Interfacial adsorption characteristics of surfactant‐modified nanoparticles: Equilibrium and dynamic effects of salinity at the oil–water interface
Bibliographic record
Abstract
Abstract This study systematically investigated the interfacial behaviour of silicon dioxide (SiO 2 ) and aluminium oxide (Al 2 O 3 ) nanoparticles (NPs) functionalized with cationic (CTAB), anionic (SDS), and nonionic (Triton X‐100) surfactants under varying MgCl 2 salinities. Surface charge and wettability analyses revealed that CTAB–SiO 2 exhibited the most pronounced hydrophobization (contact angle up to 119°) and highest adsorption affinity, whereas SDS‐modified NPs showed stronger aggregation tendencies at elevated salinity due to electrostatic attraction with Mg 2+ . Adsorption isotherms followed Langmuir characteristics, confirming monolayer coverage with additional heterogeneity for CTAB–SiO 2 and Triton X‐100 systems. Equilibrium interfacial tension (IFT) consistently reached a minimum at 25,000 ppm, reflecting optimized interfacial packing, while dynamic IFT profiles showed accelerated relaxation in positively charged systems and retarded kinetics in negatively charged ones. These findings provide direct mechanistic evidence of how surfactant type and NP surface chemistry jointly dictate salinity‐dependent interfacial activity, offering a novel framework for tailoring nanofluid interfaces.
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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".