Bibliographic record
Abstract
Asphaltenes have received significant attention over the past decade, primarily because of their complex self-assembly behavior that results in their aggregation and deposition either in the reservoir formation or the production facilities. The aggregation and deposition of asphaltenes causes severe problems in both upstream and downstream sectors of the petroleum industry. For this reason, significant effort has been expended in shedding light on the basic molecular and colloidal properties of asphaltenes to identify the key parameters controlling their stability in the crude oil mixture. Molecular simulations provided invaluable information on the main molecular mechanisms leading to the asphaltene aggregation and also the principal intermolecular forces governing this process. However, the high computational cost of these simulation approaches did not allow the scientists to fully produce the aggregation behavior of asphaltenes in the past. In this work, we aimed at studying the asphaltene self-assembly behavior at mesoscales wherein the primary colloidal particles portray the asphaltene nanoaggregates. The Brownian dynamics (BD) simulations have been utilized to investigate the aggregation behavior of asphaltenes in different solvent environments at various volume fractions of asphaltene nanoaggregates under no- and simple shear-flow conditions. The BD simulations enabled us to access significantly larger length and time scales compared to the molecular simulations resulting in complete reproduction of asphaltene aggregation hierarchy. The effects of asphaltene volume fraction, solvent quality, and the shear rate on the kinetics of aggregation, the internal structure of the formed aggregates, and the self-diffusion coefficients of asphaltenes were also discussed.
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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 | 0.000 | 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) | 0.000 | 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".