The Effectiveness of MOFs for the Removal of Pharmaceuticals from Aquatic Environments: A Review Focused on Antibiotics Removal
Bibliographic record
Abstract
There is an increasing level of various pollutants and their persistence in aquatic environments. The improper use of antibiotics and their inefficient metabolism in organisms result in their release into aquatic environments. Antibiotic abuse has led to hazardous effects on human health. Thereby, efficient removal of pharmaceuticals, particularly antibiotics, from wastewater and contaminated water bodies is greatly interested in international research communities. Metal-organic framework (MOF) materials, as a hybrid group of material containing metallic center and organic linkers, offer a porous structure that is highly efficient for removing different pollutants from contaminated water and wastewater streams. This article aims to review the recent advancement in using MOF-based adsorbents and catalysts for the removal of pharmaceuticals, especially antibiotics, from polluted water. Applying MOFs-based structures for removing antibiotics using photocatalytic removal and adsorptive removal techniques will be discussed and evaluated in this review paper. Various MOF-based materials such as functionalized MOFs, MOF-based composites, magnetic MOF-based composites, MOFs templated-metal oxide catalysts for removing pharmaceuticals, personal care products, and antibiotics from contaminated aqueous media are discussed. Furthermore, effective operational parameters on the adsorption, adsorption mechanisms, adsorption isotherms, and thermodynamic parameters are explained and discussed. Finally, in the concluding remarks, the challenges and future outlooks of using MOFs-based adsorbents and catalysts for removing antibiotics are summarized.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".