Single Molecule Force Spectroscopy of Asphaltene Aggregates
Bibliographic record
Abstract
Asphaltene aggregation and deposition cause severe problems in nearly all phases of petroleum processing. To resolve those problems, understanding the aggregation mechanisms is a prerequisite and has attracted the interest of a great number of investigators. However, to date, the nature and extent of asphaltene aggregation remain widely debated. In the present study, we attempt to investigate asphaltene aggregation from a completely new perspective. The technique of single molecule force spectroscopy (SMFS) was used to investigate the response of single asphaltene aggregates under an external pulling force. Force curves representing the stretching of single asphaltene aggregates were obtained in simple electrolyte solutions (KCl and calcium) and organic solvents (toluene and heptane). These force curves were well-fitted by the modified worm-like chain model, indicating that those asphaltene aggregates acted like long-chain polymers under pulling by an external force. It was found that lower solution pH values and the presence of divalent cations resulted in a lower bending rigidity of the formed aggregates. The information retrieved from the force curves suggests that asphaltene molecules with a structure featuring small aromatic clusters connected by aliphatic chains do exist and that asphaltene aggregation could occur through a linear polymerization mechanism. The current study extends the application scope of SMFS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".