Surface Interactions between Water-in-Oil Emulsions with Asphaltenes and Electroless Nickel–Phosphorus Coating
Bibliographic record
Abstract
Surface interactions between emulsion drops and substrate surfaces play an important role in many phenomena in industrial processes, such as fouling issues in oil production. Investigating the interaction forces between the water-in-oil emulsion drops with interfacially adsorbed asphaltenes and various substrates is of fundamental and practical importance in understanding the fouling mechanisms and developing efficient antifouling strategies. In this work, the surface interactions between water drops with asphaltenes and Fe substrates with or without an electroless nickel-phosphorus (EN) coating in organic media have been directly quantified using the atomic force microscope drop probe technique. The effects of asphaltene concentration, organic solvent type, aging time, contact time, and loading force were investigated. The results demonstrated that the adhesion between water drops and the substrates was enhanced with higher asphaltene concentration, better organic solvent to asphaltenes, longer aging time, longer contact time, and stronger loading force, which was due to the growing amount and conformational change of asphaltenes adsorbed at the water/oil interface. Meanwhile, the adhesion between the water drop and the EN substrate was much weaker than that with the Fe substrate. The bulk fouling tests also showed that EN coating had a very good antifouling performance, which was in consistence with the force measurement results. Our work sheds light on the fundamental understanding of emulsion-related fouling mechanisms in the oil industry and provides useful information for developing new coatings with antifouling performances.
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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".