Synergistic photocatalytic degradation of methylene blue and ibuprofen using Co₃O₄-Decorated hexagonal boron nitride (hBN) composites under Sun-like irradiation
Bibliographic record
Abstract
In this study, we report the synthesis and photocatalytic performance of Co₃O₄-decorated hexagonal boron nitride (hBN) composites for degrading methylene blue (MB) and ibuprofen (IBF) under sunlight irradiation. Using a dry impregnation method, the composites were prepared with varying Co₃O₄ loadings (0.5%, 1%, 2%). Comprehensive characterization confirmed the successful incorporation and uniform distribution of Co₃O₄ on the hBN matrix. Photocatalytic experiments revealed that 1% Co₃O₄-hBN composite exhibited the highest activity, achieving nearly 100% MB degradation in 60 min and 90% IBF degradation in 120 min. The enhanced photocatalytic efficiency is attributed to the synergistic effects between Co₃O₄ and hBN, which extend light absorption and promote charge separation. Our findings demonstrate the potential of Co 3 O₄-decorated hBN composites as effective photocatalysts for environmental remediation. The study provides a foundation for further exploration of these materials, including their long-term stability and application to a broader range of pollutants. • Co₃O₄-Decorated hBN was synthesized via wet impregnation. • Methylene Blue and ibuprofen were successfully degraded using Co₃O₄-hBN under Sun-like irradiation. • The 1% Co₃O₄ loading showed the highest photocatalytic efficiency for both pollutants. • Holes, O₂. •− and •OH are reactive species.
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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".