Study and Modeling of Iron Hydroxide Nanoparticle Uptake by AOT (w/o) Microemulsions
Bibliographic record
Abstract
Control over nanoparticle size is a key factor which labels a given preparation technique successful. When organic reactions are mediated by ultradispersed catalysts, the concentration of the colloidal nanoparticle catalysts and their stability become key factors as well. In this study, variables affecting iron hydroxide nanoparticle size, stability, and maximum possible colloidal concentration in AOT/water/isooctane microemulsions were investigated. Iron hydroxide was prepared in single microemulsions by first solubilizing iron chloride powder in the water pools, followed by addition of aqueous NaOH. Upon addition of NaOH, Fe(OH)3 nanoparticles stabilized in the water pools formed in addition to bulk precipitate of Fe(OH)3. The time-invariant concentration of the stabilized Fe(OH)3 is defined as the nanoparticle uptake, and it corresponds to the maximum possible concentration of the colloidal nanoparticles. The effect of the following variables on the nanoparticle uptake and size distribution was investigated: mixing time; surfactant concentration; water to surfactant mole ratio; and the initial concentration of the precursor salt. At 300 rpm of mixing a constant uptake of iron hydroxide nanoparticles was achieved in about 2 h and further mixing had limited effect on the nanoparticle uptake and particle size. An optimum R was found for which a maximum nanoparticle uptake was obtained. Nanoparticle uptake increased linearly with the surfactant concentration and displayed a power function with the initial concentrations of the precursor salt. The surface area/g of the nanoparticles was much higher than literature values, however, following a trend opposite to that of the nanoparticle uptake. The surface area/unit volume of the microemulsion, on the other hand, followed the same trend as the nanoparticle uptake. The particle size increased as R and/or the surfactant concentration increased. A mathematical model based on correlations for water uptake by Winsor type II microemulsions accurately accounted for the effect of the aforementioned variables on the nanoparticle uptake.
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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 | 0.001 |
| 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 | 0.001 | 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".