Degradation of micropollutants by metal organic framework composite-based catalysts: A review
Bibliographic record
Abstract
The presence of various micropollutants in different water sources has become a major problem due to their significant impact on both humans and the environment. This review highlights the different types of micropollutants present at the global scale and the methods applied to reduce and possibly eliminate them. These methods include membranes, adsorption and photocatalysis. While membrane filtration is extremely effective, one membrane can eliminate only a few micropollutants and its deployment remains expensive. On the other hand, adsorption constitutes a very efficient and cost-effective method, but the production of adsorbents is extremely energy intensive. Lastly, the photocatalysis method is considered to be the most promising as it avoids the problems associated with the aforementioned methods. Specifically, photocatalysts make use of direct sunlight in order to degrade micropollutants. Several types of photocatalysts, including biochar, Mxenes, nanoscaled zero valent iron, and MOFs, are discussed. Unlike the first four aforementioned types, MOFs can be combined with different materials to enhance the overall property of the composite and its efficiency in the degradation of micropollutants. The MOF-catalysts discussed in this paper include biomimetic MOFs, enzyme MOFs, and Fenton-like MOFs. The obtained system is referred to as MOF-composite-based catalysts. MOFs can be synthesized by combining an appropriate organic linker with a metallic cluster that would provide the material with the required properties for photodegradation. Several metal–organic framework catalyst composites synthesis approaches are reviewed and discussed. The selection of the approach depends on the requirements associated with the application of interest. To date, extensive research has been conducted on the performance analysis of metal–organic framework composites to investigate their efficiency in the removal of micropollutants. Several studies demonstrated their great removal capability which may reach up to 99 %. Finally, cost, health and environmental considerations are discussed with the view of the industrial applicability of MOF-composite-based catalysts. This comprehensive review presents the current state of the art and proposed promising research directions for the implementation and advancement of MOF-composite-based catalysts for micropollutants degradation.
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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) | 0.002 | 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".