Breaking the trade-off between selectivity and adsorption capacity for gas separation
Bibliographic record
Abstract
The trade-off between selectivity and adsorption capacity with porous materials is a major roadblock to reducing the energy footprint of gas separation technologies. To address this matter, we report herein a systematic crystal engineering study of C 2 H 2 removal from CO 2 in a family of hybrid ultramicroporous materials (HUMs). The HUMs are composed of the same organic linker ligand, 4-(3,5-dimethyl-1 H -pyrazol-4-yl)pyridine, pypz, three inorganic pillar ligands, and two metal cations, thereby affording six isostructural pcu topology HUMs. All six HUMs exhibited strong binding sites for C 2 H 2 and weaker affinity for CO 2 . The tuning of pore size and chemistry enabled by crystal engineering resulted in benchmark C 2 H 2 /CO 2 separation performance. Fixed-bed dynamic column breakthrough experiments for an equimolar (v/v = 1:1) C 2 H 2 /CO 2 binary gas mixture revealed that one sorbent, SIFSIX-21-Ni, was the first C 2 H 2 selective sorbent that combines exceptional separation selectivity (27.7) with high adsorption capacity (4 mmol·g −1 ).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".