Adsorption Kinetics of Asphaltenes at the Heptol–Water Interface
Bibliographic record
Abstract
The dynamic pendant drop method was employed to better understand the adsorption kinetics at the oil/water interface. Various volume ratios of n -heptane/toluene (heptol) have been chosen as solvents to represent the model of crude oil systems. The results show that an increase in the asphaltene concentration reduces the interfacial tension and this reduction is much more pronounced at higher fractions of n -heptane. The Ward–Tordai short-time model was used to estimate the diffusion coefficient of asphaltenes for dilute solutions of asphaltenes in heptol (up to 0.01 wt %). The results show that the asphaltene adsorbed as monomers onto the interface and the bulk nanoaggregates do not contribute to the adsorption process. Furthermore, the results reveal that the asphaltene diffusion coefficient decreases with increasing its concentration and decreasing solvent aliphaticity. The Langmuir adsorption isotherm was used to study the asphaltene behavior at the oil/water interface and to predict the parameters of adsorption kinetics. It was shown that asphaltenes were adsorbed as monomers onto the interface. The results show that at higher n -heptane fractions, more asphaltene molecules are adsorbed onto the interface. A higher concentration of n -heptane in solvent results in faster and more asphaltene adsorption on the oil/water interface leading to lower interfacial tension and thus promotes emulsification. These findings improve our understanding of adsorption kinetics of asphaltenes at the oil/water interface and find applications in oil/water separation and solvent-aided recovery of heavy oil and bitumen.
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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 | 0.000 |
| 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".