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Area of Science:

  • Computational chemistry and materials science.
  • Application of graph theory and quantitative structure-property relationship (QSPR) modeling.
  • Nanomaterials characterization and comparative analysis.

Background:

  • Nanotubes exhibit unique nanostructures and properties, making them crucial in various scientific fields.
  • Physio-chemical properties of nanotubes are vital for their applications.
  • Predictive modeling using topological descriptors is a key approach in nanotube research.

Purpose of the Study:

  • To characterize and rank five types of nanotubes: carbon, naphthalene, boron nitride, V-phenylene, and titania.
  • To implement and compare the effectiveness of MCDM techniques (TOPSIS, COPRAS, VIKOR) for nanotube characterization.
  • To establish criteria for ranking based on topological descriptors and physio-chemical properties.

Main Methods:

  • Utilizing chemical graph theory to derive topological descriptors.
  • Employing quantitative structure-property relationship (QSPR) modeling to link descriptors with physio-chemical properties.
  • Applying MCDM techniques: TOPSIS, COPRAS, and VIKOR for comparative analysis and ranking.
  • Performing multiple linear regression to establish relationships between descriptors and properties.

Main Results:

  • Successful characterization and ranking of the studied nanotubes using MCDM methods.
  • Comparative analysis of TOPSIS, COPRAS, and VIKOR revealed their applicability in nanotube property assessment.
  • Identification of key topological descriptors influencing nanotube physio-chemical properties.

Conclusions:

  • MCDM techniques provide a robust framework for evaluating and ranking nanotubes based on their properties.
  • The study demonstrates the utility of chemical graph theory and QSPR in understanding nanotube behavior.
  • This research offers valuable insights for selecting and designing nanotubes for specific applications.