Probing the interfacial behaviors of interfacially active and non-active asphaltenes and their impact on emulsion stability
Bibliographic record
Abstract
HYPOTHESIS: Asphaltenes subfractions with distinct interfacial behaviors may play different roles in stabilizing oil-water emulsions. EXPERIMENTS: In this work, whole asphaltenes were separated into interfacially active asphaltenes (IAA) and interfacially non-active asphaltenes (INAA). Employing advanced nanomechanical techniques, we have explored the compositions, morphologies, sizes, adsorption, and interfacial behaviors of IAA and INAA. FINDINGS: IAA exhibits a high and unevenly distributed oxygen content, distinguishing it from INAA. In toluene, the diameters of IAA and INAA are about 60 nm and 6 nm, respectively. When adsorbed irreversibly on mica surfaces, the thickness of the IAA and INAA film was measured at ∼5.5 nm or 1 nm, respectively; while in a toluene solution, the film thickness reached ∼46 nm and 3.1 nm for IAA and INAA, respectively. IAA demonstrates superior interfacial activity, and elastic/viscous moduli compared to INAA at the water-toluene interface. Quantified surface force measurements reveal that IAA stabilizes water droplets in toluene at a concentration of only 10 mg/L, while INAA requires a higher concentration of 100 mg/L. This work provides the first comprehensive investigation into the adsorption and interfacial behaviors of asphaltene subfractions and provides useful insights into the asphaltenes-stabilization mechanism of emulsions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".