Synthesis of Amphiphilic Mesoporous Silica Nanoparticles to Stabilize Pickering Emulsions for Enhanced Oil Recovery
Bibliographic record
Abstract
Pickering emulsions stabilized by nanoparticles offer significant potential for enhanced oil recovery (EOR). Nanoparticle morphology critically governs emulsion stability. This study successfully synthesized novel amphiphilic mesoporous silica nanoparticles (MSNs) modified with alkyl chains (propyl, hexyl, octyl; denoted MSNs-Cn, n = 3, 6, 8) via a two-step method and systematically investigated their structure–performance relationship in stabilizing Pickering emulsions for EOR. The morphology and surface properties of MSNs and MSNs-Cn were characterized by Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), zeta potential analysis, water contact angle measurements, and interfacial tensiometry. The emulsification capacity was evaluated through optical microscopy, static multiple light scattering (Turbiscan), and rotational rheometry, and it was found that MSNs modified with optimal hexyl chain grafting (MSNs-0.2C6) exhibited superior interfacial activity. Atomic force microscopy (AFM) and N 2 adsorption–desorption isotherms confirmed that enhanced surface roughness and a larger specific surface area (728.9 m 2 /g) significantly contributed to the emulsifying performance by promoting nanoparticle adsorption energy and capillary interactions at the oil–water interface. Core flooding experiments demonstrated that the MSNs-0.2C6-stabilized emulsion exhibited excellent EOR performance, with an increase in oil recovery of up to 19.6%. This study revealed the correlation and mechanism between the porous morphological characteristics of mesoporous silica nanoparticles and their interfacial activity, established a relevant theoretical model, and thereby promoted the development of nanoparticles in the field of EOR.
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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".