Polynomials of Degree-Based Indices of Metal-Organic Networks

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Author(s): Ali Ahmad*, Muhammad Ahsan Asim, Muhammad Faisal Nadeem

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

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Aim and Objective: Metal-organic network (MON) is a special class of molecular compounds comprising of groups or metal ion and carbon-based ligand. These chemical compounds are examined employing one, two- or threedimensional formation of porous ore and subfamilies of polymers. Metal-organic networks are frequently utilized in catalysis for the parting & distillation of different gases and by means of conducting solid or super-capacitor. In various scenarios, the compounds are observed balanced in the procedure of deletion or diluter of the molecule and can be rebuilt with another molecular compound. The physical solidity and mechanical characteristics of the metal-organic network have attained great attention due to the mention properties. This study was undertaken to find the polynomials of MON.

Methods: Topological descriptor is a numerical number that is utilized to predict the natural correlation amongst the physico-chemical properties of the molecular structures in their elementary networks.

Results: After partitioning the vertices based on their degrees, we calculate different degree-based topological polynomials for two distinct metal-organic networks with an escalating number of layers containing both metals and carbon-based ligand vertices.

Conclusion: In the analysis of the metal-organic network, topological descriptors and their polynomials play an important part in modern chemistry. An analysis between the calculated various forms of the polynomials and topological descriptors through the numeric values and their graphs is also comprised.

Keywords: Topological polynomials, degree-based index, metal-organic networks, chemical compounds, quantitative structures activity relationship, quantitative structures property relationship

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1386207323666201229152828
Price: $95

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