考虑不规则拓扑指标的碳酸盐岩材料网络三维结构建模与表征

IF 0.7 Q2 MATHEMATICS
Shahid Zaman, M. Salman, Asad Ullah, S. Ahmad, Mohammed Salaheldeen Abdelgader Abas
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引用次数: 5

摘要

三维分子结构的拓扑表征是理论和计算化学中的一个新兴研究领域。这些结构描述符用于各种领域,包括化学图论、药物传递和纳米材料表征。定量结构描述符可用于描述给定化合物的化学和物理性质。分子图的拓扑指数是一种数字量,它使我们能够在不进行实验的情况下收集有关化学结构的信息并揭示其隐藏的性质。由于实施成本低,沸石网络被认为是流行的化学网络。沸石由于其优异的化学特性,在化学、医药和商业生产中有着广泛的应用。钠石网络是由一种非常独特的沸石框架组成的,叫做钠石。它是一个由相互连接的笼子和隧道组成的三维网络,为各种化学和物理过程提供了理想的环境。本文研究了基于度的和基于逆度的碳酸盐岩材料网不规则度指标。这些指标提供了一个定量测量的不规则行为的钠石材料网络。它可以用来识别网络中发生不规则行为的区域,并比较不同的网络,以确定哪个更不规则。此外,这些指数还可用于监测不规律性随时间的变化,使我们能够衡量实施的任何干预措施的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Three-Dimensional Structural Modelling and Characterization of Sodalite Material Network concerning the Irregularity Topological Indices
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.
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