Kinetics and Thermodynamics of Asphaltene Adsorption on Metal Surfaces: A Preliminary Study
Bibliographic record
Abstract
Asphaltene deposition on pipeline surfaces during crude oil production and transportation is considered to be a key Flow Assurance issue. Asphaltene−metal surface interactions including the simplest process of asphaltene adsorption on metal surfaces remains a poorly understood topic. In this study, preliminary results on the kinetics and thermodynamics of asphaltene adsorption from toluene−heptane and toluene−pentane solutions are presented. The kinetics of asphaltene adsorption on gold surface was investigated using a quartz crystal microbalance in a flow-cell system. The kinetics of adsorption was relatively slow and did not achieve equilibrium even after 700 min. The asymptotic analyses indicate that the initial adsorption process is controlled by the diffusion of asphaltenes from the bulk solution to the adsorption surface. A thermodynamic framework to describe asphaltene adsorption on metal surfaces in terms of Lifshitz−van der Waal (LW) and acid−base (AB) free energy interactions is proposed. The LW and AB components of the surface tension parameters of asphaltenes and metal surfaces were determined from contact angle measurements. The free energy of asphaltene adsorption on metal surfaces in the presence of toluene was calculated. It is predicted that asphaltenes will adsorb preferentially in the following order Au > SS > Al.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".