Synthesis and characterization of iron incoporated metal-organic framework (MOF) photocatalyst in degradation of methylene blue
Bibliographic record
Abstract
Catalysis for environmental remediation is becoming of paramount importance as industrial and urban activities multiply, and by-products contaminate soils and wastewater. Effluents from industrial and urban activities have led to many environmental problems involving water contamination. Here, we propose a new iron-incorporated metal-organic framework (MOF) photocatalyst to decontaminate water. The nanocatalyst was synthesized by the solvothermal method, and Fe was added to the structure as a promoter and active phase. In this study, we examined the degradation of methylene blue (MB) as a cationic azo dye. The nanocatalysts were characterized by XRD, FE-SEM, BET, NH3-TPD, and FTIR techniques. The results showed high crystallinity, a large specific surface area, and a uniform promoter distribution. At a pH = 9, a catalyst amount of 110 mg and an initial MB concentration in the effluent of 2 ppm resulted in the highest removal percentage (98 %). The kinetic analysis provided a quasi-first-order model that reasonably matched the experimental data (R2 = 95 %). The results verified the catalyst’s great capability for efficient and fast MB removal in 60 minutes of photocatalytic processing.
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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".