Abstract
Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal–organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.
1. Introduction
The optical properties of metallic nanoparticles have drawn the attention of scientists and researchers. The heat created by the nanoparticles overwhelms cancer tissue while causing no harm to healthy cells. Niobium nanoparticles have the capacity to easily attach to ligands, making them ideal for optothermal cancer treatment. Chemical graph theory is a contemporary branch of applied chemistry, which has remained an attractive area of research for scientists during the past two decades, and significant contributions have been made by scientists in this area of research including [1,2,3,4,5,6,7]. We investigate the relationship between atoms and bonds using combinatorial approaches such as vertex and edge partitions. Topological indices are essential in providing directions for treating malignancies or tumors. These indices can be obtained experimentally or numerically. Although experimental data are valuable, they are also costly; therefore, computational analysis gives a cost-effective and time-efficient solution.
The transformation of a chemical structure into a number is used to generate a topological index. The topological index is a graph invariant that characterizes the graph’s topology while remaining invariant throughout graph automorphism. A topological index is a numerical number defined only by the graph. The eccentricity-based topological indices are crucial in chemical graph theory [8]. Wiener, a chemist, first used a topological index in 1947 while researching the relationship between the molecular structure and the physical and chemical properties of certain hydrocarbon compounds [9]. In 2010, Damir et al. defined the redefined second Zagreb index as the same as the inverse sum indeg index [10].
We used the concept of valency-based entropies in this article, where and denote the valency of atoms, and , within the molecule. Kulli started computing valency-based topological indices in 2016 using the valency of atom bonds and some Banhatti indices [11,12,13], each of which has the following definition:
The first valence-based K-Banhatti polynomial and the first K-Banhatti index are as follows:
The second valence based K-Banhatti polynomial and the second K-Banhatti index are as follows, respectively:
The first valence based hyper K-Banhatti polynomial and the firstst hyper K-Banhatti index are as follows, respectively:
In 2013, Ranjini [14] introduced a redefined version of the Zagreb indices , and in 2021, Shanmukha [15] defined them as
The third redefined Zagreb index was defined as
Recently, Ali et al. amalgamated the atom-bond connectivity index and sum connectivity index and initiated the new molecular descriptor named the atom-bond sum-connectivity index [16], defined as:
Shannon first popularized the concept of entropy in his 1948 work [17]. Entropy is the quantity of thermal energy per unit temperature in a system that is not accessible for meaningful work [18,19]. Because the work is derived from organized molecular motion, entropy is also a measure of a system’s molecular disorder or unpredictability [20,21]. In this article, we build the Niobium dioxide NbO and the metal–organic framework (MOF) to compute the K-Banhatti and redefined Zagreb entropies using K-Banhatti indices [22,23,24], and redefined Zagreb indices, respectively. The idea of entropy is extracted from Shazia Manzoor’s paper [25].
2. Valency-Based Entropy
The idea of edge-weighted graph entropy was introduced in 2009 [26], for an edge-weighted graph, where is the vertex set, the edge set, and the edge-weight of an edge is represented by . The entropy of an edge-weighted graph is defined as
- The first -Banhatti entropy
- The second -Banhatti entropy
- The first -hyper Banhatti entropy
- The second -hyper Banhatti entropy
- The first redefined Zagreb entropyLet . Then, the first redefined Zagreb index (5) is given byNow, by inserting these values into Equation (9), the first redefined Zagreb entropy is
- The second redefined Zagreb entropyLet . Then, the second redefined index (6) is given byNow, by inserting these values into Equation (9), the second redefined Zagreb entropy is
- The third redefined Zagreb entropyLet . Then, the third redefined Zagreb index (7) is given byNow, by inserting these values into Equation (9), the third redefined Zagreb entropy is
- Atom-bond sum connectivity entropyLet . Then, the fourth atom-bond connectivity index (8) is given byBy inserting the values of into Equation (9), the atom-bond sum connectivity entropy is
3. Niobium Dioxide NbO
Niobium Nb, a refractory metal, is a good choice for the initial shell of nuclear fusion reactors. It does, however, have a strong attraction for O and C, both of which are available in pyrotechnics and refrigerant-like liquids. As part of the first barrier, Nb is well known for its ability to interact very effectively with O [27]. As a result, reliable thermodynamic data on NbO, NbO, NbO, and other intermediate phases, such as NbO, are very effective. In transistors, niobium monoxide is used as a gate electrode, and a (NbO/NbO) junction may be used in robust switching devices. In this article, we will attempt to explain NbO, which has a total atom count of ; see Figure 1.
