COF and MOF Hybrids: Advanced Materials for Wastewater Treatment
Bibliographic record
Abstract
Abstract Recent advances in ordered porous materials, including metal‐organic (MOF) and covalent organic frameworks (COF), are set to revolutionize the strategies used for wastewater treatment. This is attributed to the large surface area, high crystallinity, structural tunability, thermal and chemical stability, and well‐defined structures of MOF and COF. Despite the distinctive properties exhibited by the single system (either MOF or COF), the combination of COF and MOF, as a hybrid construct, offers a remarkable opportunity to achieve superior functionality and performance. The favorable features of COF–MOF hybrids in different wastewater treatment sectors have opened new venues for effective environmental remediation. This review presents the state‐of‐the‐art design, synthesis, and application of COF–MOF hybrids. The synthesis principles, including MOF‐first, COF‐first, and post‐synthetic linkage of pre‐synthesized COFs and MOFs are summarized. The potential of these novel materials is evaluated by considering contaminant sensing, adsorptive removal, and catalytic photodegradation.The conclusion is drawn by assessing the existing hurdles and potential opportunities in the development of COF‐MOF hybrids as an innovative yet viable approach for addressing wastewater treatment.
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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) | 0.000 | 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 | 0.001 | 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".