Viscosities of Heavy Oils in Toluene and Partially Deasphalted Heavy Oils in Heptol in a Study of Asphaltenes Self-Interactions
Bibliographic record
Abstract
Interparticle interactions of the soluble asphaltenes in partially deasphalted heavy oils in toluene−heptane (heptol) mixtures are compared to those of several heavy oils diluted in toluene only. Viscosity−volume fraction (η−Φ) relationships for the heavy oils and bitumen in toluene were almost identical. However, the asphaltenes in toluene associated and scaled differently from its source oil. Four classical viscosity models were used to describe the data, and scaling was interpreted on the basis of asphaltenes association, as in macromolecular interactions. The Pal−Rhodes model showed deviation from sphericity with solvation constants for heavy oils in toluene and C-5 asphaltenes in toluene, at 1.4−1.6 and 3.7, respectively. The Krieger−Dougherty (K−H) model indicated high interparticle interaction factors, and maximum packing factors of ∼1 suggested polydispersity. Neither models fit the data for deasphalted oils. The Leighton−Acrivos model showed that (i) the maximum packing fraction (Φ max ) for all oils was similar, (ii) the asphaltenes alone in toluene had the highest self-associations, and (iii) the deasphalted oils showed Φ max values close to the theoretical values (0.58). From the Einstein equations, intrinsic viscosities [η] of deasphalted oils in heptol gave aspect ratios (length to radius, L / R ) of the asphaltenes at 10 (i.e., rodlike molecules). The K−H model gave [η] of ∼4 and L / R ≈ 3.5 for heavy oils in toluene; however, for asphaltenes in toluene, the model gave [η] ≈ 10.6 and L / R ≈ 5.8 (i.e., less-rodlike molecules).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".