Mechanism of YF<sub>3</sub> Nanoparticle Formation in Reverse Micelles
Bibliographic record
Abstract
This article reports an investigation of the mechanism of YF(3) nanoparticle formation in two variants of the reverse microemulsion precipitation method. These two variants involve the addition of F(-), either as a microemulsion or directly as an aqueous solution, to Y(3+) dispersed in nonionic reverse micelles. The two methods yield amorphous and single-crystal nanoparticles, respectively. The kinetics of reagent mixing are studied by (19)F NMR and colorimetric model reactions, and the particle growth is monitored by TEM. Mixing and nucleation are shown to occur within seconds to minutes whereas particle growth continues for 4 to 48 h, depending on the particle type. Moreover, the growth rate remains constant during most of the growth period, indicating that Ostwald ripening is the most probable growth mechanism. The single-emulsion method also produces a minority amorphous population that exhibits significantly different growth kinetics, attributed to a coagulation mechanism. Secondary growth experiments, involving the addition of precursor ions to mature particles, have been conducted to evaluate the relative importance of nucleation and the competitive growth of existing particle populations. The key differences between the two methods reside in the nucleation step. In the case of the classical method, nucleation occurs upon intermicellar collisions and under conditions of comparable concentrations of Y(3+) and F(-). This method generates more numerous stable nuclei and smaller particles. In the single-microemulsion method, nucleation occurs in the presence of excess F(-) through the interaction of Y(3+)-containing micelles with microdroplets of aqueous F(-). These conditions lead to the formation of crystalline particles and a wider size distribution of unstable nuclei.
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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 | 0.001 | 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 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".