Formation of Silver Chloride Nanoparticles in Microemulsions by Direct Precipitation with the Surfactant Counterion
Bibliographic record
Abstract
Nanoparticles of silver chloride were prepared by direct precipitation of silver ions with the surfactant counterion in the water pools of microemulsions formed by dioctyldimethylammonium chloride in an organic n -decanol/isooctane phase. This work represents a new concept to form nanoprecipitates using a single reverse micellar system. The net result is a fast reaction with less dependency on the intermicellar exchange of solubilizate. The effects of the surfactant and cosurfactant concentrations, of the mole ratio of water to surfactant, R, and of the loading of silver nitrate were evaluated. Increasing the surfactant concentration at fixed values of R and moles of silver nitrate resulted in a higher dependency on the reverse micellar exchange dynamics and increased the particle size. At high n -decanol concentration, the particle size increased due to decreasing the interaction between the nanoparticles and the stabilizing surfactant layer. Similar results were found at high values of R . Increasing the amount of silver nitrate resulted in the formation of more nuclei, and hence in the production of smaller particles. The trends in the particle size and the size distribution were followed using UV spectrophotometry and transmission electron microscope photographs.
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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 | 0.000 | 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 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".