Low-power CW laser-assisted synthesis of plasmonic aluminium nanoparticles using low duty cycle optical chopper
Bibliographic record
Abstract
This paper reports continuous wave (CW) laser-assisted synthesis of plasmonic aluminium nanoparticles (ANPs) by drastically reducing the laser power density from the existing 10 5 W/cm 2 to 9 W/cm 2 using a specially designed ultralow duty cycle chopper. The aluminium target is subjected to laser irradiation by keeping it in a liquid medium maintained at a specific temperature. The effect of chopping frequencies (F), temperatures (T), and surfactant-glucose (C) on the formation of ANPs are investigated through field emission scanning electron microscopic and UV-visible spectroscopic analyses. The energy required for the ejection of ANPs from the target is obtained from the optical source, thermal source, and the molecular collision due to convection current. The purity of the nanoparticle is determined from energy dispersive spectroscopic analysis. The analysis reveals that ANPs of sizes in the range 20–50 nm are formed when F = 30 Hz, T = 60 °C, and C = 18 mg/cc, exhibiting excellent ageing stability. The photoluminescence spectrum, power spectrum, and CIE analyses for the excitations at 350 nm and 390 nm indicate the emission to be blue, suggesting that it is a suitable fluorescence material for UV-plasmonics.
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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) | 0.000 | 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".