Abstract

This work initiates a concept of reduced reverse degree based RRDM-Polynomial for a graph, and differential and integral operators by using this RRDM-Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using the RRDM-Polynomial. The topological descriptors, denoted as TD’s, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.

Details

Title
On degree-based operators and topological descriptors of molecular graphs and their applications to QSPR analysis of carbon derivatives
Author
Khan, Abdul Rauf 1 ; Bhatti, Saad Amin 1 ; Tawfiq, Ferdous 2 ; Siddiqui, Muhammad Kamran 3 ; Hussain, Shahid 4 ; Ali, Mustafa Ahmed 5 

 Ghazi University, Department of Mathematics, Faculty of Sciences, Dera Ghazi Khan, Pakistan (GRID:grid.448869.f) (ISNI:0000 0004 6362 6107) 
 King Saud University, Mathematics Department, College of Science, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396) 
 COMSATS University Islamabad, Lahore Campus, Department of Mathematics, Lahore, Pakistan (GRID:grid.418920.6) (ISNI:0000 0004 0607 0704) 
 Lulea University of Technology, Energy Engineering Division, Department of Engineering Science and Mathematics, Lulea, Sweden (GRID:grid.6926.b) (ISNI:0000 0001 1014 8699) 
 Somali National University, Department of Mathematics, Faculty of Science, Mogadishu, Somalia (GRID:grid.412667.0) (ISNI:0000 0001 2156 6060) 
Pages
21543
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3104871098
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.