Amino-silane-grafted NH<sub>2</sub>-MIL-53(Al)/polyethersulfone mixed matrix membranes for CO<sub>2</sub>/CH<sub>4</sub> separation
Bibliographic record
Abstract
Mixed-matrix membranes (MMMs) are promising candidates for carbon dioxide separation. However, their application is limited due to improper dispersion of fillers within the polymer matrix, poor interaction of fillers with polymer chains, and formation of defects and micro-voids at the interface of both phases, which all result in the decline of the gas separation performance of MMMs. In this work, we present a new method to overcome these challenges. To this end, a series of MMMs based on polyethersulfone (PES) as the continuous polymer matrix and MIL-53-derived MOFs as the dispersed filler were prepared. FTIR-ATR, XRD, TGA, FESEM, and N2 adsorption/desorption analyses were employed to characterize the structural properties of the synthesized nanoparticles. The obtained results indicated that 3-aminopropyltriethoxysilane (APTES) molecules were successfully attached onto the surface of NH2-MIL-53(Al). Morphological characterization by FESEM and energy dispersive X-ray mapping (EDX) showed that desirable distribution within the whole membrane thickness, suitable nanoscale dispersion, and excellent interface were achieved by using amino-silane-grafted NH2-MIL-53(Al) (A-MIL-53(Al)) nanoparticles. The permeation results indicated that the permeability of two gases and the ideal CO2/CH4 selectivity enhanced by increasing the concentration of MOFs. In particular, comparing the experimental gas separation results of A-MMM-10 with those of pure PES membrane showed an 84% increase in the CO2 permeability and a 70% increase in CO2/CH4 selectivity. These results suggest that post-synthetic modification of MOF nanoparticles and strong interfacial adhesion between functionalized nanoparticles and polymer matrix could be a useful method to eliminate interfacial voids and improve gas separation efficiency.
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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 | 0.000 | 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".