Green synthesis of copper oxide nanoparticles: Characterization and applications for environmental and biomedical fields
Bibliographic record
Abstract
Abstract In recent years, there has been an increasing interest in the development of plant‐based nanoparticles due to their numerous benefits over conventional physio‐chemical methods, including sustainability and environmental safety. Green synthesis, a process that produces safe and sustainable goods without the use of harsh chemicals or other harmful processes, is gaining popularity. The current study focuses on the green synthesis of copper oxide nanoparticles using Piper nigrum leaf extracts, their characterization, and applications. The synthesis of nanoparticles was confirmed by changes in colour, further endorsed by UV–visible spectroscopy. Copper oxide (CuO) nanoparticles were characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). CuO nanoparticle sizes ranged between 58.23 and 69.89 nm and were spherical in shape. FTIR results indicated a functional group capped on the nanoparticle surface. The antibacterial activity of the copper oxide nanoparticles was tested, and they exhibited the significant decrease in bacterial concentration and the largest zone of inhibition, making them an efficient disinfectant. Antimicrobial activity against Bacillus subtilis and Escherichia coli was observed. Furthermore, the synthesized CuO nanoparticles exhibited a high affinity for safranin dyes and demonstrated maximum removal efficiency. This makes them an effective agent for removing dyes in wastewater from industries such as clothing manufacturing. Safranin dye was successfully removed with an efficiency of 78% using nanoparticles. In conclusion, the green synthesis of copper oxide nanoparticles using plant extracts presents an eco‐friendly and sustainable approach for producing nanoparticles with a wide range of potential applications.
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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 | 0.001 | 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".