Solvent Nuances Modulate the Decreasing Effect of a Model Asphaltene on Interfacial Tension
Bibliographic record
Abstract
In this work, a series of molecular dynamics simulations was performed to investigate the role of organic solvents in modulating the decreasing effect of a model asphaltene on interfacial tension (IFT). Pentane, pentol with a volume ratio of 50:50 (pentane/toluene), heptane, and heptol with a volume ratio of 56:44 (heptane/toluene) were adopted as the organic solvents; violanthrone-79 (VO-79), a widely tested compound, was chosen as the representative model for asphaltene. Our simulations revealed that, while VO-79 can reduce solvent/water IFT, the reduction magnitude of pentane/water IFT is smaller than that of heptane/water IFT. That is, despite the fact that the pentane/water IFT is lower than that of the heptane/water interface without VO-79, the presence of VO-79 reverses this trend, resulting in higher pentane/water IFT values compared to those of the heptane/water interfaces. Similar observations hold for the comparison between pentol/water and heptol/water IFT values. Detailed analysis on the interfacial and bulk properties of VO-79 suggests that, depending on the nature of the solvents, distinct mechanisms exist that are responsible for these observations. Pentane and heptane are poor solvents for VO-79, and compared to heptane, pentane can introduce even more unfavored solvation. That is, a stronger self-aggregation of VO-79 is observed at pentane/water interfaces, which could decrease their mobility and thus entropy, and ultimately reduce the ability to decrease solvent/water IFT. Contrarily, due to the presence of toluene in pentol and heptol, the migration of VO-79 into an organic solvent phase also affects its ability to decrease solvent/water IFT. Our results here demonstrate that solvent nuances can modulate the decreasing effect of model asphaltenes on IFT and suggest that precaution should be taken when using IFT reduction as an indicator for asphaltene adsorption extent.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".