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2000
Volume 22, Issue 7
  • ISSN: 1570-1794
  • E-ISSN: 1875-6271

Abstract

Background

Hexagonal fractals are intricate geometric patterns that exhibit self-similarity. They are characterized by their repetitive hexagonal shapes at different scales. Due to their unique properties and potential applications, hexagonal fractals have been studied in various fields, including mathematics, physics, and chemistry.

Objectives

The primary aim of this research is to provide a comprehensive analysis of hexagonal fractals, focusing on their topological indices, fractal dimensions, and their applications in structure-property modeling. We aim to calculate topological indices to quantify the structural complexity and connectivity of hexagonal fractals. Additionally, we will determine fractal dimensions to characterize their self-similarity and scaling behaviour. Finally, we will explore the relationship between topological indices, fractal dimensions, and relevant properties through structure-property modeling.

Methods

A systematic approach was employed to investigate hexagonal fractals. Various topological indices were computed using established mathematical techniques. Fractal dimensions were determined. Structure-property modeling was conducted by establishing relationships between the calculated topological indices and fractal dimensions with experimentally measured properties.

Results

The research yielded significant findings regarding hexagonal fractals. A variety of topological indices were calculated, revealing the intricate connectivity and structural complexity of these fractals. Fractal dimensions were determined, confirming their self-similar nature and scaling behaviour. Structure-property modeling demonstrated strong correlations between the topological indices and fractal dimensions with properties such as conductivity, mechanical strength, and chemical reactivity.

Conclusion

This research provides valuable insights into the topological characteristics, fractal dimensions, and potential applications of hexagonal fractals. The findings contribute to a deeper understanding of these complex structures and their relevance in various scientific domains. The developed structure-property modeling approaches offer a valuable tool for predicting and controlling the properties of materials based on their fractal structure. Future research may explore additional applications and delve into the underlying mechanisms governing the relationship between fractal structure and properties.

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2025-12-11
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References

