Electric field-induced deformation and breakup of water droplets in polymer-flooding W/O emulsions: A simulation study
Bibliographic record
Abstract
Polymers have a significant impact on the electrostatic demulsification efficiency of water-in-oil (W/O) emulsions by altering the motion characteristics of water droplets. To uncover the underlying micro-mechanism behind electric field-induced deformation and breakup of polymer-containing water droplets, we employed the molecular dynamics (MD) method to investigate the effect of different polymer concentrations ( c p ) on droplet motion under a DC electric field. The simulation results indicated that polyacrylamide (PAM) molecules possess a prominent electrostatic potential (ESP), resulting in a strong electrostatic attraction between PAM and water. The interface film of the droplet was found to exhibits high strength due to the formation of hydrogen bonds with a lifetime of 1 ps between polymers. The addition of polymers increased the polarity of the droplets, thereby promoting their deformation and breakup under an electric field. Both polarized polymers and ions migrated by carrying water molecules, which ultimately led to droplet breakup. At c p = 0.0221 mol·L −1 , the critical field strength ( E c ) for droplet breakup was the lowest, at only 0.672 V·nm −1 . As c p > 0.0221 mol·L −1 , the steric hindrance of PAM molecules and the strong interfacial film impeded the breakup of the droplets. These findings provided a theoretical basis for enhancing the electrostatic demulsification efficiency of polymer-flooding crude oil.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".