A mesoscopic numerical study of shear flow effects on asphaltene self-assembly behavior in organic solvents
Bibliographic record
Abstract
A significant amount of research work has been conducted to shed light on the asphaltene aggregation behavior under no-flow conditions. However, their aggregation under shear flow conditions is poorly understood mainly due to the lack of research studies performed on this subject. In this work, we employ the Brownian dynamics simulation to examine the shear flow effects on the self-assembly behavior of asphaltenes. Three volume fractions φ of asphaltene nanoaggregates, ranging from 1 to 7%, are used to investigate the asphaltene aggregation behavior in heptane and heptol (i.e., a solvent containing both heptane and toluene) solvents under shear rates of [small gamma, Greek, dot above] = 0.0-2.5 × 108 s-1. The shear is applied parallel to the x-axis and the shear-gradient is along the y-axis. Under shear flow conditions, the formation of the percolating networks of aggregates is triggered at φ = 3% which is lower than that under the no-flow conditions, i.e., φ = 7%. In both solvent systems, the formed networks mainly percolate along the x- or z-axis to experience less shear-gradient. At all volume fractions, an increase in the shear rate from [small gamma, Greek, dot above] = 0.0 to [small gamma, Greek, dot above] = 2.5 × 108 s-1 resulted in two to three orders of magnitude improvement in the self-diffusion coefficients of colloids.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".