Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices

The topological invariants are related to the molecular graph of the chemical structure and are numerical numbers that help us to understand the topology of the concerned chemical structure. With the help of these numbers, many properties of graphene can be guessed without preforming any experiment....

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Main Authors: Jie Wu, Saima Nazeer, Iftikhar Ahmed, Farkhanda Yasmin
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2022/3809806
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author Jie Wu
Saima Nazeer
Iftikhar Ahmed
Farkhanda Yasmin
author_facet Jie Wu
Saima Nazeer
Iftikhar Ahmed
Farkhanda Yasmin
author_sort Jie Wu
collection DOAJ
description The topological invariants are related to the molecular graph of the chemical structure and are numerical numbers that help us to understand the topology of the concerned chemical structure. With the help of these numbers, many properties of graphene can be guessed without preforming any experiment. Huge amount of calculations are required to obtain topological invariants for graphene, but by applying basic calculus roles, neighborhood M -polynomial of graphene gives its indices. The aim of this work is to compute neighborhood degree-dependent indices for the graph of graphene and the line graph of subdivision graph of graphene. Firstly, we establish neighborhood M-polynomial of these families of graphs, and then, by applying basic calculus, we obtain several neighborhood degree-dependent indices. Our results play an important role to understand graphene and enhance its abilities.
format Article
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institution Kabale University
issn 2314-4785
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Mathematics
spelling doaj-art-30d376f2de3942bcbe43f7480e4ea5752025-08-20T03:55:12ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/3809806Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological IndicesJie Wu0Saima Nazeer1Iftikhar Ahmed2Farkhanda Yasmin3Basic Teaching DepartmentDepartment of MathematicsDepartment of MathematicsDepartment of MathematicsThe topological invariants are related to the molecular graph of the chemical structure and are numerical numbers that help us to understand the topology of the concerned chemical structure. With the help of these numbers, many properties of graphene can be guessed without preforming any experiment. Huge amount of calculations are required to obtain topological invariants for graphene, but by applying basic calculus roles, neighborhood M -polynomial of graphene gives its indices. The aim of this work is to compute neighborhood degree-dependent indices for the graph of graphene and the line graph of subdivision graph of graphene. Firstly, we establish neighborhood M-polynomial of these families of graphs, and then, by applying basic calculus, we obtain several neighborhood degree-dependent indices. Our results play an important role to understand graphene and enhance its abilities.http://dx.doi.org/10.1155/2022/3809806
spellingShingle Jie Wu
Saima Nazeer
Iftikhar Ahmed
Farkhanda Yasmin
Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices
Journal of Mathematics
title Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices
title_full Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices
title_fullStr Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices
title_full_unstemmed Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices
title_short Study of Graphene Networks and Line Graph of Graphene Networks via NM-Polynomial and Topological Indices
title_sort study of graphene networks and line graph of graphene networks via nm polynomial and topological indices
url http://dx.doi.org/10.1155/2022/3809806
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