  1. HodgmanC.D. LindS.C. Handbook of chemistry and physics.J. Phys. Colloid Chem.19495371139113910.1021/j150472a018
    [Google Scholar]
  2. TheilerJ. Estimating fractal dimension.J. Opt. Soc. Am. A Opt. Image Sci. Vis.1990761055107310.1364/JOSAA.7.001055
    [Google Scholar]
  3. SainsburyM. Aromatic compounds: A modern comprehensive treatise.Elsevier1995
    [Google Scholar]
  4. FranckH.G. StadelhoferJ.W. Industrial aromatic chemistry: Raw materials processes products.Springer science and business media2012
    [Google Scholar]
  5. LawalA.T. Polycyclic aromatic hydrocarbons. A review.Cogent Environ. Sci.201731133984110.1080/23311843.2017.1339841
    [Google Scholar]
  6. HaratiB. ShahtaheriS.J. KarimiA. AzamK. AhmadiA. RadM.A. HaratiA. Cancer risk analysis of benzene and ethyl benzene in painters.Basic Clin. Canc. Res.2016842228
    [Google Scholar]
  7. TomlinsonM. An introduction to the chemistry of benzenoid compounds.Elsevier2016
    [Google Scholar]
  8. ClaydenJ. GreevesN. WarrenS. Organic chemistry.USAOxford University Press201210.1093/hesc/9780199270293.001.0001
    [Google Scholar]
  9. AnslynE.V. DoughertyD.A. Modern physical organic chemistry.University science books2006
    [Google Scholar]
  10. BrushS.G. Dynamics of theory change in chemistry: Part 1. The benzene problem 1865–1945.Stud. Hist. Philos. Sci.1999301217910.1016/S0039‑3681(98)00027‑2
    [Google Scholar]
  11. KleinD.J. CraveyM.J. HiteG.E. Fractal benzenoids.Polycycl. Aromat. Comp.199122–3163182
    [Google Scholar]
  12. RajiM. JayalalithaG. Molecular graph of linear benzenoid compounds as fractals. Element.Educ. Online202120421462146
    [Google Scholar]
  13. Plavˇsi’cD. Clar structures in fractal benzenoids.Croat. Chem. Acta1992652279284
    [Google Scholar]
  14. BalabanA.T. Applications of graph theory in chemistry.J. Chem. Inf. Comput. Sci.198525333434310.1021/ci00047a033
    [Google Scholar]
  15. BondyJ.A. MurtyU.S.R. Graph theory.New YorkSpringer200810.1007/978‑1‑84628‑970‑5
    [Google Scholar]
  16. BalabanA.T. MotocI. BonchevD. MekenyanO. Topological indices for structure-activity correlations.Top. Curr. Chem.1983114215510.1007/BFb0111212
    [Google Scholar]
  17. WestD.B. Introduction to graph theory.2nd EditionUpper Saddle RiverPrentice Hall2001
    [Google Scholar]
  18. GutmanI. TrinajstićN. Graph theory and molecular orbitals. Total φ-electron energy of alternant hydrocarbons.Chem. Phys. Lett.197217453553810.1016/0009‑2614(72)85099‑1
    [Google Scholar]
  19. FajtlowiczS. On conjectures of graffiti, in annals of discrete mathematics 38.Elsevier1988113118
    [Google Scholar]
  20. RandicM. Characterization of molecular branching.J. Am. Chem. Soc.197597236609661510.1021/ja00856a001
    [Google Scholar]
  21. ArockiarajM. PaulD. ClementJ. TiggaS. JacobK. BalasubramanianK. Novel molecular hybrid geometric-harmonic-Zagreb degree based descriptors and their efficacy in QSPR studies of polycyclic aromatic hydrocarbons.SAR QSAR Environ. Res.202334756958910.1080/1062936X.2023.2239149 37538006
    [Google Scholar]
  22. KirmaniS.A.K. AliP. AzamF. Topological indices and QSPR/QSAR analysis of some antiviral drugs being investigated for the treatment of COVID ‐19 patients.Int. J. Quantum Chem.20211219e2659410.1002/qua.26594 33612855
    [Google Scholar]
  23. ShirakolS. KalyanshettiM. HosamaniS.M. QSPR analysis of certain distance based topological indices.Appl. Mathemat. Nonlin. Sci.20194237138610.2478/AMNS.2019.2.00032
    [Google Scholar]
  24. ShanmukhaM.C. BasavarajappaN.S. ShilpaK.C. UshaA. Degree-based topological indices on anticancer drugs with QSPR analysis.Heliyon202066e0423510.1016/j.heliyon.2020.e04235 32613116
    [Google Scholar]
  25. AdnanM. BokharyS.A.U.H. AbbasG. IqbalT. Degree-based topological indices and QSPR analysis of antituberculosis drugs.J. Chem.2022202211710.1155/2022/5748626
    [Google Scholar]
  26. HosamaniS. PerigidadD. JamagoudS. MaledY. GavadeS. QSPR analysis of certain degree based topological indices.J. Stat. Appl. Probab.20176236137110.18576/jsap/060211
    [Google Scholar]
  27. HosseiniH. ShafieiF. Quantitative structure property relationship models for the prediction of gas heat capacity of benzene derivatives using topological indices.Match201675583592
    [Google Scholar]
