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 MoSe2 effectively improves photocatalytic performance. Herein, we investigated the effect of MoSe2 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. Cu2+ doping does not significantly alter the MoSe2 band gap, but Cd2+ doping increases the MoSe2 band gap. Likewise, CuCd-MoSe2 exhibits an intermediate band gap. The photocatalytic activities were tested by degrading TC under incandescent light irradiation. The results show that Cu-MoSe2 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-MoSe2 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-MoSe2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".