Computational and comparative aspects of two carbon nanosheets with respect to some novel topological indices

Abstract

The recent discovery of various forms of carbon nanostructures inspired the researchers due to their uses in various fields. Numerous production approaches for carbon nanostructures have been presented; doping, functionalization, chemical modification, and filling have been achieved, and manipulation, characterization and separation of carbon nanostructures are now possible. On the other hand, the flexibility and strength of carbon nanosheets make them potential candidates in controlling further nanoscale structures, which indicates that they have an important role in nanotechnology engineering. Because of their uniform thickness of only around 1 nanometer( nm) , and their high mechanical, thermal and chemical stability, such materials are of high concern for a wide range of applications. A topological index or molecular descriptor is a mathematical formulation that can be applied to any graph which models some molecular structure. Based on the molecular descriptor, it is convenient to analyze mathematical values and advance investigation on some physicochemical properties of a molecule. Consequently, it is an effective method in replacing laborious, expensive and time-consuming laboratory experimentations. Molecular descriptors perform an important part in mathematical chemistry, especially during the investigation of Quantitative structure property relationships (QSPRs) and Quantitative structure-activity relationships (QSARs) that provide insight into animate effects based on chemical structures. In this paper, we have computed some novel neighborhood version molecular descriptors for the two carbon nanosheets V C 5 C 7 p , q and ( H C 5 C 7 p , q ) and derived formulas for them. Our computed results are surely helpful in understanding the topology of understudy nanosheets. These computed indices have best correlation with acentric factor and entropy, therefore, are effective in QSPRs and QSARs analysis with powerful accuracy.