Physicochemical, catalytic, and antimicrobial activities of porous cobalt oxide nanoparticles (kinetics study of H<sub>2</sub>O<sub>2</sub> decomposition using fluorometric and gasometric methods)
Bibliographic record
Abstract
Co3O4 nanomaterials were prepared by different methods. The samples were nominated as Co3O4 (Pr), Co3O4 (Co), and Co3O4 (Hy) due to preparation by precipitation, combustion, and a hydrothermal method, respectively. These nanomaterials were characterized by studying their structural, morphological, and surface properties. The catalytic activity was evaluated by following H2O2 decomposition through fluorometric and gasometric methods. The obtained results showed that the catalytic efficiency of the catalysts was affected by their preparation methods. The order of catalytic activities of the investigated samples using fluorometric method is compatible with that obtained by gasometric method. Co3O4 (Hy) exhibited much higher catalytic activity due to the high surface area, small particle size, different oxidation states, different shapes, and the high production of hydroxyl radical. The antimicrobial activity is studied against standard bacterial and fungal strains, and the studies showed that the hydrothermal method enhanced antimicrobial activity more than other preparation methods.
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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".