Ultrasonically synthesized MOFs for modification of polymeric membranes: A critical review
Bibliographic record
Abstract
Metal-organic framework (MOF) membranes hold the promise for energy-efficient separation processes. These nanocrystalline compounds can effectively separate materials with different sizes and shapes at a molecular level. Furthermore, MOFs are excellent candidates for improving membrane permeability and/or selectivity due to their unique properties, such as high specific area and special wettability. Generally, MOFs can be used as fillers in mixed matrix membranes (MMMs) or incorporated onto the membrane surface to modify the top layer. Characteristics of the MOFs, and correspondingly, the properties of the MOF-based membranes, are majorly affected by their production technique. This critical review discusses the sonication technique for MOF production and the opportunities and challenges of using MOF for making membranes. Effective parameters on the characteristics of the synthesized MOFs, such as sonication time and power, were discussed in detail. Although the ultrasonically synthesized MOFs have shown great potential in the fabrication/modification of membranes for gas and liquid separation/purification, so far, no comprehensive and critical review has been published to clarify such accomplishments and technological gaps for the future research direction. This paper aims to review the most recent research conducted on ultrasonically synthesized MOF for the modification of polymeric membranes. Recommendations are provided with the intent of identifying the potential future works to explore the influential sonication parameters.
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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 | 0.001 |
| 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) | 0.003 | 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".