On the interpretation of viscosity data of suspensions of asphaltene nano‐aggregates
Bibliographic record
Abstract
The viscous behaviour of suspensions of asphaltene nano‐aggregates is often described in terms of the Pal and Rhodes model (Pal, Rhodes, J. Rheol. 1989, 33, 1021). The model assumes particles to be spherical in shape and takes into account the solvation or hydration of particles. However, the solvation coefficient estimated from the Pal and Rhodes model for asphaltene particles is generally too high to be realistic. Furthermore, the model does not consider the packing limitations (maximum packing volume fraction) of the asphaltene particles. A large body (14 sets) of the available viscosity data for asphaltene suspensions are reinterpreted in terms of the Krieger–Dougherty model. The data analysis indicates that asphaltene nano‐aggregates are non‐spherical disk‐shaped particles with low aspect ratio (ratio of particle thickness to particles diameter). The aspect ratio depends on the nature of the asphaltene/oil system. For a given system, it increases with the increase in the temperature. Interestingly, the maximum packing volume fraction (φm) of asphaltene particles is found to be nearly constant , independent of the nature of the asphaltene/oil system and temperature. Also the values of φm predicted from the viscosity data are in reasonable agreement with the values predicted from a percolation model. Based on the analysis carried out in this work, the following model is proposed for accurate estimation and correlation of the viscosity of asphaltene suspensions: ηr = [1 − (φ/0.37)]−0.37[η], where ηr is the relative viscosity, [η] is the intrinsic viscosity and φ is the volume fraction of asphaltene particles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".