Interactions of Asphaltene Subfractions in Organic Media of Varying Aromaticity
Bibliographic record
Abstract
Whole asphaltenes were fractionated by extended-saturates, aromatics, resins, and asphaltenes (E-SARA) analysis into four asphaltene subfractions: toluene-extracted interfacially active asphaltenes (T-IAA), toluene-extracted remaining asphaltenes (T-RA), Heptol 50/50-extracted interfacially active asphaltenes (HT-IAA), and Heptol 50/50-extracted remaining asphaltenes (HT-RA). The aggregation kinetics of fractionated asphaltenes measured by dynamic light scattering (DLS) showed that decreasing solvent aromaticity promoted asphaltene aggregation for all subfractions. In a given solvent, T-IAA exhibited the strongest aggregation tendency, followed by HT-IAA, then T-RA, and HT-RA. Such differences were attributed to the higher oxygen and sulfur contents (highlighted in sulfoxide content) in IAA subfractions than RA subfractions, as confirmed by elemental analysis and X-ray photoelectron spectroscopy (XPS). The interaction forces between immobilized fractionated asphaltenes were measured using an atomic force microscope (AFM) to obtain a fundamental understanding of asphaltene interactions in organic media of varying aromaticity. The results showed that decreasing solvent aromaticity reduced steric repulsion and increased adhesion between asphaltenes with asphaltenes adopting a more compressed conformation. IAA subfractions, in particular T-IAA, exhibited higher adhesion forces than RA subfractions during separation of two asphaltene films in contact. The results of AFM colloidal force measurements were in good agreement with the DLS data. In spite of the small sulfoxide content in asphaltenes, the sulfoxide groups are believed to play a critical role in enhancing asphaltene aggregation in the bulk oil phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".