Figure 1.
Niobium dioxide 3D structure.
There are three types of atoms in NbO based on their valency: eight atoms with valency 2, atoms with valency 3, and atoms with valency 4. Table 1 shows the atom-bond partitions of NbO derived from these results.
Table 1.
Atom-bond partition of NbO.
- The first -Banhatti entropy of NbOLet NbO be a network of a niobium dioxide molecule. Then, by using Equation (1) and Table 1, the first K-Banhatti polynomial isAfter simplifying Equation (18), we obtain the first K-Banhatti index by taking the first derivative at .
- The second -Banhatti entropy of NbO
- The first -hyper Banhatti entropy of NbO
- The second -hyper Banhatti entropy of NbO
- The first redefined Zagreb entropy of NbOLet NbO be a network of a niobium dioxide molecule. Then, by using Equation (5) and Table 1, the first redefined Zagreb polynomial isTaking the first derivative of Equation (26) at , we obtain the first redefined Zagreb index
- The second redefined Zagreb entropy of NbOLet NbO be a network of a niobium dioxide molecule. Then, by using Equation (6) and Table 1, the second redefined Zagreb polynomial isTaking the first derivative of Equation (28) at , we obtain the second redefined Zagreb index
- The third redefined Zagreb entropy of NbOLet NbO be a network of a niobium dioxide molecule. Then, by using Equation (7) and Table 1, the third redefined Zagreb polynomial isTaking the first derivative of Equation (30) at , we obtain the third redefined Zagreb index
- Atom-bond sum connectivity entropy of NbOLet NbO be a network of a niobium dioxide molecule. Then, using Equation (8) and Table 1, the atom-bond sum connectivity polynomial isTaking the first derivative of Equation (32) at , we obtain the atom-bond sum connectivity index
Comparison
In this section, we compare the K-Banhatti indices namely (first K-Banhatti index), (second K-Banhatti index), (first hyper K-Banhatti index), (second hyper K-Banhatti index) and the redefined Zagreb indices (, , ) for NbO numerically and graphically in Table 2 and Figure 2, respectively.
Table 2.
Numerical comparison of the K-Banhatti topological indices of NbO.
Figure 2.
Graphical comparison of TI’s of NbO.
4. Metal–Organic Framework
Metal–organic frameworks are distinguished by their three-dimensional frameworks composed of metallic ions. This metal–organic framework has the molecular formula FeTPyP–Co, where Fe denotes iron, TPyP denotes tetrakis pyridyl porphyrin, and Co denotes cobalt [28]. All metal ions and organic molecules in the network can accommodate a wide range of guest molecules. Metal–organic frameworks have several uses, including as energy storage devices, gas storage, heterogeneous catalysis, and chemical evaluation. We will examine a structure of a metal–organic framework called , where s and t are the unit cells in a row and column, respectively. The is shown in Figure 3. There are atoms in the , and atom-bonds are used, as Figure 3 of demonstrates.
Figure 3.
Two-dimensional MOF_(2,2) structure.
The atom-bonds partition of the is shown in Table 3.
Table 3.
Atom-bonds partition of .
