QSAR and machine learning models of redox potentials of some organic pigments
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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-in...dependent) 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:
21st Young Researchers' Conference Materials Sciences and Engineering : program and the book of abstracts, 2023, 35-35Издавач:
- Belgrade : Institute of Technical Sciences of SASA
Напомена:
- Twenty-First Young Researchers’ Conference - Materials Science and Engineering: Program and the Book of Abstracts; November 29 – December 1, 2023, Belgrade, Serbia
Колекције
Институција/група
VinčaTY - CONF AU - Stevanović, Kristina AU - Maksimović, Jelena AU - Senćanski, Jelena AU - Pagnacco, Maja AU - Senćanski, Milan PY - 2023 UR - https://vinar.vin.bg.ac.rs/handle/123456789/12310 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 - 21st Young Researchers' Conference Materials Sciences and Engineering : program and the book of abstracts T1 - QSAR and machine learning models of redox potentials of some organic pigments SP - 35 EP - 35 UR - https://hdl.handle.net/21.15107/rcub_vinar_12310 ER -
@conference{ author = "Stevanović, Kristina and Maksimović, Jelena and Senćanski, Jelena 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 = "21st Young Researchers' Conference Materials Sciences and Engineering : program and the book of abstracts", title = "QSAR and machine learning models of redox potentials of some organic pigments", pages = "35-35", url = "https://hdl.handle.net/21.15107/rcub_vinar_12310" }
Stevanović, K., Maksimović, J., Senćanski, J., Pagnacco, M.,& Senćanski, M.. (2023). QSAR and machine learning models of redox potentials of some organic pigments. in 21st Young Researchers' Conference Materials Sciences and Engineering : program and the book of abstracts Belgrade : Institute of Technical Sciences of SASA., 35-35. https://hdl.handle.net/21.15107/rcub_vinar_12310
Stevanović K, Maksimović J, Senćanski J, Pagnacco M, Senćanski M. QSAR and machine learning models of redox potentials of some organic pigments. in 21st Young Researchers' Conference Materials Sciences and Engineering : program and the book of abstracts. 2023;:35-35. https://hdl.handle.net/21.15107/rcub_vinar_12310 .
Stevanović, Kristina, Maksimović, Jelena, Senćanski, Jelena, Pagnacco, Maja, Senćanski, Milan, "QSAR and machine learning models of redox potentials of some organic pigments" in 21st Young Researchers' Conference Materials Sciences and Engineering : program and the book of abstracts (2023):35-35, https://hdl.handle.net/21.15107/rcub_vinar_12310 .