Effects of Salt and Surfactant on Interfacial Characteristics of Water/Oil Systems: Molecular Dynamic Simulations and Dissipative Particle Dynamics
Bibliographic record
Abstract
Multiphase systems and their behaviors/characteristics appear to be crucial in a variety of industries such as the oil and gas sector, pharmaceutical industry, and food industry. In this paper, the mesoscale simulation method is used to predict the interfacial behaviors of the water/oil systems at different temperatures and salt concentrations in the presence of a nonionic surfactant (hexaethylene glycol monododecyl ether). Dissipative particle dynamics (DPD) is employed to model the interfacial properties (e.g., interfacial density and interfacial tension) and structural properties such as the radius of gyration as a function of water/oil ratio, surfactant concentration, temperature, and salinity of oil/surfactant/water mixtures. Molecular dynamics (MD) simulations are carried out to estimate the Flory–Huggins chi parameter by means of temperature-dependent solubility parameter and cohesive energy calculations using Monte Carlo (MC) method, which is then utilized as an input for the DPD approach. The DPD repulsive interaction parameter (aij) is also obtained from the dependence of chi parameter to temperature using MD simulations. Both the density profiles and simulation snapshots indicate a well-defined interface between water and oil phases, where the thickness of the layer increases with increasing the surfactant concentration and the peak of density becomes higher accordingly. It is found that the radius of gyration is a weak function of salinity; however, it increases with an increase in the surfactant concentration, revealing that the surfactant molecules become more stretched at the interface. By increasing the water content or water/oil ratio (WC), the interfacial tension increases to reach a maximum value. After the maximum interfacial tension, increasing the water/oil ratio lowers this important parameter. According to the results of the MD simulations, the presence of salt improves the interfacial efficiency of the surfactant by decreasing the interfacial tension, which is in a good agreement with the literature data. Integrating the micro- and mesoscale modeling through chi parameter determination improves the accuracy of the calculations. This integration also decreases the calculation time (and costs). Employing the integrated modeling approach, the dynamic performance of the targeted systems can be thus well-reproduced with respect to the results reported in the literature. This research work offers useful tips for surfactant selection as well as important results and information on the interactions of molecules at water/oil interface, which are central to analyze emulsion stability at different process and thermodynamic conditions.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".