Recent advances in synthesis and applications of mixed matrix membranes
Bibliographic record
Abstract
Researchers are currently considering membranes separation processes due to their eco-friendly, process simplicity and high efficiency. Selecting a suitable and efficient operation is the primary concern of researchers in the field of separation industries. In recent decades, polymeric and inorganic membranes in the separation industry have made significant progress. The polymeric and inorganic membranes have been challenged due to their competitiveness in permeability and selectivity factors. A combination of nanoparticle fillers within the polymer matrix is an effective method to increase polymeric and inorganic membranes’ efficiency in separation processes. Mixed matrix membranes (MMMs) have been considered by the separation industry due to high mechanical and physicochemical, and transfer properties. Moreover, gas separation, oil treatment, heavy metal ions removal, water treatment and oil-water separation are common MMMs applications. Selecting suitable polymer blends and fillers is the key to the MMMs construction. The combination of rubbery and glassy polymers with close solubility parameters increases the MMMs performance. The filler type and synthesis methods also affect the morphological and transfer properties of MMMs significantly. Zeolites, graphene oxide (GO), nanosilica, carbon nanotubes (CNTs), zeolite imidazole frameworks (ZIFs) and metal-organic frameworks (MOFs) are used in the MMMs synthesis as fillers. Finally, solution mixing, polymerization in situ and sol-gel are the primary synthesising MMMs methods.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".