Mesoscale computer modeling of asphaltene aggregation in liquid paraffin
Bibliographic record
Abstract
Asphaltenes represent a novel class of carbon nanofillers that are of potential interest for many applications, including polymer nanocomposites, solar cells, and domestic heat storage devices. In this work, we developed a realistic coarse-grained Martini model that was refined against the thermodynamic data extracted from atomistic simulations. This allowed us to explore the aggregation behavior of thousands of asphaltene molecules in liquid paraffin on a microsecond time scale. Our computational findings show that native asphaltenes with aliphatic side groups form small clusters that are uniformly distributed in paraffin. The chemical modification of asphaltenes via cutting off their aliphatic periphery changes their aggregation behavior: modified asphaltenes form extended stacks whose size increases with asphaltene concentration. At a certain large concentration (44 mol. %), the stacks of modified asphaltenes partly overlap, leading to the formation of large, disordered super-aggregates. Importantly, the size of such super-aggregates increases with the simulation box due to phase separation in the paraffin-asphaltene system. The mobility of native asphaltenes is systematically lower than that of their modified counterparts since the aliphatic side groups mix with paraffin chains, slowing down the diffusion of native asphaltenes. We also show that diffusion coefficients of asphaltenes are not very sensitive to the system size: enlarging the simulation box results in some increase in diffusion coefficients, with the effect being less pronounced at high asphaltene concentrations. Overall, our findings provide valuable insight into the aggregation behavior of asphaltenes on spatial and time scales that are normally beyond the scales accessible for atomistic simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".