CO2 Selective, Zeolitic Imidazolate Framework-7 Based Polymer Composite Mixed-Matrix Membranes
Bibliographic record
Abstract
CO2 removal is necessary to mitigate the effects of global warming but it is a challenging process to separate CO2 from natural gas, biogas, and other gas streams. Development of hybrid membranes by use of polymers and metal-organic framework (MOF) particles is a viable option to overcome this challenge. A ZIF-7 nano-filler that was synthesized in our lab was embedded into a designed polymer matrix at various loadings and the performance of the mixed matrix membranes was evaluated in terms of gas permeance and selectivity. Hybrid membranes with various loadings (20, 30 and 40 wt%) were developed and tested at room temperature by a custom made time lag equipment and a jump in selectivity was observed when compared with the pristine polymer. A commercially attractive region for the selectivity CO2 over CH4 was achieved with a selectivity of 39 for 40 wt% particle loading. An increase in selectivity was observed with the increase of ZIF-7 loadings. Best performance was seen at 40% ZIF-7 loaded membrane with an ideal selectivity of 39 for CO2 over CH4. The obtained selectivity was 105% higher for CO2 over CH4 than the selectivity of the pristine polymer with a slight decrease in permeance. Morphological characterization of such developed membranes showed an excellent compatibility between the polymer and particle adhesion.
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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) | 0.000 | 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 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".