Topological Aspects of Synthetic Polymers through NM Polynomials
Authors
Sushmitha Jain1, *, A. S. Maragadam2, V. Lokesha3, Dafik4
1Department of Studies in Mathematics, Ballari Institute of Technology and Management, Ballari, India
2Department of Studies in Mathematics, Vijayanagara Sri Krishnadevaraya University, Ballari, India
3Department of studies in Mathematics, Vijayanagara Sri Krishnadevaraya University, Ballari, India
4Department of Studies in Mathematics Education, Universitas Jember, Jember, Indonesia
*Corresponding author.
Email: sushmithajain9@gmail.com
Corresponding Author
Sushmitha Jain
Available Online 29 June 2024.
- DOI
- 10.2991/978-94-6463-445-7_6How to use a DOI?
- Keywords
- Vulcanized rubber network; poly-methyl methacrylate network; NM-polynomial; topological index
- Abstract
In this study, we delve into the versatile application of the Neighborhood M-Polynomial (NM) in predicting a wide array of material characteristics. Our research investigates the capability of the neighborhood M-polynomial to discern neighborhood degree sum-based topological indices when analyzing synthetic polymers. These indices serve as pivotal tools, enabling us to accurately predict the diverse physical, chemical, and biological properties inherent in the materials under scrutiny.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sushmitha Jain AU - A. S. Maragadam AU - V. Lokesha AU - Dafik PY - 2024 DA - 2024/06/29 TI - Topological Aspects of Synthetic Polymers through NM Polynomials BT - Proceedings of the 2nd International Conference on Neural Networks and Machine Learning 2023 (ICNNML 2023) PB - Atlantis Press SP - 43 EP - 54 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-445-7_6 DO - 10.2991/978-94-6463-445-7_6 ID - Jain2024 ER -