Tuning the size of gold nanoparticles produced by multiple filamentation of femtosecond laser pulses in aqueous solutions
Bibliographic record
Abstract
In the present study, we consider the self-regulated generation of spatially homogeneous low density plasma (LDP) micro-channels as a high intensity ionization source arising from the multi-filamentation of powerful femtosecond (fs) laser pulses in aqueous solutions. We investigate the modulation of the femtosecond laser multiple filamentation for tuning the size of gold nanoparticles (AuNPs) synthesized in an irradiated gold chloride solution. Previous studies on the radiation-induced synthesis of colloidal gold by more conventional ionization sources, such as high energy γ-rays and electron beams, highlighted the dependence of the size distribution of AuNPs on the density of energy deposited per unit of time, i.e. the dose rate. The present method of laser-induced production of AuNPs rests on a similar radiation-assisted process, i.e. the reduction of the solvated trivalent gold ions by the hydrated electrons produced upon ionization of water. We find that trivial optical manipulation varies the rate of deposited energy by laser irradiation, which can be considered equivalent to a variation of the dose rate. We investigate the influence of varying the density of energy deposited on the laser-induced gold cluster size distribution and made a comparison with the high energy radiation-induced synthesis of AuNPs. Here, our results highlight that the present method of laser irradiation, in the regime of LDP generation, mimics the radiolysis of water at an adjustable high dose rate. More generally, these spatially and temporally resolved plasmas could be developed as a tool for the unprecedented control of chemistry under ionizing radiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".