Raza Ali, Ismaeel Mishal, Tolasa Fikadu Tesgera
Department of Mathematics, University of Punjab Lahore, Lahore, Pakistan.
Department of Mathematics, Nanjing University of Science and Technology, Nanjing, China.
Sci Rep. 2024 Mar 25;14(1):7080. doi: 10.1038/s41598-024-54962-5.
In this study, we introduce a novel valency-based index, the neighborhood face index (NFI), designed to characterize the structural attributes of benzenoid hydrocarbons. To assess the practical applicability of NFI, we conducted a linear regression analysis utilizing numerous physiochemical properties associated with benzenoid hydrocarbons. Remarkably, the results revealed an extraordinary correlation exceeding 0.9991 between NFI and these properties, underscoring the robust predictive capability of the index. The NFI, identified as the best-performing descriptor, is subsequently investigated within certain infinite families of carbon nanotubes. This analysis demonstrates the index's exceptional predictive accuracy, suggesting its potential as a versatile tool for characterizing and predicting properties across diverse molecular structures, particularly in the context of carbon nanotubes.
在本研究中，我们引入了一种基于化合价的新型指数——邻面指数（NFI），旨在表征苯型烃的结构属性。为评估NFI的实际适用性，我们利用与苯型烃相关的众多物理化学性质进行了线性回归分析。值得注意的是，结果显示NFI与这些性质之间存在超过0.9991的极高相关性，突出了该指数强大的预测能力。被确定为表现最佳描述符的NFI，随后在某些无限碳纳米管族中进行了研究。该分析证明了该指数具有卓越的预测准确性，表明其作为一种通用工具在表征和预测各种分子结构性质方面的潜力，特别是在碳纳米管的背景下。
