Metal‐Organic Frameworks and Electrospinning: A Happy Marriage for Wastewater Treatment
Bibliographic record
Abstract
Abstract Metal‐organic frameworks (MOFs), an emerging class of porous organic‐inorganic hybrid materials, have shown great potential for water and wastewater treatment applications. However, pure MOF powders have limited practical applications in water treatment due to their insolubility, poor processability, brittleness, safety hazard from dust formation, and difficult separation from aqueous solutions. Thus, exploring potential MOFs composites with improved separation performance is of great importance. The marriage of MOFs with electrospun nanofiber with forethought into the final product's morphology, structure, and chemistry has opened up new opportunities for efficient wastewater treatment. The present review exhaustively summarizes the strategies to integrate MOFs into nanofibers via electrospinning to remove various pollutants (i.e., organic dyes, heavy metal ions, pharmaceuticals, personal care products, oily compounds, organic solvents, etc.) via adsorption, photodegradation, and membrane filtration. Besides, the most recent advances of electrospun MOF nanofibers for wastewater treatment and their current challenges and future outlook are delineated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".