Green and cost-effective phyto fabrication of copper oxide nanoparticles for exploring its therapeutic applications
Bibliographic record
Abstract
Copper oxide nanoparticles (CuONPs) are gaining traction for their promising roles in various scientific and medical applications. This study highlights a green synthesis approach using hydroalcoholic extract of Bambusa vulgaris leaves as a natural reducing and stabilizing agent, with copper sulfate serving as the precursor. The solution's transformation to a greenish-brown hue indicated successful nanoparticle formation. Bioactive compounds extracted from the leaves facilitated the reduction and stabilization processes. Comprehensive characterization techniques—including UV-Vis spectroscopy, SEM-EDX, XRD, particle size analysis, and zeta potential measurements—were employed. A UV-Vis peak at 357 nm confirmed surface plasmon resonance, while SEM revealed cuboidal nanoparticle shapes with 46.03 % copper content. XRD affirmed the crystalline structure, and DLS analysis reported an average size of 70.9 nm with a zeta potential of −1 mV. Biological assessments showcased notable bioactivity: antioxidant capacity via DPPH assay, antibacterial effects through agar well diffusion, anticancer action against PC-3 cells using MTT assay, and enhanced wound closure observed in scratch assays. These results suggest that Bambusa vulgaris -derived CuONPs possess strong antioxidant, antimicrobial, cytotoxic, and wound-healing properties. Overall, this eco-friendly method presents a viable route for producing multifunctional CuONPs with significant therapeutic potential.
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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".