PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 30, 2026The Journal of Physical Chemistry A1 citations

Prediction of Molecular Structure of Asphaltenes in Heavy Oils

View Full Paper
ZYZhihao YuanZJZhaoli JiangSYShiling Yuan

Key Points

  • The aim is to predict the molecular structures of asphaltenes in heavy oils using advanced computational methods.
  • Constructed 5000 candidate asphaltene structures via Monte Carlo sampling and aromatic building blocks.
  • Employed nonlinear optimization with a least-squares objective function to identify dominant structures and their molar fractions.
  • Analyzed asphaltene-toluene systems through radial distribution functions and dihedral angle distributions.
  • PMSA-generated molecules exhibited stable π-π stacked aggregates with an aggregation free energy of approximately 4-6 kcal/mol.
  • Dominant molecular structures align with characteristic features of asphaltenes, confirming the method's reliability.
  • The approach provides robust theoretical support for future applications in heavy oil research.

Abstract

H NMR spectra. Based on these parameters, 5000 candidate asphaltene structures were constructed by integrating Monte Carlo sampling with a pre-established library of aromatic building blocks. Nonlinear optimization with a least-squares objective function was then employed to screen out 4-6 dominant molecular structures and determine their respective molar fractions in the sample. To validate the predicted structures, asphaltene-toluene systems were established, and analyses of radial distribution functions (RDF) and dihedral angle distributions between aromatic planes were conducted. The results showed that PMSA-generated molecules can form stable π-π stacked aggregates, exhibiting an aggregation free energy of approximately 4-6 kcal/mol, which aligns well with the characteristic structural features of asphaltenes and provides robust theoretical support for the reliability and applicability of the PMSA program.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Yuan et al. (2026) studied this question.

synapsesocial.com/papers/69f2f0e31e5f7920c6386eb5https://doi.org/10.1021/acs.jpca.6c00715
Ask AI
Helpful
Bookmark
Share
View Full Paper