Literature DB >> 24817664

Prediction on miscibility of silicone and gasoline components by Monte Carlo simulation.

Qingyin Li1, Dong Liu, Linhua Song, Pingping Wu, Zifeng Yan.   

Abstract

The miscibility behavior between silicone materials and mixed gasoline components was explored via Monte Carlo simulation. The variation of energy of mixing and Gibbs energy of mixing between silicone and gasoline components shifted with temperature was calculated. The findings indicated that the miscibility of gasoline components was exceptional in silicone 2 and the selectivity of thiophene was superior to that of other silicones, which resulted from the ester groups and methyl side chains. The density of methyl side chains in silicone 2 was significantly higher than other silicones; therefore, it could explain the lower energy of mixing and higher selectivity concerning silicone 2 and thiophene. The energy of mixing between silicone 2 and gasoline components declined with the increasing temperature (300-500 K). Nevertheless, the more increased the temperature, the more decreased the selectivity of thiophene. At 350 K, it was essential for us to research the miscibility between silicone 2 and gasoline components further. The value of Gibbs energy of mixing for silicone 2 was considerably smaller than that of the hydrocarbons at 350 K. It could be demonstrated that the miscibility between silicone 2 and thiophene was better than that of hydrocarbons. Accordingly, we should attach importance to silicone 2 in the gasoline desulfurization process.

Entities:  

Year:  2014        PMID: 24817664     DOI: 10.1007/s00894-014-2244-2

Source DB:  PubMed          Journal:  J Mol Model        ISSN: 0948-5023            Impact factor:   1.810


  5 in total

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4.  AIPAR: ab initio parametrization of intermolecular potentials for computer simulations.

Authors:  Marcelo Z Hernandes; Ricardo L Longo
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5.  Can all nitrogen-doped defects improve the performance of graphene anode materials for lithium-ion batteries?

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Journal:  Phys Chem Chem Phys       Date:  2013-09-04       Impact factor: 3.676

  5 in total
  2 in total

1.  Thermodynamic analysis of fuels in gas phase: ethanol, gasoline and ethanol - gasoline predicted by DFT method.

Authors:  A F G Neto; F S Lopes; E V Carvalho; M N Huda; A M J C Neto; N T Machado
Journal:  J Mol Model       Date:  2015-09-19       Impact factor: 1.810

2.  Thermodynamic DFT analysis of natural gas.

Authors:  Abel F G Neto; Muhammad N Huda; Francisco C Marques; Rosivaldo S Borges; Antonio M J C Neto
Journal:  J Mol Model       Date:  2017-07-14       Impact factor: 1.810

  2 in total

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