Incorporating Microporous Zn<sub>3</sub> and Zn<sub>2</sub>Cd MOFs into Pebax/PVDF Mixed Matrix Membranes for Improved Carbon Dioxide Separation Performance
Bibliographic record
Abstract
A pair of related metal–organic frameworks ( Zn 3 and Zn 2 Cd ) developed in our group were incorporated into Pebax 30R51 and PVDF Kynar 761 polymers to fabricate mixed matrix membranes (MMMs). These MOFs were chosen due to the carbon dioxide molecular sieving ability of Zn 3, and the slightly larger pore aperture of Zn 2 Cd that allows carbon dioxide and larger gases to enter the pores. For Pebax-based MMMs, this work demonstrated an over two-fold and four-and-a-half-fold increase in carbon dioxide permeability for Zn 3 - (15 wt %) and Zn 2 Cd -containing (10 wt %) MMMs over the pristine polymer. Separation selectivity (CO 2:N 2 ) of 4.21 and 7.33 were observed for Zn 3 and Zn 2 Cd (10 wt %). For PVDF-based MMMs, the incorporation of Zn 3 and Zn 2 Cd (10 wt %) increased the carbon dioxide permeability approximately two- and three-fold. The CO 2 /N 2 selectivity of the PVDF membranes increased 73% (1.01 to 1.86) and 68% (1.01 to 1.68) when 15 wt % Zn 3 and Zn 2 Cd were incorporated into PVDF. The improved performance of Pebax over PVDF based MMMs is attributed to matching the permeability of the polymer bulk phase (Pebax over PVDF) and the dispersed phase ( Zn 3 and Zn 2 Cd) . The lower permeability allows the MOF, which has slow kinetics associated with molecular sieving, to participate in the permeation process better. With regards to Zn 3 vs Zn 2 Cd, while Zn 3 acts as a molecular sieve and Zn 2 Cd does not, we hypothesize that the faster diffusion of carbon dioxide gas in Zn 2 Cd can outcompete the lower nitrogen gas permeability and molecular sieving properties of Zn 3 . However, we expect that further increasing the pore aperture would increase the permeabilities of nitrogen gas such that differences in diffusion kinetics due to molecular size would be unimportant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".