On Face Index of Silicon Carbides

Zhang, Xiujun; Raza, Ali; Fahad, Asfand; Jamil, Muhammad Kamran; Chaudhry, Muhammad Anwar; Iqbal, Zahid

doi:10.1155/2020/6048438

Cited by 22 publications

(9 citation statements)

References 22 publications

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“…This work contributes to a deeper understanding of the dynamics of complex systems.In the realm of molecular graph theory, graph energies derived from the adjacency matrix play a pivotal role in predicting molecular stability and reactivity. Wang et al investigate the application of graph energies in understanding the electronic structure of chemical compounds, revealing correlations between specific graph energy measures and molecular properties [70], [71]. Their findings have implications for computational chemistry and drug discovery.…”

Section: F Graph Energiesmentioning

confidence: 99%

A Spectrum-Based Approach to Network Analysis Utilizing Laplacian and Signless Laplacian Spectra to Torus Networks

Raza,

Munir,

Hussain

et al. 2024

IEEE Access

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Exploring the applications of Laplacian and signless Laplacian spectra extends beyond theoretical chemistry, computer science, electrical networks, and complex networks. These spectra, with their relevance, provide valuable insights into the structures of real-world networks and facilitate the prediction of their structural properties. The focal point of the study lies in the spectrum-based analysis of torus grid graphs. From these analyses, crucial network measures such as mean-first passage time, average path length, spanning trees, and spectral radius are derived. This research enriches our comprehension of the interplay between graph spectra and network characteristics, offering a holistic understanding of complex networks. Consequently, it contributes to the ability to make predictions and conduct analyses across diverse scientific disciplines.

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Section: F Graph Energiesmentioning

confidence: 99%

A Spectrum-Based Approach to Network Analysis Utilizing Laplacian and Signless Laplacian Spectra to Torus Networks

Raza,

Munir,

Hussain

et al. 2024

IEEE Access

Self Cite

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“…The vertices of a graph represent items like cities, individuals, or molecules, while the edges reflect the connections or interactions between these entities. Edges can be directed (for a one-way connection) or undirected (for a two-way connection) 2 . Weights can also be added to graph edges to signify the strength or expense of the connections.…”

Section: Introductionmentioning

confidence: 99%

On topological indices and entropy measures of beryllonitrene network via logarithmic regression model

Yu,

Siddiqui,

Hussain

et al. 2024

Sci Rep

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Chemical graph theory, a subfield of graph theory, is used to investigate chemical substances and their characteristics. Chemical graph analysis sheds light on the connection, symmetry, and reactivity of molecules. It supports chemical property prediction, research of molecular reactions, drug development, and understanding of molecular networks. A crucial part of computational chemistry is chemical graph theory, which helps researchers analyze and manipulate chemical structures using graph algorithms and mathematical models. Beryllonitrene , a compound of interest due to its potential applications in various fields, is examined through the lens of graph theory and mathematical modeling. The study involves the calculation and interpretation of topological indices and graph entropy measures, which provide valuable insights into the structural and energetic properties of Beryllonitrene’s molecular graph. Logarithmic regression models are employed to establish correlations between these indices, entropy, and other relevant molecular attributes. The results contribute to a deeper understanding of Beryllonitrene’s complex characteristics, facilitating its potential applications in diverse scientific and technological domains. In this study, degree-based topological indices $$\text{TI}$$ TI are determined, as well as the entropy of graphs based on these $$\text{TI}$$ TI .

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“…Being one of the most extensively used wide bandgap materials, SiC performs a vital role in power industries by setting new principles in power savings as recti ers or switches in the system for data centers, wind turbines, solar cells, and electric vehicles along with high radiation and temperature-tolerant electronic appliances. Several papers have been devoted to the study of silicon and carbon-based structures, for details, see [19][20][21][22][23]. Some silicon carbides variants such as…”

Section: Introductionmentioning

confidence: 99%

Chemical Applicability of Newly Introduced Topological Invariants and Their Relation with Polycyclic Compounds

Alghazzawi

Raza

Munir

et al. 2022

Journal of Mathematics

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In quantitative structure-property and structure-activity relationships studies, several graph invariants, namely, topological indices have been defined and studied due to their numerous applications in computer networks, biotechnology, and nanochemistry. Topological indices are numeric parameters that describe the biological, physical, and chemical properties depending on the structure and topology of different chemical compounds. In this article, we inaugurated some degree-based novel indices, namely, geometric-harmonic GHI , harmonic-geometric HGI , neighborhood harmonic-geometric NHGI , and neighborhood geometric-harmonic NGHI indices and verified their chemical applicability. Furthermore, an attempt is made to calculate analytical closed formulas for different variants of silicon carbides and analyze the obtained results graphically.

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On Face Index of Silicon Carbides

Cited by 22 publications

References 22 publications

A Spectrum-Based Approach to Network Analysis Utilizing Laplacian and Signless Laplacian Spectra to Torus Networks

A Spectrum-Based Approach to Network Analysis Utilizing Laplacian and Signless Laplacian Spectra to Torus Networks

On topological indices and entropy measures of beryllonitrene network via logarithmic regression model

Chemical Applicability of Newly Introduced Topological Invariants and Their Relation with Polycyclic Compounds

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