Influence of CTAB-Grafted Faujasite Nanoparticles on the Dynamic Interfacial Tension of Oil/Water Systems
Bibliographic record
Abstract
In this study, the dynamic IFT and dilational rheological behavior of fluids/nanofluids generated from cetyltrimethylammonium bromide (CTAB)-grafted faujasite (FAU)-based nanoparticles were investigated in toluene–fluid/nanofluid interfaces under various conditions. To generate each nanofluid, FAU nanoparticles were initially synthesized under ambient conditions and then grafted with various quantities of CTAB surfactants. Afterward, the surface properties (i.e., morphology, surface area, functionality, and stability) of the synthesized nanoparticles before and after grafting with a low concentration of CTAB molecules were investigated by an array of characterization methods (XRD, HRTEM, BET surface area, ζ-potential, and DLS). The results showed that our generated FAU-based nanofluids outperformed the nongrafted FAU and physically mixed CTAB with FAU-based nanofluids in terms of IFT reduction. Our experimental IFT data were fitted with a general diffusion model that allowed us to determine the oil–water (o/w) emulsification mechanism due to the presence of CTAB-grafted nanoparticles. Based on our investigation, we emphasized that the IFT reduction was limited with the loading of a substantial amount of surfactant molecules, indicating that bulky nanoparticles are adsorbed slowly on the o/w interface. On the other hand, increasing the salinity level by introducing an additional amount of NaCl led to enhancing the levels of IFT reduction since the dissociated ions forced more surfactant molecules to migrate toward the o/w interface. A considerable viscoelasticity enhancement was remarked in the presence of our CTAB-grafted nanofluids, indicating that the presence of FAU nanoparticles in the core of the grafted nanoparticles robustly allowed the adsorption of higher amounts of CTAB molecules at the interface with less aggregation. The findings of this study shed light on the applications of nanofluids generated from grafted nanoparticles at the o/w systems, which can significantly increase the oil production at the end of water flooding with more efficient emulsification with surfactant concentration lower than the critical micelle concentration (CMC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".