  28. HussainS. AfzalF. AfzalD. FarahaniM.R. CancanM. EdizS. Theoretical study of benzene ring embedded in P-type surface in 2d network using some new degree based topological indices via M-polynomial, Eurasian.Chem. Commun.202133180186
    [Google Scholar]
  29. AhmadA. On the degree based topological indices of benzene ring embedded in P-type-surface in 2D network.Hacet. J. Math. Stat.2018471918
    [Google Scholar]
  30. AhmadA. ElahiK. HasniR. NadeemM.F. Computing the degree based topological indices of line graph of benzene ring embedded in P-type-surface in 2D network.J. Inform. Optim. Sci.20194071511152810.1080/02522667.2018.1552411
    [Google Scholar]
  31. AkhterS. ImranM. On molecular topological properties of benzenoid structures.Can. J. Chem.201694868769810.1139/cjc‑2016‑0032
    [Google Scholar]
  32. NaumannU. SchenkU. Combinatorial scientific computing.CRC Press201210.1201/b11644
    [Google Scholar]
  33. KalsiP. S. Spectroscopy of organic compoundsNew age international2007
    [Google Scholar]
  34. RaufA. NaeemM. RahmanJ. SaleemA.V. QSPR study of Ve-degree based end Vertice edge entropy indices with physio-chemical properties of breast cancer drugs.Polycycl. Aromat. Compd.20234354170418310.1080/10406638.2022.2086272
    [Google Scholar]
  35. BalasubramanianK. Combinatorial and quantum techniques for large data sets: Hypercubes and halocarbons, In big data analytics in chemoinformatics and bioinformatics.Elsevier2023187217
    [Google Scholar]
  36. GovardhanS. RoyS. PrabhuS. SiddiquiM.K. Computation of neighborhood M-polynomial of three classes of polycyclic aromatic hydrocarbons.Polycycl. Aromat. Compd.20234365519553510.1080/10406638.2022.2103576
    [Google Scholar]
  37. GovardhanS. SantiagoR. Degree-sum based topological indices of supercoronene and triangle-shaped discotic graphene using NM-polynomial.Polycycl. Aromat. Compd.202444150752010.1080/10406638.2023.2177314
    [Google Scholar]
  38. GovardhanS. RoyS. BalasubramanianK. PrabhuS. Topological indices and entropies of triangular and rhomboidal tessellations of kekulenes with applications to NMR and ESR spectroscopies.J. Math. Chem.20236171477149010.1007/s10910‑023‑01465‑9
    [Google Scholar]
  39. GovardhanS. RoyS. Topological analysis of hexagonal and rectangular porous graphene with applications to predicting $${π $$-electron energy.Eur. Phys. J. Plus2023138767010.1140/epjp/s13360‑023‑04307‑4
    [Google Scholar]
  40. GovardhanS. RoyS. PrabhuS. ArulperumjothiM. Topological characterization of cove-edged graphene nanoribbons with applications to NMR spectroscopies.J. Mol. Struct.2024130313749210.1016/j.molstruc.2024.137492
    [Google Scholar]
  41. AngueraJ. PuenteC. BorjaC. SolerJ. Fractal shaped antennas: A reviewEncyclopedia of RF and microwave engineering2005
    [Google Scholar]
  42. WqrnerD.H. GangulyS. An overview of fractal antenna engineering research.IEEE Antennas Propag. Mag.2003451385710.1109/MAP.2003.1189650
    [Google Scholar]
  43. KrzysztofikW. J. BrambilaF. Fractals in antennas and metamaterials applicationsFractal analysis: Applications in physics, engineering and technology2017953978
    [Google Scholar]
  44. KarmakarA. Fractal antennas and arrays: A review and recent developments.Int. J. Microw. Wirel. Technol.202113217319710.1017/S1759078720000963
    [Google Scholar]
  45. NadeemM.F. AzeemM. FarmanI. Comparative study of topological indices for capped and uncapped carbon nanotubes.Polycycl. Aromat. Compd.20224274666468310.1080/10406638.2021.1903952
    [Google Scholar]
  46. NadeemM.F. ZafarS. ZahidZ. On topological properties of the line graphs of subdivision graphs of certain nanostructures.Appl. Math. Comput.201627312513010.1016/j.amc.2015.10.010
    [Google Scholar]
  47. UllahA. ZamanS. HussainA. JabeenA. BelayM.B. Derivation of mathematical closed form expressions for certain irregular topological indices of 2D nanotubes.Sci. Rep.20231311118710.1038/s41598‑023‑38386‑1 37433876
    [Google Scholar]
  48. HayatS. KhanA. AliK. LiuJ.B. Structure-property modeling for thermodynamic properties of benzenoid hydrocarbons by temperature-based topological indices.Ain Shams Eng. J.202415310258610.1016/j.asej.2023.102586
    [Google Scholar]
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