Three-Dimensional Structural Modelling and Characterization of Sodalite Material Network concerning the Irregularity Topological Indices

• Published in: Journal of mathematics (Hidawi) Vol. 2023; pp. 1 - 9
• Main Authors: Zaman, Shahid, Salman, Muhammad, Ullah, Asad, Ahmad, Shahzad, Abdelgader Abas, Mohammed Salaheldeen
• Format: Journal Article
• Language: English
• Published: Cairo Hindawi 2023; John Wiley & Sons, Inc; Wiley
• Subjects: Chemistry; Computational chemistry; Graph theory; Greenhouse gases; Irregularities; Mathematics; Molecular structure; Nanomaterials; Networks; Physical properties; Sodalite; Structural analysis; Three dimensional models; Topology; Zeolites
• ISSN: 2314-4629; 2314-4785
• DOI: 10.1155/2023/5441426

Topological characterization of 3D molecular structures is an emerging study area in theoretical and computational chemistry. These structural descriptors are used in a variety of domains, including chemical graph theory, drug delivery, and nanomaterial characterization. Quantitative structural descriptors can be used to characterize the chemical and physical properties of a given compound. Topological indices of molecular graphs are numerical quantities that allow us to collect information about the chemical structure and reveal its hidden qualities without performing experiments. Due to the low cost of implementation, zeolite networks are considered popular chemical networks. Zeolites are widely used networks with applications in chemistry, medicine, and commercial production owing to their excellent chemical features. The sodalite network is composed of a very unique type of zeolite framework called sodalite. It is a three-dimensional network of interconnected cages and tunnels that provide an ideal environment for a wide range of chemical and physical processes. This paper deals with the sodalite material network’s degree-based and reverse degree-based irregularity indices. These indices provide a quantitative measure of the irregular behaviour of the sodalite material network. It can be used to identify areas of the network where irregular behaviour is occurring and to compare different networks to determine which is more irregular. Additionally, these indices can be used to monitor changes in irregularity over time, allowing us to measure the impact of any interventions that are implemented.