Probing the Interaction Mechanism between Oil-in-Water Emulsions and Electroless Nickel–Phosphorus Coating with Implications for Antifouling in Oil Production
Bibliographic record
Abstract
Fouling issues are highly undesirable in oil industries, and stable water–oil emulsion is one of the major causes of fouling on pipelines, upgrading equipment, and other surfaces in oil production. Studying the interfacial interactions between emulsion drops and various metal substrates is of significant importance in the fundamental understanding of fouling mechanisms. In this work, surface force measurements using a drop probe atomic force microscope technique and fouling tests were applied to investigate the fouling and antifouling mechanisms of electron-beam-deposited iron substrates with and without electroless nickel–phosphorus (EN) coatings. The effects of oil or aqueous solution conditions have been systematically investigated, including the asphaltene concentration, salinity, pH, and presence of divalent ions. A theoretical model based on the Reynolds lubrication equation and augmented Young–Laplace equation has been applied to analyze the measured force profiles. Our results indicate that the attractive van der Waals force plays an important role in the fouling phenomena, particularly under high-salinity conditions, while the repulsive electric double-layer interaction contributes to the antifouling behavior. Surface force measurements and fouling tests of Fe and EN substrates in toluene-in-water emulsions clearly demonstrate the excellent performance of the EN coating. Our work provides useful insights in the fundamental understanding of fouling/antifouling mechanisms of oil-in-water emulsions on different substrates, with implications to the development of efficient antifouling coatings and strategies in oil production processes.
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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) | 0.000 | 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 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".