Green synthesis, characterization, and biological activities of Zn, Cu monometallic and bimetallic nanoparticles using Borassus flabellifer leaves extract
Bibliographic record
Abstract
In the present work, Zn, Cu monometallic and bimetallic nanoparticles were synthesized using leaves extract of Borassus flabellifer. Plant extract acts as both surfactant and reducing agent. The synthesized nanoparticles were characterised by UV-Vis, XRD, FESEM, EDX, and HRTEM techniques. UV-Vis spectroscopy is used to monitor the synthesis of nanoparticles. XRD technique was used to confirm the amorphous nature of nanoparticles. The FESEM images demonstrate that the shape of the nanoparticles such as Zn monometallic (pseudo-spherical), Cu monometallic (rod), Zn-Cu bimetallic are (pseudo-spherical and rod-shaped). HRTEM images show the approximate size of the Zn, Cu monometallic and Zn-Cu bimetallic nanoparticles is 3.0 nm, 3.52 nm and 2.2 nm respectively. EDX spectra confirm the presence of Zn, Cu and O in the sample. Synthesized Zn, Cu monometallic nanoparticles, and Zn-Cu bimetallic nanoparticles were used to evaluate their possible antimicrobial, antidiabetic and antioxidant properties. Bimetallic nanoparticles displayed higher antioxidant, antidiabetic, and antimicrobial properties in the comparison of monometallic nanoparticles. The results suggest that Zn-Cu bimetallic nanoparticles have greater potential than monometallic nanoparticles.
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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".