Microporous Adsorbent-Based Mixed Matrix Membranes for CO2/N2 Separation
Bibliographic record
Abstract
As the atmospheric carbon dioxide (CO2) concentration rapidly rises, carbon capture, utilization, and storage (CCUS) is an emerging field for climate change mitigation. Various carbon capture technologies are in development with the help of adsorbents, membranes, solvent-based systems, etc. One of the main challenges in this field is the removal of CO2 from nitrogen (N2) gas. This paper focuses on mixed matrix membrane technology, for which the CO2/N2 separation performance is based on differences in gas permeations. Membrane separation and purification technologies are widely studied for carbon capture. Microporous adsorbents such as zeolites and metal organic frameworks (MOFs) for carbon capture have been attracting researchers’ attention due to their highly porous structures, high selectivity values, and tunable porosities. Utilizing microporous adsorbents dispersed within a novel, blended polymer matrix, fourteen membranes were prepared with the commercial MOF ZIF-8, zeolite 13X, and kaolin, with methyl cellulose (MC) and polyvinyl alcohol (PVA), which were tested using a single gas permeation setup in this study. The addition of polyallylamine (PAH) as a chemisorbent was also investigated. These membranes were synthesized both with and without a polyacrylonitrile (PAN) support to compare their performances. MC was found to be an ideal polymeric matrix component to develop free-standing MMMs. At 24 °C and a relatively low feed pressure of 2.36 atm, a free-standing zeolite-13X-based membrane (MC/PAH/13X/PVA) exhibited the highest N2/CO2 selectivity of 2.8, with a very high N2 permeability of 6.9 × 107 Barrer. Upon the optimization of active layer thickness and filler weight percentages, this easily fabricated free-standing MMM made of readily available materials is a promising candidate for CO2 purification through nitrogen removal.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".