The Effectiveness of MOFs for the Removal of Pharmaceuticals from Aquatic Environments: A Review Focused on Antibiotics Removal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it