Scientific Reports (May 2021)

Generation and properties of the new asphalt binder model using molecular dynamics (MD)

  • Hui Yao,
  • Junfu Liu,
  • Mei Xu,
  • Andreas Bick,
  • Qing Xu,
  • Jinxi Zhang

DOI
https://doi.org/10.1038/s41598-021-89339-5
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 15

Abstract

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Abstract Asphalt binder is the main material for road pavement and building construction. It is a complex mixture composed of a large number of hydrocarbons with different molecular weights. The study of asphalt binders and asphalt concretes from a molecular perspective is an important means to understand the intricate properties of asphalt. Molecular dynamics simulation is based on Newton’s law and predicts the microscopic performance of materials by calculating the intra- and intermolecular interactions. The asphalt binder can be divided into four components: saturates, aromatics, resins, and asphaltenes (SARA). A new molecular model of asphalt was proposed and verified in this study. Eight molecules selected from the literature were used to represent the four components of asphalt. The AMBER Cornell Extension Force Field was applied in this study to model building and the calculation of properties. The density of the asphalt model was calculated and compared with experimental results for validity verifications. The results show that the purposed model can be used to calculate the microscopic properties of the asphalt binder because the density of the model is close to the real value in the field. Besides, the proportions of different molecules in the model were adjusted to predict the relationship between the asphalt binder density and the hydrocarbon ratios and heteroatom contents of the molecular model. Moreover, the glass transition temperature of the asphalt binder model is predicted by the simulation of the heating process. The range of the glass transition temperature is determined by calculating the relationship between specific volume and temperature, and the calculated range is close to the experimental value.