Rheology and magnetorheology of ferrofluid emulsions: Insights into formulation and stability
Bibliographic record
Abstract
The integration of surfactants and nanoparticles in emulsion formulations has attracted significant attention due to their potential synergistic effects, improving stability and enabling the development of stimuli-responsive materials. The objective of this study was to investigate the stability, bulk rheological, and magnetorheological properties of oil in water (o/w) emulsions, composed of Fe3O4 kerosene-based ferrofluids dispersed in surfactant solutions (hexadecylpyridinium chloride, and nonylphenol polyethoxylate—ethylene oxide = 40, known as Tergitol NP-40), as a function of concentration and nature of the emulsifying agents. The results demonstrated the formation of stable systems (>2 months), featuring an average droplet size below 4 μm, with the primary stabilization mechanism attributed to the reduction of interfacial tension by surfactant activity. The emulsions exhibited shear thinning and viscoelastic solid-like behavior, which were enhanced by increasing the concentrations of both emulsifiers. Emulsions stabilized with hexadecylpyridinium exhibited a higher structural rigidity, with dynamic moduli an order of magnitude higher than Tergitol formulations. In the presence of a perpendicular magnetic field, it was demonstrated that incorporating ferrofluid as a dispersed phase in an o/w emulsion potentiates the magnetoviscous effect, compared to that observed with neat ferrofluid at the same concentration. A maximum relative increase in viscosity of up to 17-fold was observed in emulsions stabilized with 2.5 w/v% of hexadecylpyridinium and 10 000 ppm of nanoparticles when exposed to a linearly increasing magnetic field up to 796.73 mT at 1 s−1. The observed magnetoviscous effect remained reproducible for up to one year after formulation, highlighting the potential of these systems for multiple applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".