Effect of Metal Atom in Zeolitic Imidazolate Frameworks (ZIF-8 & 67) for Removal of Dyes and Antibiotics from Wastewater: A Review
Bibliographic record
Abstract
The use of antibiotics and dyes has resulted in severe water pollution and health risks; therefore, it is urgent to remove them from water sources. Among the most common methods for removing harmful water contaminants, adsorption and photodegradation are the most economical, simple, and reusable. Due to their high porosity, adjustability, and crystal structure, metal-organic frameworks (MOFs) are one of the effective adsorbents and photocatalysts. A typical MOF material is zeolitic imidazolate framework-8/67 (ZIF-8 and ZIF-67), comprising essentially of the metal atoms Zn and 2-methylimidazole (2-MIM). ZIF-8 and ZIF-67 have unique properties that make them efficient in water treatment due to high adsorption capacities and being good hosts for photocatalytic materials. In this article, a review study of the design and methods of synthesis of ZIF-8 and ZIF-67 composites is presented. An introduction to the current research on the role of ZIF-8 and ZIF-67 compounds as adsorbents and photocatalysts for wastewater pollution removal is provided. In this review study, we aim to supply a mechanistic perspective on the use of ZIF-8/67 composites in wastewater purification and present novel visions for the development of extremely effective ZIF-8/67-based adsorbents and photocatalysts. To unlock the full potential of ZIF-8/67 composites in dye and antibiotic removal and water recycling, current difficulties will be discussed in detail.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".