Green synthesis of nano‐crystallite <scp>CuO</scp> from waste sources for the enhancement of antimicrobial properties
Bibliographic record
Abstract
Abstract Global electronic waste has almost doubled since 2010, increasing from 33.8 MMT to over 62 MMT in 2022, and will reach 82 MMT by 2030. So, it will become the most pressing problem for the world if it cannot be utilized properly. In this research, Cu was collected from waste wire and used to synthesize CuO nanoparticles. The hydrothermal technique was properly employed, and X‐ray diffraction (XRD), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) were used to characterize the prepared CuO nanoparticles. Scherrer's equation, linear straight‐line method, Monshi–Scherrer's method, size‐strain plot method, Williamson–Hall method, Sahadat–Scherrer's model, and Halder–Wagner method were applied to evaluate the crystallite size, preference growth, volume of the unit cell, lattice parameters, degree of crystallinity, macrostrain, energy density, and crystallinity index of the developed CuO samples. All models, except the linear straight‐line model, computed that crystallite size was within the acceptable range of 1–100 nm. The Rietveld refinement reported that the synthesized compound consisted of CuO phases and a small amount of Cu 2 O, which was negligible. The antimicrobial action of synthesized CuO nanoparticles was examined against gram‐negative ( Escherichia coli ) and gram‐positive ( Staphylococcus aureus ) bacteria. The investigations found that a significant inhibition zone was created for those microorganisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".