- The first -Banhatti entropy ofLet be a metal–organic framework. Then, using Equation (1) and Table 3, the first K-Banhatti polynomial isNow, we compute the 1st K-Banhatti entropy of () by using Table 3 and Equation (35) in Equation (10) in the following way:After simplification, we obtain
- The second -Banhatti entropy of
- The first -hyper Banhatti entropy ofLet be a metal–organic framework. Then, using Equation (3) and Table 3, the first K-hyper Banhatti polynomial isNow, we compute the first K-hyper Banhatti entropy of by using Table 3 and Equation (40) in Equation (12) in the following way:After simplification, we obtain
- The second -hyper Banhatti entropy ofLet be a metal–organic framework. Then, by using Equation (4) and Table 3, the second K-Banhatti polynomial isNow, we compute the second K-hyper Banhatti entropy of by using Table 3 and Equation (42) in Equation (13) in the following way:After simplification, we obtain
- The first redefined Zagreb entropy ofLet be a metal–organic framework. Then, using Equation (5) and Table 3, the first redefined Zagreb polynomial isTaking the first derivative of Equation (44) at , we obtain the first redefined Zagreb indexNow, we compute the first redefined Zagreb entropy using Table 3 and Equation (45) in Equation (14) in the following way:After simplification, we obtain
- The second redefined Zagreb entropy ofLet be a metal–organic framework. Then, using Equation (6) and Table 3, the second redefined Zagreb polynomial isTaking the first derivative of Equation (46) at , we obtain the second redefined Zagreb indexNow, we compute the second redefined Zagreb entropy by using Table 3 and Equation (47) in Equation (15) in the following way:After simplification, we obtain
- The third redefined Zagreb entropy ofLet be a metal–organic framework. Then, using Equation (7) and Table 3, the third redefined Zagreb polynomial isTaking the first derivative of Equation (48) at , we obtain the third redefined Zagreb indexNow, we compute the third redefined Zagreb entropy by using Table 3 and Equation (49) in Equation (16) in the following way:After simplification, we obtain
- Atom-bond sum connectivity entropy ofLet NbO be a network of a niobium oxide molecule. Then, using Equation (8) and Table 1, the atom-bond sum connectivity polynomial isTaking the first derivative of Equation (50) at , we obtain the atom-bond sum connectivity index
Comparison
In this section, we compare the K-Banhatti and redefined Zagreb indices for numerically and graphically in Table 4 and Figure 4, respectively.
Table 4.
Numerical comparison of the topological indices of .
Figure 4.
Graphical comparison of TI’s of metal–organic framework.
5. Conclusions
The remarkable optical properties of metallic nanoparticles have piqued the interest of scientists and researchers. In this article, two important molecules niobium dioxide NbO and the were considered, and the accurate formulas of some important valency-based topological indices were calculated using the technique of atom-bond partitioning. We investigated the distance-based entropies associated with a new information function and evaluated the relationship between degree-based topological indices and degree-based entropies in this article using Shannon’s entropy and Chen et al.’s entropy definitions. The idea of distance-based entropy is widely ingrained in industrial chemistry. It is used to calculate the complexity of molecules and molecular ensembles, their electronic structure, signal processing, physicochemical processes, and so on. The K-Banhatti entropy, in conjunction with the chemical structure, thermodynamic entropy, energy, and computer sciences can play an essential role in bridging various domains and providing a foundation for new interdisciplinary research. In the future, we hope to expand this concept to include various chemical structures, allowing researchers to pursue new avenues in this field.
Author Contributions
Conceptualization, M.U.G., F.S., E.S.M.T.E.D., A.R.K., J.-B.L. and M.C.; methodology, M.U.G., F.S. and A.R.K.; validation, M.U.G. and F.S.; formal analysis, M.U.G., F.S., E.S.M.T.E.D., A.R.K., J.-B.L. and M.C.; investigation, M.U.G., F.S., E.S.M.T.E.D., A.R.K., J.-B.L. and M.C.; data curation, M.U.G. and F.S.; writing—original draft preparation, M.U.G., F.S. and A.R.K.; writing—review and editing, M.U.G. and A.R.K.; visualization, M.U.G., F.S., E.S.M.T.E.D., A.R.K., J.-B.L. and M.C.; supervision, M.U.G., F.S. and A.R.K.; project administration, M.U.G. and F.S.; funding acquisition, E.S.M.T.E.D., A.R.K. and J.-B.L. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
All data generated or analyzed during this study are included in this published article.
Conflicts of Interest
The authors declare no conflict of interest.
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