Ultrahigh Size Exclusion Selectivity for Carbon Dioxide from Nitrogen/Methane in an Ultramicroporous Metal–Organic Framework
Bibliographic record
Abstract
Separations based on molecular size (molecular sieving) are a solution for environmental remediation. We have synthesized and characterized two new metal–organic frameworks (MOFs) ( Zn 2 M; M = Zn, Cd) with ultramicropores (<0.7 nm) suitable for molecular sieving. We explore the synthesis of these MOFs and the role that the DMSO/H 2 O/DMF solvent mixture has on the crystallization process. We further explore the crystallographic data for the DMSO and methanol solvated structures at 273 and 100 K; this not only results in high-quality structural data but also allows us to better understand the structural features at temperatures around the gas adsorption experiments. Structurally, the main difference between the two MOFs is that the central metal in the trimetallic node can be changed from Zn to Cd and that results in a sub-Å change in the size of the pore aperture, but a stark change in the gas adsorption properties. The separation selectivity of the MOF when M = Zn is infinite given the pore aperture of the MOF can accommodate CO 2 while N 2 and/or CH 4 is excluded from entering the pore. Furthermore, due to the size exclusion behavior, the MOF has an adsorption selectivity of 4800:1 CO 2 /N 2 and 5 × 10 28:1 CO 2 /CH 4 . When M = Cd, the pore aperture of the MOF increases slightly, allowing N 2 and CH 4 to enter the pore, resulting in a 27.5:1 and a 10.5:1 adsorption selectivity, respectively; this is akin to UiO-66, a MOF that is not able to function as a molecular sieve for these gases. The data delineate how subtle sub-Å changes to the pore aperture of a framework can drastically affect both the adsorption selectivity and separation 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) | 0.000 | 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".