Ionic Hydrophobic Gates on Metal–Organic Frameworks Enable High-Purity CO<sub>2</sub> Separation from Humid Flue Gas
Bibliographic record
Abstract
Efficient extraction of high-purity CO 2 from humid flue gas via adsorptive separation offers a promising and sustainable solution for carbon reduction and downstream applications. However, the coadsorption of H 2 O vapor and N 2 from humid flue gas remains a persistent challenge that limits separation efficiency. To overcome this issue, this work introduces a novel concept of ionic hydrophobic gates on porous adsorbents, which enables one-step separation of high-purity CO 2 directly from humid flue gas. By assembling hydrophobic ionic liquids and fluorine-rich terephthalaldehyde onto the surface of a metal–organic framework (MOF), this design establishes H 2 O barriers and CO 2 channels on the outer shell while maintaining pore integrity in the core. The resulting core–shell material demonstrates exceptional CO 2 adsorption capacity and an extraordinary CO 2 /N 2 selectivity of 1780 (15/85, v/v), surpassing conventional adsorbents. Notably, dry CO 2 with 99.999% purity is successfully extracted from humid flue gas (relative humidity, RH = 100%) in a single breakthrough experiment. In situ diffuse reflectance Fourier transform infrared spectroscopy (in situ DRIFTS) and density functional theory calculations reveal that fluorine-rich hydrophobic sites act as effective H 2 O barriers, while ionic liquid segments facilitate the transport of CO 2 through hydrogen bonding and electrostatic interactions. Owing to its excellent scalability and broad compatibility with diverse MOF platforms, this ionic hydrophobic gating strategy offers a robust and versatile approach for constructing advanced gas separation materials, holding great promise for industrial applications in carbon capture, clean energy, and sustainable chemical processes.
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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".