Enhanced Visible Light Photocatalytic Degradation of Organic Pollutants over Flower-Like Bi2O2CO3 Dotted with Ag@AgBr
Bibliographic record
Abstract
A facile and feasible oil-in-water self-assembly approach was developed to synthesize flower-like Ag@AgBr/Bi₂O₂CO₃ micro-composites. The photocatalytic activities of the samples were evaluated through methylene blue degradation under visible light irradiation. Compared to Bi₂O₂CO₃, flower-like Ag@AgBr/Bi₂O₂CO₃ micro-composites show enhanced photocatalytic activities. In addition, results indicate that both the physicochemical properties and associated photocatalytic activities of Ag@AgBr/Bi₂O₂CO₃ composites are shown to be dependent on the loading quantity of Ag@AgBr. The highest photocatalytic performance was achieved at 7 wt % Ag@AgBr, degrading 95.18% methylene blue (MB) after 20 min of irradiation, which is over 1.52 and 3.56 times more efficient than that of pure Ag@AgBr and pure Bi₂O₂CO₃, respectively. Bisphenol A (BPA) was also degraded to further demonstrate the degradation ability of Ag@AgBr/Bi₂O₂CO₃. A photocatalytic mechanism for the degradation of organic compounds over Ag@AgBr/Bi₂O₂CO₃ was proposed. Results from this study illustrate an entirely new approach to fabricate semiconductor composites containing Ag@AgX/bismuth (X = a halogen).
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".