Effect of Liquid Layer Thickness on the Ablation Efficiency and the Size-Control of Silver Colloids Prepared by Pulsed Laser Ablation
Bibliographic record
Abstract
Sliver colloidal solutions were synthesized by Nd: YAG laser ablation 1064 nm of a high purity silver target immersed in deionised water. The effect of water layer thickness on the laser ablation efficiency of nanoparticles was investigated experimentally. UV-Vis spectrophotometer and transmission electron microscopy observations were employed to characterize the optical spectra and particle sizes of colloids, respectively. The optimum parameter of the water layer thickness (which yielded the maximum ablation efficiency) was determined. It was demonstrated that both: the average particle size and the ablation efficiency which can be tuned by choosing suitable experimental parameters of liquid layer thickness, laser fluence and post-ablation laser wavelength. Average particle size and redistribution of nanoparticles was controlled by the subsequent treatment of the ablated colloid solution with combination of 1064 and 532 nm pulses. The effects of post-ablation under laser-induced particle modification reduced the average particle size from 15.1 to 4.3 nm. Particle size distribution was also narrowed with 532 nm pulses.
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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) | 0.000 | 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".