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1. Introduction
Mathematical chemistry provides tools such as polynomials and functions that depend upon the information hidden in the symmetry of graphs of chemical compounds and helps to predict properties of the understudy molecular compound without the use of quantum mechanics. A topological index is a numerical parameter of a graph and depicts its topology. It describes the structure of molecules numerically and are used in the development of qualitative structure activity relationships (QSARs). There are three kinds of topological indices, namely, degree-based, distance-based, and surface-based topological indices. Lot of research has been done on degree-based topological indices, for example, see [1–9]. Degree-based topological indices correlate the structure of the molecular compound with its various physical properties, biological activities, and chemical reactivity [10–14]. Boiling point, heat of formation, fracture toughness, strain energy, and rigidity of a molecule are strongly connected to its graphical structure.
The first topological index was introduced by Wiener when he was studying the boiling point of alkanes [15], which is now known as the Wiener index [16–20]. In 1975, Milan Randić introduced a simple topological index called the Randić index [21]. Many research papers and survey papers have been written on this graph invariant due to its interesting mathematical properties and valuable applications in chemistry [22–27]. The other oldest topological indices are Zagreb indices defined by Gutman and Trinajstic in [28] and are one of the most studied topological indices [29–33]. Topological indices are helpful in guessing properties of concerned compounds and are used in QSPRs [34–37]. There are more than 148 topological indices in the literature [38–42], but none of them are able to guess all the properties of the concerned compound (together they do it to some extent). Therefore, there is always room to define new topological indices [43]. Recently, in 2017, the first and second Gourava indices [44] were defined as
In the same year, the first and second hyper-Gourava indices [45] have been defined as
Note that
2. Methodology
To compute our results, first we constructed the graph of the concerned molecular compounds and counted the total number of vertices and edges. Secondly, we divided the edge set of concerned graphs into different classes based on the degrees of end vertices. By applying definitions of Gourava indices, we computed our desired results. We plotted our computed results by using Maple 2015 to see their dependencies on the involved parameters.
3. Gourava Indices
In this section, we present our main computational results. This section consists of three subsections. In Section 3.1, we present results about silicone carbides
3.1. Gourava Indices for Silicon Carbides
Silicon carbide (SiC), also called carborundum, is a semiconductor containing silicon and carbon. It occurs in nature as the incredibly uncommon mineral Moissanite. Manufactured SiC powder has been created in mass since 1893 for use as an abrasive. Grains of silicon carbide are reinforced together by sintering to shape extremely hard ceramic production that are generally utilized in applications requiring high continuance, for example, vehicle brakes, vehicle clutches, and ceramic plates in impenetrable vests. Electronic utilizations of silicon carbide, for example, light-emitting diodes (LEDs) and locators in early radios, were first exhibited around 1907. SiC is utilized in semiconductor electronic devices that work at high temperatures or high voltages, or both. Huge single crystals of silicon carbide can be developed by the Lely technique, and they can be cut into gems known as manufactured Moissanite. SiC with a high surface zone can be created from SiO2 contained in the plant material. Due to huge amount of application, silicone carbides have been studied extensively [6, 42]. In this section, we computed Gourava indices for silicon carbides
3.1.1. Gourava Indices for Silicon Carbide
The molecular graphs of silicon carbide
Table 1
Degree-based edge partition of
1 | |
1 | |
Theorem 1.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 2.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.1.2. Gourava Indices for Silicon Carbide
The molecular graphs of silicon carbide
Table 2
Degree-based edge partition of
2 | |
1 | |
Theorem 3.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 4.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.1.3. Gourava Indices for Silicon Carbide
The unit cell of
Table 3
Degree-based edge partition of
2 | |
Theorem 5.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 6.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.1.4. Gourava Indices for Silicon Carbide
The unit cell of
Table 4
Degree-based edge partition of
2 | |
1 | |
Theorem 7.
Let
(1)
(2)
Proof.
From the edge partition of the edge set of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 8.
Let
(1)
(2)
Proof.
From the edge partition of the edge set of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.1.5. Graphical Comparison of Results of Silicone Carbides
In Figures 17–20, we can observe that the behavior of all indices is exponentially increasing with respect to the involved parameters.
[figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF]Codes for plotting the first and second Gourava indices for silicon carbide
3.2. Gourava Indices for Bismuth Triiodide
3.2.1. Bismuth Triiodide Chain
The molecular graph of the unit cell of
Table 5
Degree-based edge partition of
Theorem 9.
Let
(1)
(2)
Proof.
From the edge partition of the edge set of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 10.
Let
(1)
(2)
Proof.
From the edge partition of the edge set of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.2.2. Bismuth Triiodide Sheet
The molecular graph of the bismuth triiodide sheet
Table 6
Degree-based edge partition of
Theorem 11.
Let
(1)
(2)
Proof.
From the edge partition of the edge set of the bismuth triiodide sheet
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 12.
Let
(1)
(2)
Proof.
From the edge partition of the edge set of the bismuth triiodide sheet
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.3. Graphical Representation
Graphical representation of computed topological indices for the bismuth triiodide chain is shown in Figures 24–27, and the graphical representation of the bismuth triiodide sheet is shown in Figures 28–31.
[figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF][figure omitted; refer to PDF]3.4. Gourava Indices for Dendrimers
In the medication mathematical model, the structure of the drug is addressed as an undirected graph, where each vertex exhibits a molecule and each edge addresses a bond between atoms. A huge number of new drugs have been made each year. From this time forward, it asks for a giant measure of work to choose the pharmacological compound and organic qualities of these new drugs, and such remaining tasks at hand end up being progressively specific and grouped. It requires enough reagent rigging and accomplices to test the exhibitions and the responses of new drugs. Nevertheless, in cut down poor countries and locales (for instance, certain urban networks and countries in South America, Southeast Asia, Africa, and India), there is no sufficient money to settle reagents and apparatus which can be used to gauge the biochemical properties. For topological study of dendrimers, we refer [53–66].
3.4.1. Gourava Indices of Porphyrin Dendrimer
The algebraic graph of porphyrin dendrimer
Table 7
Degree-based edge partition of
Theorem 13.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 14.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.4.2. Propyl Ether Imine Dendrimer (
The algebraic graph of propyl ether imine dendrimer (
Table 8
Degree-based edge partition of (
Theorem 15.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 16.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.4.3. Zinc-Porphyrin Dendrimer
The algebraic graph of zinc-porphyrin dendrimer
Table 9
Degree-based edge partition of
4 |
Theorem 17.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 18.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.4.4. Poly(EThyleneAmidoAmine) Dendrimer (
The algebraic graph of Poly(EThyleneAmidoAmine) dendrimer (
Table 10
Degree-based edge partition of (
Theorem 19.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first Gourava index for
(2)
The second Gourava index for
Theorem 20.
Let
(1)
(2)
Proof.
From the edge partition of
(1)
The first hyper-Gourava index for
(2)
The second hyper-Gourava index for
3.4.5. Graphical Comparison
In this section, we will present the graphical comparison of first, second, first hyper-, and second hyper-Gourava indices for porphyrin dendrimer
4. Conclusions and Future Works
It is important to calculate topological indices of dendrimers because it is a proved fact that topological indices help to predict many properties without going to the wet lab. There are more than around 148 topological indices, but none of them can completely describe all properties of a chemical compound. Therefore, there is always room to define and study new topological indices. Gourava indices are one step in this direction and are very close to Zagreb indices. Zagreb indices are very well studied by chemists and mathematicians due to their huge applications in chemistry. It is an interesting problem for researchers to study chemical properties and bonds of Gourava indices.
Authors’ Contributions
All authors have equal contribution.
Acknowledgments
This paper was supported by the Educational Commission of Anhui Province of China, under Grant no. KJ2017A627, Quality Engineering Project of Anhui Province, under Grant no. 2016sxzx021, and Quality Engineering Project of Anhui Xinhua University, under Grant nos. 2015xqjdx04; 2015xxk06; AH20171221607.
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Abstract
Silicon carbide (SiC), also called carborundum, is a semiconductor containing silicon and carbon. Dendrimers are repetitively branched molecules that are typically symmetric around the core and often adopt a spherical three-dimensional morphology. Bismuth(III) iodide is an inorganic compound with the formula
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1 School of Pharmacy, Anhui Xinhua University, Hefei 230088, China
2 Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan
3 Department of Mathematics, COMSATS University of Islamabad, Lahore Campus, Lahore 54000, Pakistan
4 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam; Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam