Optimization of Copper Oxide Nanoparticles Production by Pulsed Laser Ablation: A Study on Energy Density Effects
Bibliographic record
Abstract
Nanomaterial's especially metal oxide nanoparticles have recently been of interest in many fields such as physics, biology, and medicine.This work employs Pulsed laser ablation in liquid as a clean and versatile technique for synthesizing copper oxide nanoparticles that produce high-quality materials with minimal chemical interference.Copper plates were treated in deionized water using laser energy density to produce copper nanoparticles at two different energy densities of 400 and 800 mJ.Copper oxide nanoparticles categorized by X-ray diffraction method, UV-visible spectroscopy and scanning electron microscopy (SEM).Changes in absorption spectra and subsequent UVvisible spectroscopy were observed for particle size, with a redshift at greater laser intensity (650 nm for 800 mJ compared to 600 nm for 400 mJ).X-ray diffraction (XRD) result presented this material as having a monoclinic crystalline structure with particle size estimated to be between 30 and 60 nm (34.626° and 34.714° 2θ for 400 mJ; 31.49° and 66.45° 2θ for 800 mJ).Specific morphological analyses through SEM showed products contained elongated nanoflake-like shaped particles extending to 800 mJ that aggregated into a rough structure which boost their catalytic effectiveness.These results confirm application for photocatalytic activities, antibacterial activity, and enhanced biomedical science by controlling size, optical, structure, and morphology.
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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".