Composites of Metal–Organic Frameworks (<scp>MOFs</scp>): Synthesis and Applications in Separation and Catalysis
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) are one of the leading edge materials that have gained enormous research attention in the past decade. Some extraordinary features including adjustable porosity, easy synthesis process, and tailorable functionality render MOFs as potential candidates for a wide range of applications. Moreover, the preparation of MOF composite is also considered as a promising strategy to improve their physicochemical properties through the synergistic effect. Also, this allows for an additional dimension to precisely control the properties of the resulting composite through the variation of synthesis conditions and formulations. Significant enhancements could be realized for the existing MOFs applications as well as creating new frontiers where MOFs can play vital roles. This review aims at presenting and discussing the rapidly growing research and development in this area. The different synthesis protocols, their impacts on the resultant properties, and their applications in separation and catalysis were summarized and discussed in this chapter.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".