Hydrothermal Synthesis of CuO Nanoparticles: Study on Effects of Operational Conditions on Yield, Purity, and Size of the Nanoparticles
Bibliographic record
Abstract
Hydrothermal synthesis of CuO nanoparticles under near-critical and supercritical conditions was investigated from two different standpoints in the current study. The first standpoint was optimization of “yield”, “purity”, and “size of the nanoparticles” that were optimized at T = 500 °C, time = 2 h, [Cu(NO 3 ) 2 ] = 0.1 mol dm −3, and pH 3. This was achieved by undertaking an orthogonal experiment design methodology and performing different instrumental analyses, such as X-ray diffractometry, inductively coupled plasma spectrometry, and transmission electron microscopy, along with treatment of the data by analysis of variance (ANOVA). The second goal of the study was elucidation of the mechanisms of effects of operational conditions (e.g., temperature) on the above-mentioned target parameters, through application of the appropriate mechanisms of formation of nanoparticles. Nanoparticles are suggested to form initially in the liquid phase as Cu(OH) 2, which are later transformed to Cu 2 (OH) 3 NO 3, through which CuO product is obtained. Decomposition of nitric acid also plays role in this mechanism. Fabricated nanoparticles are effective catalysts for the synthesis of benzoheterocycle compounds in the pharmaceutical industries.
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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 | 0.001 |
| 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".