Influence of Surfactant Additives on Photochemical Synthesized Silver Nanoparticles using UV Pulsed Laser Irradiations in Aqueous Silver Nitrate Solution
Bibliographic record
Abstract
The effect of different additives on the AgNPs formation process was explored in this study. AgNPs were synthesized in an aqueous solution of silver nitrate-containing surfactants by photoreduction of silver ions. The concentration dependency of AgNPs formation suggested that stability was induced by the equilibrium of AgNPs adsorbed by surfactants with higher carbon chain molecules such as SDS and AOT. These results open up a new window both for structural control and the development process. It also indicated that different additives had an impact on the morphology of NPs. The hydrocarbon chain influenced the growth process and demonstrated that <10 carbon chain surfactants such as SMS, SOS, did not constitute the CGC and had a minor effect on the mechanism of growth. However, the NPs formation begun at a lower limit indicated as CGC. It was observed only with hydrocarbon chains of > 10 carbon atoms such as AOT, SDS. Fluorescence results confirmed that after laser irradiation, hemi-micelle formation after the development of AgNPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".