Application of nano-quantitative structure–property relationship paradigm to develop predictive models for thermal conductivity of metal oxide-based ethylene glycol nanofluids

  • Kimia Jafari
  • Mohammad Hossein FatemiEmail author


In this work, the relatively thermal conductivity of metal oxide-based ethylene glycol nanofluids is being predicted by using quantitative structure–property relationship methodology. The structural features of studied nanoparticles are represented by quasi-SMILES which is a coded linear structure. The gathered dataset includes ten types of nanoparticles (including Al2O3, MgO, TiO2, ZnO, Co3O4, CeO2, CuO, Fe2O3, Fe3O4, and SnO2) suspended in the same base fluid, ethylene glycol. The calculated optimal descriptors acquired by applying the Monte Carlo method in the free software available on the Web (named CORAL) and four random splits into the training, invisible, calibration, and validation sets were appraised. The statistical characteristics confirmed the predictive power and reliability of the developed models; all splits had \( \overline{{R_{\text{m}}^{2} }} \) more than 0.5 and \( \Delta R_{\text{m}}^{2} \) less than 0.2, and also the validation set showed the correlation coefficient (R2) in ranges 0.8611–0.6816 and cross-validated correlation coefficient (Q2) in ranges 0.8518–0.6668. The presented models accurately predicted the thermal conductivity of all considered nanofluids, and the technique is expected to provide a novel way for future theoretical projects.


Nanofluids Thermal conductivity Nano-QSPR CORAL Quasi-SMILES Molecular features 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10973_2019_9215_MOESM1_ESM.docx (113 kb)
Supplementary material 1 (DOCX 112 kb)


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2020

Authors and Affiliations

  1. 1.Chemometrics Laboratory, Faculty of ChemistryUniversity of MazandaranBabolsarIran

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