Probing the interactions between asphaltenes and a PEO-PPO demulsifier at oil–water interface: Effect of temperature
Bibliographic record
Abstract
• PEO-PPO type EB exhibits a superior interfacial activity than asphaltenes. • The EB disrupts interfacial asphaltene films to render droplet coalescence. • The EB separates W/O emulsions more efficiently at high temperatures. • High temperature promotes the adsorption kinetics of both EB and asphaltenes. • High temperature facilitates the destruction of asphaltene films by EB molecules. Asphaltenes are primary stabilizers in water-in-oil (W/O) emulsions that cause corrosion and fouling issues. In oil sands industry, oil/water separation processes are generally conducted at high temperatures. A high temperature is expected to impact the interactions between asphaltenes and emulsion breakers (EBs), consequently influencing demulsification performance. The adsorption and interactions of asphaltenes and a PEO-PPO type EB (Pluronic F68) at the oil–water interface were investigated at various temperatures, using tensiometer, quartz crystal microbalance with energy dissipation (QCM-D), and atomic force microscopy (AFM). The effect of temperature on EB’s demulsification performance was explored through bottle tests. Additionally, demulsification mechanisms were studied using direct force measurements with the droplet probe AFM technique. Dynamic interfacial tension and QCM-D results demonstrate that the PEO-PPO type EB exhibits higher interfacial activity than asphaltenes and can disrupt rigid asphaltene films at the oil–water interfaces. Elevated temperatures accelerate the displacement of adsorbed asphaltenes by EB molecules, leading to sparse interfacial films, rapid droplet coalescence, and improved demulsification efficiency (supported by AFM and bottle test results). This work provides valuable insights into interfacial interactions between asphaltenes and EB at different temperatures, enhancing the understanding of demulsification mechanisms and offering useful implications for the development of efficient EBs to enhance oil/water separation performance.
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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 | 0.001 | 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) | 0.000 | 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".