Tailoring graphene-oxide and reduced-graphene-oxide with NaNO<sub>3</sub> and CaCl<sub>2</sub> catalysts with enhanced photo-catalytic degradation of methylene blue dye
Bibliographic record
Abstract
. Moreover, the prepared mixtures were utilized in the degradation process of methylene blue (MB) dye using photo-catalysis, with exposure to both ultraviolet (UV) light and sunlight. When exposed to UV and sunlight irradiation, WN-GO showed rapid and ecologically friendly breakdown of MB dye in comparison to N-GO. WN-GO exhibited exceptional adsorption capabilities, surpassing other tested materials like N-GO, WN-C-RGO and C-RGO. Although WN-C-RGO has demonstrated satisfactory performance in terms of photo-catalytic degradation, as the concentration-time graph of the MB dye revealed significant degradation, with a reduction of up to 90% and 62.5% under UV light and sunlight exposure, respectively. These results offer insightful information on the potential of graphene-based materials to address other environmental issues, particularly in the areas of water treatment.
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".