Mixed Matrix Membranes Produced from a Fluorinated MOF and Pebax for CO2/H2 Separation
Bibliographic record
Abstract
Hydrogen (H2) emerges as a promising clean energy source, but its efficient purification from various sources needs advanced separation technologies. This study explores the use of CO2-selective membranes, especially mixed matrix membranes (MMM) incorporating KAUST-7 metal-organic framework (MOF), for hydrogen purification. The MMM was fabricated with various KAUST-7 content in a polymer matrix (Pebax 1657) and characterized via Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and gas permeation tests. The XRD analysis confirms the incorporation of KAUST-7 into the MMM, while SEM reveals a homogeneous particle distribution at low content (below 10%) but agglomeration at higher ones (above 10%). FTIR confirms good interfacial interactions between the MOF and polymer matrix. TGA results show that the MMM thermal stability slightly decreases with increasing MOF content. Gas permeation results reveal improved CO2 permeability (79%) and CO2/H2 selectivity (19%) for MMM compared to neat Pebax membranes, with an optimal performance observed at 10 wt.% KAUST-7. Beyond this threshold, the performance deteriorates, possibly due to polymer rigidity and MOF agglomeration. Overall, the study highlights the potential of KAUST-7/Pebax MMM for enhanced hydrogen purification.
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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".