Effect of the Composition on the Nonlinear Optical Response of Au<sub><i>x</i></sub>Ag<sub>1–x</sub> Nano-Alloys
Bibliographic record
Abstract
The synthesis of variable composition Au x Ag (1–x) alloyed nanoparticles prepared by laser ablation in water is reported. The nanoalloys exhibited a single characteristic surface plasmon resonance peak, whose spectral position was lying between the surface plasmon resonance peaks of neat Ag and Au nanoparticles, at about 400 and 530 nm respectively, depending directly on the composition of the alloyed nanoparticles. The nonlinear optical response of the nanoalloys was studied in details under 532 nm (visible) 35 ps and 4 ns laser excitation and it was found to be significant and greatly influenced by the position of the plasmonic band relative to the laser excitation wavelength. In this respect, increase of the Au molar fraction resulted in shifting of the nanoalloy plasmon band toward the location of the plasmon band of neat Au nanoparticles, i.e., at about 530 nm, closer to the laser excitation wavelength, hence causing more efficient resonance enhancement of the nonlinear optical response. Moreover, the nanoalloys were found to exhibit strong saturable absorption behavior when excited by ps or ns pulses, in the latter case this behavior changing to reverse saturable absorption at high laser intensity. The origin, the magnitude and the sign of the observed optical nonlinearities of the alloyed nanoparticles are explained and discussed in terms of the hot-electron and the interband contributions taking place under laser excitation in such nanostructures. The possibility of controlling the magnitude and the sign of the nonlinear optical response of Au x Ag (1–x) nanoalloys through their composition provides an attractive and efficient way to tailor the optical nonlinearities of noble metal nanoalloys, making them very useful for various emerging photonic, biophotonic, and optoelectronic applications.
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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 | 0.001 |
| 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".