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Topological descriptors and connectivity analysis of coronene fractal structures: insights from atom-bond sum-connectivity and Sombor indices

  • Xiujun Zhang , Sahar Aftab , Sadia Noureen EMAIL logo , Adnan Aslam ORCID logo and Sobhy M. Ibrahim
Published/Copyright: February 10, 2025

Abstract

Coronene, a polycyclic aromatic hydrocarbon (PAH) consisting of six benzene rings fused in a hexagonal arrangement, exhibits a fractal structure that is significant in various fields such as condensed matter physics, materials science, surface science, and interdisciplinary areas like nanotechnology and astrochemistry. Topological descriptors, which characterize the geometric and connectivity properties of a structure independently of specific spatial coordinates, are crucial for understanding coronene’s complex geometry and connectivity. In this study, we compute the atom-bond sum (ABS)-connectivity index and four versions of the Sombor indices for three different configurations of the coronene fractal structure: Zig-zag Hexagonal Coronene Fractal (ZHCF), Armchair Hexagonal Coronene Fractal (AHCF), and Rectangular Coronene Fractal (RCF). To assess their chemical applicability, we develop linear regression models to estimate the physicochemical properties boiling point (BP) and molecular weight (MW) of benzene derivatives using these topological indices. The regression parameters for each case are provided, and the results show that the ABS index outperforms all other topological indices, making it the most effective predictor for these properties.


Corresponding author: Sadia Noureen, Department of Mathematics, Faculty of Science, University of Gujrat, Gujrat, Pakistan, E-mail:

Funding source: This work was supported by Researchers Supporting Project number (RSP2025R100), King Saud University, Riyadh, Saudi Arabia.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. All authors contributed equally.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by Researchers Supporting Project number (RSP2025R100), King Saud University, Riyadh, Saudi Arabia.

  7. Data availability: Not applicable.

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Received: 2024-11-22
Accepted: 2025-01-22
Published Online: 2025-02-10
Published in Print: 2025-03-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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