Exploring a novel approach for computing topological descriptors of graphene structure using neighborhood multiple M-polynomial DOI Creative Commons
Tumiso Kekana, Kazeem Olalekan Aremu, Maggie Aphane

et al.

Frontiers in Applied Mathematics and Statistics, Journal Year: 2025, Volume and Issue: 10

Published: Jan. 17, 2025

Graphene, composed of a single layer carbon atoms arranged in hexagonal lattice pattern, has been the focus extensive research due to its remarkable properties and practical applications. Topological indices (TIs) play crucial role studying graphene's structure as mathematical functions mapping molecular graphs real numbers, capturing their topological characteristics. To compute these TIs, we employ M-polynomial approach, an efficient method for deriving degree-based descriptors graphs. In this study, analyze neighborhood multiple use it derive eleven TIs. These TIs allow us predict various graphene theoretically, bypassing need experiments or computer simulations. Furthermore, showcase numerical graphical representations emphasizing intricate connections between structural parameters. computations were further employed Quantitative Structure-Property Relationship (QSPR) mechanical graphene, such Young's Modulus, Poisson's Ratio, Shear Tensile Strength. The results showed strong correlations Ratio underscoring predictive power properties. findings highlight effectiveness characterizing predicting structures, providing valuable insights future applications material science.

Language: Английский

Exploring a novel approach for computing topological descriptors of graphene structure using neighborhood multiple M-polynomial DOI Creative Commons
Tumiso Kekana, Kazeem Olalekan Aremu, Maggie Aphane

et al.

Frontiers in Applied Mathematics and Statistics, Journal Year: 2025, Volume and Issue: 10

Published: Jan. 17, 2025

Graphene, composed of a single layer carbon atoms arranged in hexagonal lattice pattern, has been the focus extensive research due to its remarkable properties and practical applications. Topological indices (TIs) play crucial role studying graphene's structure as mathematical functions mapping molecular graphs real numbers, capturing their topological characteristics. To compute these TIs, we employ M-polynomial approach, an efficient method for deriving degree-based descriptors graphs. In this study, analyze neighborhood multiple use it derive eleven TIs. These TIs allow us predict various graphene theoretically, bypassing need experiments or computer simulations. Furthermore, showcase numerical graphical representations emphasizing intricate connections between structural parameters. computations were further employed Quantitative Structure-Property Relationship (QSPR) mechanical graphene, such Young's Modulus, Poisson's Ratio, Shear Tensile Strength. The results showed strong correlations Ratio underscoring predictive power properties. findings highlight effectiveness characterizing predicting structures, providing valuable insights future applications material science.

Language: Английский

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