Engineering copper/cadmium doping of MoSe<sub>2</sub> for efficient photocatalytic oxytetracycline removal
Bibliographic record
Abstract
Abstract The strategy of metal ion doping of MoSe 2 effectively improves photocatalytic performance. Herein, we investigated the effect of MoSe 2 doping and co-doping on the photocatalytic degradation of oxytetracycline (OTC) antibiotic in aqueous media. As-synthesized samples were characterized by powdered X-ray diffractometry, Raman spectroscopy, UV–vis absorption spectroscopy, photoluminescence spectroscopy, scanning electron microscopy and energy-dispersive X-ray spectroscopy. Cu 2+ doping does not significantly alter the MoSe 2 band gap, but Cd 2+ doping increases the MoSe 2 band gap. Likewise, CuCd-MoSe 2 exhibits an intermediate band gap. The photocatalytic activities were tested by degrading TC under incandescent light irradiation. The results show that Cu-MoSe 2 performs the best with photocatalytic degradation of almost 97% of OTC in just 50 min, with the highest K app (0.059 min −1 ). Cu-MoSe 2 exhibits the lowest anodic-cathodic peak-to-peak ratio (ΔEp) of all the as-synthesized samples, indicating a higher electron transfer. Moreover, the largest anodic current density given by Cu-MoSe 2 reflects a more efficient electron transfer. In brief, the doping enhances photocatalytic performance compared to co-doping. This study highlights the critical importance of metal ion doping in semiconductors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".