. By validation with the literature data, but also with the obtained experimental data for purpurin, this model proved very reliable in predicting the redox potential. A comparison was also made with the machine learning model that was formed in parallel.", publisher = "Belgrade : Institute of Technical Sciences of SASA", journal = "Twenty -first young researchers’ conference materials science and engineering", title = "QSAR and machine learning models of redox potentials of some organic pigments", pages = "35-35", url = "https://hdl.handle.net/21.15107/rcub_imagine_2222" }
Stevanović, K., Maksimović, J., Senćansk, J., Pagnacco, M.,& Senćanski, M.. (2023). QSAR and machine learning models of redox potentials of some organic pigments. in Twenty -first young researchers’ conference materials science and engineering Belgrade : Institute of Technical Sciences of SASA., 35-35. https://hdl.handle.net/21.15107/rcub_imagine_2222
Stevanović K, Maksimović J, Senćansk J, Pagnacco M, Senćanski M. QSAR and machine learning models of redox potentials of some organic pigments. in Twenty -first young researchers’ conference materials science and engineering. 2023;:35-35. https://hdl.handle.net/21.15107/rcub_imagine_2222 .
Stevanović, Kristina, Maksimović, Jelena, Senćansk, Jelen, Pagnacco, Maja, Senćanski, Milan, "QSAR and machine learning models of redox potentials of some organic pigments" in Twenty -first young researchers’ conference materials science and engineering (2023):35-35, https://hdl.handle.net/21.15107/rcub_imagine_2222 .