Stay Together or Split Up: Investigating Selective Adsorption of Carbon Dioxide and Acetylene in Anion-Pillared Microporous Metal–Organic Frameworks
Bibliographic record
Abstract
A major challenge in gas separation is designing porous materials with energy-efficient guest selectivity, primarily because molecular-level mechanisms underpinning adsorption and selectivity remain unclear from both the thermodynamic and kinetic perspectives. This work examines the selective adsorption of acetylene versus carbon dioxide in anion-pillared microporous metal–organic frameworks (MOFs) using solid-state nuclear magnetic resonance spectroscopy, quantum mechanical cluster models integrated with density functional theory (DFT) calculations, and molecular dynamics simulations. The isostructural SIFSIX-1-Cu and SIFSIX-3-Cu MOFs share a pcu topology, are composed of the same Cu(II) metal node, and have an identical inorganic pillared ligand (SiF 6 2– ) but incorporate the different 4,4’ bipyridine and pyrazine organic linkers. The variation in pore size and chemical composition between SIFSIX-1-Cu and SIFSIX-3-Cu gives rise to distinct host–guest interactions. Multinuclear in situ variable temperature solid-state nuclear magnetic resonance experiments targeting single-component and binary mixtures of 13 CO 2 and C 2 D 2, accompanied by DFT calculations, reveal that SIFSIX-1-Cu is strongly selective toward acetylene adsorption despite its larger pore size, due to stronger C–H···F interactions and more favorable host–guest geometry, while SIFSIX-3-Cu efficiently adsorbs both acetylene and carbon dioxide. The adsorption locations and guest dynamics of acetylene and carbon dioxide were determined from experimental and computational data. These findings provide important guidance for rational design of selective adsorption materials, including metal–organic frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".