Effect of incorporating different ZIF-8 crystal sizes in the polymer of intrinsic microporosity, PIM-1, for CO2/CH4 separation
Bibliographic record
Abstract
Effective and economical carbon dioxide-methane separation (CO2/CH4) is highly desirable in several industries such as sweetening natural gases and renewable natural gas (RNG) from biogas and landfills. Among the different separation technologies, membrane separation has been shown to have lower cost of production and lower CH4 losses. In this study, Zeolitic Imidazole Frameworks (ZIF-8) crystals with sizes varying from 45 nm to 450 nm were synthesized and incorporated in the polymer of intrinsic microporosity, PIM-1, to form mixed matrix membranes (MMMs). The structure, morphology, and physicochemical properties of the MMMs were characterized by 1H NMR, FTIR, XRD, TGA, and SEM. ZIF-8 crystal size was controlled using the concentration of sodium formate. The influence of the ZIF-8 crystal size on MMMs was studied by sorption, gas permeability, and aging of the membranes. The MMMs with ZIF-8 crystals of 120 nm particle diameter yielded the greatest improvement in gas transport properties; the CO2/CH4 selectivity-CO2 permeability was 11.4 and 9700 Barrer compared to PIM-1 with 6.4 and 9300 Barrer respectively. The former is near the Robeson 2008 upper bound, while PIM-1 is on the 1991 upper bound. After 40 days of aging, selectivity increased and permeability decreased; the changes were parallel to the Robeson upper bounds indicating increased polymer packing and diffusivity selectivity.
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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".