The Effect of Composition on the Properties and Application of CuO-NiO Nanocomposites Synthesized Using a Saponin-Green/Microwave-Assisted Hydrothermal Method
Bibliographic record
Abstract
In this study, we explored the formation of CuO nanoparticles, NiO nanoflakes, and CuO-NiO nanocomposites using saponin extract and a microwave-assisted hydrothermal method. Five green synthetic samples were prepared using aqueous saponin extract and a microwave-assisted hydrothermal procedure at 200 °C for 30 min. The samples were pristine copper oxide (100C), 75% copper oxide-25% nickel oxide (75C25N), 50% copper oxide-50% nickel oxide (50C50N), 25% copper oxide-75% nickel oxide (25C75N), and pristine nickel oxide (100N). The samples were characterized using FT-IR, XRD, XPS, SEM, and TEM. The XRD results showed that copper oxide and nickel oxide formed monoclinic and cubic phases, respectively. The morphology of the samples was useful and consisted of copper oxide nanoparticles and nickel oxide nanoflakes. XPS confirmed the +2 oxidation state of both the copper and nickel ions. Moreover, the optical bandgaps of copper oxide and nickel oxide were determined to be in the range of 1.29-1.6 eV and 3.36-3.63 eV, respectively, and the magnetic property studies showed that the synthesized samples exhibited ferromagnetic and superparamagnetic properties. In addition, the catalytic activity was tested against para-nitrophenol, demonstrating that the catalyst efficiency gradually improved in the presence of CuO. The highest rate constants were obtained for the 100C and 75C25N samples, with catalytic efficiencies of 98.7% and 78.2%, respectively, after 45 min.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".