Redox-active metal-organic frameworks for the removal of contaminants of emerging concern
Bibliographic record
Abstract
The pollution due to the presence of contaminants of emerging concern (CECs) is a major cause for concern because of the serious threat it supposes to human health and ecosystem functions. Many efforts have been geared toward their removal to guarantee safer freshwater. Metal-organic frameworks (MOFs) are crystalline hybrid materials with high surface area and flexible rational design, which allows the incorporation of different active sites into a particular framework, thereby emerging as a potentially excellent candidate for water and wastewater treatment. Benefiting from the unique redox-active properties of MOFs, this review surveys literature update on their application for the removal of CECs. The underlaying electron transfer mechanism and strategies for incorporating redox-active sites into MOFs are comprehensively discussed. Different components of MOFs that are redox-active are further highlighted. This study elaborates the application of MOFs for Fenton-type and other advanced oxidation processes (AOPs) for removing emerging contaminants. AOPs generate highly reactive strong oxidants like hydroxyl and sulfate radicals that are efficient for degrading emerging pollutants with high mineralization rates. MOFs display semiconductor-like properties. Their photocatalytic use for the removal of dissolved emerging pollutants is detailed in the discussion. This review also provides an overview of the most promising directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".