CO Guest Interactions in SDB-Based Metal–Organic Frameworks: A Solid-State Nuclear Magnetic Resonance Investigation
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) are promising materials for greener carbon monoxide (CO) capture and separation processes. SDB-based (SDB = 4,4 ’ -sulfonyldibenzoate) MOFs are particularly attractive due to their remarkable gas adsorption capacity under humid conditions. However, to the best of our knowledge, their CO adsorption abilities have yet to be investigated. In this report, CO-loaded PbSDB and CdSDB were characterized using variable-temperature (VT) 13 C solid-state nuclear magnetic resonance (SSNMR) spectroscopy. These MOFs readily captured CO, with the adsorbed CO exhibiting dynamics as indicated by the temperature-dependent changes in the SSNMR spectra. Spectral simulations revealed that the CO simultaneously undergoes a localized wobbling about the adsorption site and a nonlocalized hopping between adjacent adsorption sites. The wobbling and hopping angles were also found to be temperature-dependent. From the appearance of the VT spectra and the extracted motional data, the CO adsorption mechanism was concluded to be analogous to that of CO 2 . To gain a better understanding on the gas adsorption properties of these MOFs and their CO capture abilities, we subsequently compared the motional data to those reported for CO 2 in SDB-based MOFs and CO in MOF-74, respectively. A significant contrast in adsorption strength was observed in both cases because of the different physical properties of the guests (i.e., CO vs CO 2 ) and the MOF frameworks (i.e., SDB-based MOFs vs MOFs with open metal sites). Our results demonstrate that SSNMR spectroscopy can be employed to probe variations in binding behavior.
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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".