A Novel Approach for the Preparation of AgBr Nanoparticles from Their Bulk Solid Precursor Using CTAB Microemulsions
Bibliographic record
Abstract
Microemulsions are suitable reaction media to prepare a wide variety of nanoparticles and provide control over their sizes. However, as typically used, microemulsions limit rates of rapid reactions and suffer from low reactant solubilization capacity. This work presents a new application of a novel approach aimed at minimizing these limitations. This approach, which was previously applied for AgCl nanoparticle preparation, involves solubilization of a bulk silver halide in the form of higher halides, by means of reaction with the surfactant counterion of a microemulsion, and the reprecipitation of silver halide nanoparticles in the water pools of individual reverse micelles. CTAB microemulsions were employed because they possess a reactive counterion and are known to have a high solubilization capacity for ionic reactants. Despite their high solubilization capacity, CTAB microemulsions achieved lower nanoparticles uptake (molar concentration of the colloidal nanoparticles) for the same surfactant concentration when compared to our previous study. The effect of the following variables on the nanoparticle uptake and the particle size was investigated: (1) operation variables, including rate of mixing and temperature; and (2) microemulsion variables, including CTAB and n-butanol concentrations, and water-to-surfactant mole ratio, R. These variables provide a comprehensive test to the proposed mechanism and expose the role of the surfactant layer rigidity. The nanoparticle uptake increased as the rate of mixing, temperature, and CTAB concentration increased, and decreased as n-butanol concentration and R increased. High n-butanol concentration and R values reduced the effective surfactant concentration and contributed to less surfactant layer rigidity and to particle aggregation.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".