Molecular-Scale Insights into the Aqueous Dispersion and Water–Oil Interfacial Behavior of Surfactant Functionalized Silica Nanoparticles
Bibliographic record
Abstract
Studying nanoparticle dispersion in water and their behavior at fluid interfaces is crucial for optimizing their performance in chemical enhanced oil recovery. Stable aqueous dispersion ensures efficient nanoparticle transport, while strong water–oil interfacial affinity promotes effective interaction with reservoir fluids. However, current understanding is limited by insufficient molecular-level insight into how surfactant type, surfactant coverage, and reservoir conditions such as pH regulate nanoparticle stability and interfacial behavior. This study employs molecular dynamics simulations to investigate the dispersity and interfacial properties of silica nanoparticles functionalized with various surfactants─anionic, cationic, zwitterionic, and nonionic─across surfactant coverages ranging from 0 to 4.6 molecules nm –2 . The impact of water pH, modeled via silica surface charge densities representing alkaline (−0.85 e nm –2 ) and acidic (0 e nm –2 ) environments, is also examined. Nanoparticle dispersity is evaluated qualitatively through equilibrium distributions and quantitatively via potential of mean force profiles as a function of center-of-mass distance. Results reveal that sufficient surfactant coverage is critical for maintaining good dispersity. At high coverage, anionic and zwitterionic nanoparticles remain well dispersed regardless of pH, whereas cationic nanoparticles exhibit diminished dispersity under alkaline conditions. Nonionic nanoparticles show poor dispersity across all coverages and pH values. All nanoparticle types exert minimal influence on the water–oil interfacial tension, and possible reasons for this behavior are discussed. However, their interfacial affinities differ: anionic nanoparticles display moderately strong adsorption at the interface, while zwitterionic nanoparticles are nearly excluded from it. These findings highlight the importance of surfactant coverage and surfactant type in optimizing nanoparticle performance. Anionic nanoparticles, in particular, exhibit robust resistance to pH variations and moderate water–oil interfacial affinity, highlighting their broad applicability for enhanced oil recovery.
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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) | 0.001 | 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".