Recent advances in application of metal-organic frameworks (MOFs) as adsorbent and catalyst in removal of persistent organic pollutants (POPs)
Bibliographic record
Abstract
The presence of persistent organic pollutants (POPs) in the aquatic environment is causing widespread concern due to their bioaccumulation, toxicity, and possible environmental risk. These contaminants are produced daily in large quantities and released into water bodies. Traditional wastewater treatment plants are ineffective at degrading these pollutants. As a result, the development of long-term and effective POP removal techniques is critical. In water, adsorption removal and photocatalytic degradation of POPs have been identified as energy and cost-efficient solutions. Both technologies have received a lot of attention for their efforts to treat the world's wastewater. Photocatalytic removal of POPs is a promising, effective, and long-lasting method, while adsorption removal of persistent POPs represents a simple, practical method, particularly in decentralized systems and isolated areas. It is critical to develop new adsorbents/photocatalysts with the desired structure, tunable chemistry, and maximum adsorption sites for highly efficient removal of POPs. As a class of recently created multifunctional porous materials, Metal-organic frameworks (MOFs) offer tremendous prospects in adsorptive removal and photocatalytic degradation of POPs for water remediation. This review defines POPs and discusses current research on adsorptive and photocatalytic POP removal using emerging MOFs for each type of POPs.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".