Abstract
Asphaltenes are operationally defined as the fraction of crude oil that is soluble in toluene but insoluble in n-heptane. According to the Yen-Mullins model, typical asphaltenes are relatively small molecules consisting of a single aromatic core flanked by aliphatic chains. The Yen-Mullins model posits that asphaltene aggregation proceeds via a hierarchical mechanism involving small nanoaggregates with stacked aromatic cores surrounded by a corona of aliphatic tails. In this work, we introduce a coarse-grained (CG) model for investigating the physical picture underlying the Yen-Mullins model and, more generally, the effects of the solvent character and molecular structure upon asphaltene self-assembly. By representing proposed asphaltenes in united atom detail, this CG model accurately describes their shape and conformational properties. Conversely, the CG model mimics varying solvent conditions by modulating the effective attraction between aliphatic and aromatic groups. Given the simplicity of this model, we performed long, replicate simulations of 147 different asphaltene solutions. As proposed by the Yen-Mullins model, island-type molecules readily form stacked aggregates under conditions that promote aromatic interactions. Interestingly, the onset of nanoaggregation appears to be insensitive to the aliphatic tails, although these tails may sterically stunt further growth of nanoaggregates. Consequently, nanoaggregates form more readily and grow larger under conditions that promote both aliphatic and aromatic interactions. In contrast, archipelago-type molecules also form large aggregates, but they do not demonstrate significant stacking interactions. Thus, the CG model reasonably describes the physical intuition of the Yen-Mullins picture and may prove to be useful for exploring later stages of asphaltene aggregation.
Original language | English (US) |
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Pages (from-to) | 6111-6122 |
Number of pages | 12 |
Journal | Journal of Physical Chemistry B |
Volume | 123 |
Issue number | 28 |
DOIs | |
State | Published - Jun 24 2019 |
Bibliographical note
Funding Information:Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for the support (or the partial support) of this research. W.G.N. gratefully acknowledges the financial support of ACS PRF award 52100-ND6 and REU grant CHE-1263053. Figures 1 and 5 employed VMD.(98) VMD is developed with the NIH support by the Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois at Urbana-Champaign. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562.(99) The computational resources required for this study were also provided in part by the Penn State Institute for Cyberscience and the Minnesota Supercomputing Institute.
Funding Information:
Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for the support (or the partial support) of this research. W.G.N. gratefully acknowledges the financial support of ACS PRF award 52100-ND6 and REU grant CHE-1263053. Figures 1 and 5 employed VMD. (98) VMD is developed with the NIH support by the Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois at Urbana-Champaign. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. (99) The computational resources required for this study were also provided in part by the Penn State Institute for Cyberscience and the Minnesota Supercomputing Institute.
Publisher Copyright:
© 2019 American Chemical Society.