Partial molar volumes of model asphaltenes with varying inhibitorconcentrations
Bibliographic record
Abstract
Partial molar volume (PMV) is a crucial thermodynamic property that provides insights into solute-solvent interactions in the mixtures. In this study, linear regression was used to obtain PMVs of solution components from molecular dynamics simulations. The focus was on systems containing Violanthrone-79 (Vo-79) as a model asphaltene compound, 4-Dodecylbenzenesulfonic acid (DBSA) as a chemical inhibitor of asphaltene aggregation, and alkanes (i.e., n-heptane and n-pentane) as organic solvents. To determine the PMV of each component in the solution, a post-processing approach was employed. Specifically, snapshots from the simulation trajectories were sampled and partitioned into an array of control volumes. The amount of each component present in each control volume was then counted, and a system of equations was constructed. The solutions to this system of equations were approximated using multivariate linear regression. To investigate the effect of different aggregation configurations of asphaltene on PMV values, the concentration of DBSA in the systems was varied in this study. The results reveal that the PMVs of asphaltene and DBSA were affected by the concentration of DBSA in the system in a non-monotonic way. At low DBSA concentrations, model asphaltene molecules (VO-79) self-aggregate to form large parallel structures, resulting in smaller PMVs of asphaltene. Conversely, in the intermediate range of DBSA concentrations, fewer but more entangled VO-79 aggregates were formed, leading to larger PMVs of asphaltene. At high DBSA concentrations, the model asphaltene molecules formed small aggregates that were highly dispersed. This resulted in a decrease in PMV value that falls below the values obtained for low-concentration DBSA systems. The conducted study demonstrates that the aggregation configuration of asphaltene can significantly impact the PMV values and can help to understand the thermodynamic effects of chemical inhibitors.
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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) | 0.001 | 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".