Nickel-Based Metal–Organic Frameworks to Improve the CO<sub>2</sub>/CH<sub>4</sub> Separation Capability of Thin-Film Pebax Membranes
Bibliographic record
Abstract
Incorporating metal–organic frameworks (MOFs) into the thin layer of thin-film composite (TFC) membranes is an effective way of improving the CO 2 /CH 4 separation performance. In this study, porous polyethersulfone (PES) membranes were surface-coated with a novel CO 2 -permeable layer consisting of CO 2 -philic Pebax and nickel-based MOF particles. The MOF particles were synthesized using nickel(II) acetate tetrahydrate as a metal source and 2-amino-1,4-dicarboxybenzene (NH 2 -BDC) as an organic linker. The properties and performance of the MOFs and synthesized membranes were assessed using analytical techniques including differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), field-emission scanning electron microscopy (FE-SEM), and dynamic light scattering (DLS). DLS analysis showed that the MOF particle size range was in a range of 350–650 nm. Moreover, cross-sectional FE-SEM images depicted that a uniform and dense Pebax layer was shaped on top of the PES substrate. Well dispersion of the particles was demonstrated by surface FE-SEM imaging. DSC analysis showed that embedding Ni-NH 2 -BDC MOF particles into the Pebax-1657 film increased the crystallinity degree and the glass-transition temperature ( T g ) of resulted membranes. To evaluate the membrane’s separation performance, permeation experiments were performed with CO 2, CH 4, and CO 2 /CH 4 mixtures at ambient temperature. Embedding 5 wt % Ni-based MOF particles improved the CO 2 permeability and CO 2 /CH 4 selectivity from 19.05 Barrer and 32.2 to 31.55 Barrer and 94, respectively, compared to MOF-free membranes. Loading MOF particles into the Pebax matrix also improved the real gas separation factor. The obtained results demonstrate the great potential of the fabricated TFC membranes for gas separation.
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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) | 0.001 | 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".