Preparation of Plasmonic Cu Nanoparticles By Pulsed Laser Ablation in Liquid and Their Characterization
Bibliographic record
Abstract
Photocatalysis plays a pivotal role in solar energy transferring into usable energy. In recent years, visible light driven photocatalysts have been attracted more attention due to the broadened solar absorption wavelength. In this regard, the plasmonic photocatalysts based on gold (Au), silver (Ag) and copper (Cu) can strongly absorb visible light due to their localized surface plasmon resonance (LSPR). Compared with most studied Ag and Au nanoparticles, Cu is a low-cost plasmonic material owing to its higher earth abundance. However, the difficulty in fabricating chemically stable Cu nanoparticles limits their application. Here, the colloidal Cu nanoparticles were fabricated via pulsed laser ablation in liquid (PLAL) by Nd:YAG laser (1064 nm) and the size and optical properties of the nanoparticles were characterized by transmission electron microscopy and UV-visible spectrophotometry, respectively. The effect of fabrication parameters, such as laser fluence, ablation time, organic solvent and ablation time was further investigated. In addition, the composition and stability of the as-prepared plasmonic Cu nanoparticle was also studied. The as-prepared plasmonic Cu nanoparticle exhibit strong LSPR absorption peak in the visible region, which is beneficial to the application in photocatalysis, solar energy harvesting, optoelectronics, and biomedical technologies.
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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".