Cluster of Asphaltene Nanoaggregates by DC Conductivity and Centrifugation
Bibliographic record
Abstract
A model of the dominant molecular and stable colloidal structures of asphaltenes has been proposed, the Yen–Mullins model. The formation of clusters of asphaltene nanoaggregates in toluene was reported elsewhere to occur at a concentration of a few grams per liter with a cluster aggregation number of approximately 8 (Mullins, O. C. Energy Fuels 2010, 24, 2179−2207). Here, we measure the DC-conductivity signal of toluene as a function of asphaltene concentration obtaining support for the critical clustering concentration (CCC) of a roughly 1.7 g/L in toluene. In addition, the small change in the Stokes drag at the CCC indicates that the cluster aggregation number is small, less than 10. The temperature variation of the CCC is measured to be small and within error, suggesting that cluster formation is entropically driven. Centrifugation experiments were also performed on asphaltene–toluene solutions at different concentrations. These experiments confirmed that a significant change in asphaltene aggregation occurs in the concentration range of the CCC. The CCC values from centrifugation and DC-conductivity measurements are roughly the same. The centrifugation experiments confirm a cluster size of ∼5 nm corroborating the small aggregation number found in the DC-conductivity experiments. These results add to the growing body of literature validating the Yen–Mullins model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".