4.7 Article

Sensitivity of adhesive and cohesive intermolecular forces to the incorporation of MWCNTs into liquid paraffin: Experimental study and modeling of surface tension

期刊

JOURNAL OF MOLECULAR LIQUIDS
卷 310, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.molliq.2020.113235

关键词

Liquid paraffin; Surface tension; MWCNTs; Artificial neural network; Nonlinear regression

资金

  1. Key project of the National Social Science Fund of the year 2018 [18AJY013]
  2. 2017 National Social Science foundation project [17CJY072]
  3. 2018 Planning Project of Philosophy and Social Science of Zhejiang Province [18NDJC086YB]
  4. 2018 Fujian Social Science Planning Project [FJ2018B067]
  5. Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019 [19YJA790102]

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In this study, the effect of the presence of MWCNTs on the surface tension of paraffin-based fluid was examined. Surface tension measurements of samples containing MWCNTs (0.005-5 wt%) were performed at temperatures of 25-70 degrees C by SITA dynotester. Surface tension corresponds to the adhesive and cohesive forces resultant. Adding nanoparticles diminishes the cohesive force and simultaneously strengthens the adhesive force. Owing to the presence of MWCNTs, the effect of adhesion on the surface tension was superior to the effect of cohesion which consequently diminishes the surface tension. The loading nanoparticles efficacy on surface tension is weekend as the temperature increases. On the other hand, the presence of particles diminishes the sensitivity of surface tension to the temperature growth. In other words, temperature and mass fraction weaken each other's effects. The highest decrease in surface tension is reported to be 18.6%, which occurs at the lowest temperature as well as the maximum mass fraction. The prediction of MWCNTs/liquid paraffin surface tension behavior was performed by applying nonlinear regression and artificial neural network techniques, and statistical calculations revealed that the R-squared for the first and second techniques was calculated to be 0.99 and 0.998, respectively. Both techniques accurately predict nanofluid surface tension. (C) 2020 Published by Elsevier B.V.

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