Suspensions of Iron Oxide Nanoparticles Stabilized by Anionic Surfactants
Bibliographic record
Abstract
Abstract Two common anionic surfactants, sodium oleate (SO) and sodium dodecyl benzene sulfonate (SDBS) were used to re‐suspend iron oxide nanoparticles in aqueous solutions. At certain SO concentrations, the SO formulations produced highly stable suspensions. In contrast, SDBS‐stabilized nanoparticles exhibited poor stability at all concentrations. The adsorption isotherm of SO on iron oxide nanoparticles revealed that stable suspensions were obtained when the equilibrium SO concentration (after adsorption) reached its critical micelle concentration (CMC). At this “optimal” condition, the maximum SO adsorption was reached, and the zeta‐potential of the particles was highly negative (∼ −50 mV). According to the SO isotherm, this optimal formulation coincided with the formation of a highly compact SO bilayer. The SDBS isotherm, on the other hand, revealed that SDBS is not strongly adsorbed on the surface of iron oxide nanoparticles and that is likely that a patchy, loosely packed bilayer, is formed on the surface of the iron oxide nanoparticles when the equilibrium SDBS concentration reaches its CMC. The DLVO theory confirmed the connection between formulation conditions and the corresponding stability. This works confirmed that the formation of a surfactant bilayer is an important element in producing stable nanoparticle suspensions with anionic surfactants. It was also confirmed that for anionic surfactants, electrostatic repulsions are an important factor in establishing an energy barrier against flocculation. This work also introduced two more elements into the design of nanoparticle suspensions. The first element is that, in order to ensure the best possible dispersion, the surfactant concentration in solution at equilibrium with the adsorbed surfactant should be close or slightly above its CMC. The second element is that the molecular structure of the surfactant should facilitate the formation of closely packed bilayers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".