Enhanced visible photocatalytic activity of Fe‐Cu‐ZnO/graphene oxide photocatalysts for the degradation of organic dyes
Bibliographic record
Abstract
Abstract Fe‐Cu‐ZnO/graphene oxide (Fe‐Cu‐ZnO/GO) photocatalysts are successfully prepared by the sol‐gel method and characterized by X‐ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), high resolution transmission electron microscopy (HRTEM), Fourier transform infrared spectrometry (FTIR), UV‐Vis diffuse reflectance spectra (UV‐Vis DRS), and X‐ray photoelectron spectroscopy (XPS). The results show that the Fe‐Cu‐ZnO/GO photocatalyst has the smaller average crystallite size and the narrower band gap, exhibits the stronger light absorption in the whole visible light region, and possesses better charge separation capability than that of pure ZnO, ZnO/GO, and Cu‐ZnO/GO photocatalysts. The photocatalytic activity of these catalysts is tested by degradation of dark green dye under visible light irradiation which demonstrates that Fe‐Cu‐ZnO/GO photocatalyst effectively degrades dark green dye and shows a significant photocatalytic enhancement compared to ZnO, ZnO/GO, and Cu‐ZnO/GO photocatalysts. The degradation rate of dark green dye can reach up to 99.28 %, when the initial concentration of dark green dye is 50 mg/L and the Fe‐Cu‐ZnO/GO catalyst dosage is 1 g/L with neutral pH under 90 min of visible light irradiation. In addition, Fe‐Cu‐ZnO/GO photocatalyst shows a good degradation efficiency on three other dyes. Meanwhile, the catalyst shows relatively superior reusability according to the cycling tests.
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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".