Femtosecond Laser Synthesis of AuAg Nanoalloys: Photoinduced Oxidation and Ions Release
Bibliographic record
Abstract
A femtosecond laser irradiation approach has been developed for the production of homogeneous AuAg nanoalloys of various compositions. The mean size was controlled by the dextran−nanoparticle affinity and resulted in the production of 5−7 nm nanoalloys for all nanoparticle compositions. Strong improvement of the oxidation resistance resulted from the increase of the atomic gold fraction in the nanoparticles. At gold fractions above 0.4, most of the nanoparticle oxidation was quenched, inhibiting the release of toxic silver ions in solution. The oxidation of the produced nanoparticles was mainly attributed to the generation of free radicals (O •, H •, • OH) and of molecular reactive oxygen species (O 2, H 2, H 2 O 2 ) formed by the decomposition of the water molecules through femtosecond laser-induced optical breakdown. For some biological applications, like surface-enhanced Raman spectroscopy (SERS), it is anticipated that AuAg nanoalloys would be the best compromise in terms of chemical stability and plasmonic response, as they possess much better resistance to oxidation in comparison to pure silver and a much stronger and narrower plasmon peak in comparison to pure gold.
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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) | 0.001 | 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".