Novel Synthesis of CuO/GO Nanocomposites and Their Photocatalytic Potential in the Degradation of Hazardous Industrial Effluents
Bibliographic record
Abstract
Photocatalytic degradation of dyes has been the subject of extensive study due to its low cost, eco-friendly operation, and absence of secondary pollutants. Copper oxide/graphene oxide (CuO/GO) nanocomposites are emerging as a new class of fascinating materials due to their low cost, nontoxicity, and distinctive properties such as a narrow band gap and good sunlight absorbency. In this study, copper oxide (CuO), graphene oxide (GO), and CuO/GO were synthesized successfully. X-ray diffractometer (XRD) and Fourier transform infrared (FTIR) spectroscopy confirm the oxidation and production of GO from the graphene of lead pencil. According to the morphological analysis of nanocomposites, CuO nanoparticles of sizes ≤20 nm on the GO sheets were evenly adorned and distributed. Nanocomposites of different CuO:GO ratios (1:1 up to 5:1) were applied for the photocatalytic degradation of methyl red (MR). CuO:GO(1:1) nanocomposites achieved 84% MR dye removal, while CuO:GO(5:1) nanocomposites achieved the highest value (95.48%). The thermodynamic parameters of the reaction for CuO:GO(5:1) were evaluated using the Van't Hoff equation and the activation energy was found to be 44.186 kJ/mol. The reusability test of the nanocomposites showed high stability even after seven cycles. CuO/GO catalysts can be used in the photodegradation of organic pollutants in wastewater at room temperature due to their excellent properties, simple synthesis process, and low cost.
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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".