Adsorption and interaction mechanisms of asphaltene subfractions on silica surfaces
Bibliographic record
Abstract
Adsorption of asphaltenes onto mineral solids contributes to fouling, scaling, and plugging issues in the oil industry. Among asphaltene subfractions, those with strong oil/water interfacial activity are expected to possess superior adsorption abilities on mineral surfaces. In this study, interfacially non-active (INAA) and active (IAA) fractions were separated from whole asphaltenes. Surface forces apparatus, atomic force microscopy imaging, quartz crystal microbalance with dissipation, and surface force measurements were employed to study adsorption behaviors and intermolecular interactions of INAA/IAA asphaltenes on silica surfaces. The results indicate that IAA asphaltenes formed larger aggregates on silica surfaces compared to INAA. After 90 min of adsorption, the thickness of adsorbed IAA asphaltene layers reached ~67 nm, considerably greater than that of INAA (~4.1 nm). The adsorption capacity (~150 mg/m 2 ) and diffusion coefficient (~10 −8 m 2 /s) of IAA asphaltenes were significantly higher than those of INAA and previously reported values for whole asphaltenes. Significant adhesion forces were measured for IAA–silica interactions, whereas negligible adhesion/cohesion was observed for INAA–silica and INAA–INAA interactions. Notably, IAA–silica and IAA–IAA interactions showed increased adhesion/cohesion with greater maximum loading forces, most likely originating from enhanced interactions such as π-π stacking, hydrogen bonding, and van der Waals forces. The strong adsorption ability of IAA asphaltenes for silica surfaces and their tendency to self-associate led to thick IAA asphaltene layers. This study provides novel insights into the adsorption mechanisms and intermolecular interactions of asphaltene subfractions, advancing the fundamental understanding of asphaltene–mineral interactions in crude oil exploitation.
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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) | 0.000 | 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) | 0.000 | 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".