Modeling and Scaling of the Viscosity of Suspensions of Asphaltene Nanoaggregates
Bibliographic record
Abstract
The scaling and modeling of the viscosity of suspensions of asphaltene nanoaggregates is carried out successfully taking into consideration the solvation and clustering of nanoaggragates, and the jamming of the suspension at the glass transition volume fraction of asphaltene nanoaggregates. The nanoaggregates of asphaltenes are modeled as solvated disk-shaped “core–shell” particles taking into account the most recent small-angle neutron scattering (SANS), small-angle X-ray scattering (SAXS), and solid-state 1H NMR studies available on the size and structure of asphaltene nanoaggregates. This work is an extension of our earlier studies on modeling of asphaltene suspensions where solvation of asphaltene nanoaggregates was not considered. A new mathematical model is developed for estimating the aspect ratio (ratio of thickness to diameter of particle) and the corresponding intrinsic viscosity of suspension of solvated disk-shaped asphaltene nanoaggregates using the experimental relative viscosity data of suspensions at low asphaltene concentrations. The solvation of asphaltene nanoaggregates is found to be significant. The intrinsic viscosity increases with the increase in the degree of solvation of nanoaggregates. At high concentrations of asphaltenes, clustering of solvated nanoaggregates dominates resulting in large viscosities. A new scaling law is discovered to scale the viscosity data of different asphaltene suspensions. According to the new scaling law, a unique correlation is obtained, independent of the type of asphaltene system, when the data are plotted as ( η r − 1 ) / [ η ] S versus ϕ S where η r is the relative viscosity of suspension, [ η ] S is the intrinsic viscosity of suspension of solvated nanoaggregates, and ϕ S is the volume fraction of solvated nanoaggregates. Twenty sets of experimental viscosity data on asphaltene suspensions gathered from different sources are used to verify and confirm the scaling law and the viscosity model proposed in this work. Based on the experimental data, the glass transition volume fraction of solvated asphaltene nanoaggregates where jamming of suspension, and hence divergence of viscosity, takes place is found to be approximately 0.4. The viscosity model proposed in this work can be used to predict the viscosity of a new asphaltene system over a broad range of asphaltene concentrations provided that the intrinsic viscosity of the suspension is obtained from viscosity measurements at very low asphaltene concentrations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".