Understanding The Fascinating Origins of CO<sub>2</sub> Adsorption and Dynamics in MOFs
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) have shown great promise for the adsorption and separation of gases, including the greenhouse gas CO 2 . In order to improve performance and realize practical applications for MOFs as CO 2 adsorbents, a deeper understanding of the number and type of CO 2 adsorption mechanisms must be unlocked, along with fine details of CO 2 motion within MOFs. Using several complementary characterization methods is a promising protocol for comprehensively investigating the various host–guest interactions between MOFs and CO 2 . In this work, a combination of solid state NMR (SSNMR) and single crystal X-ray diffraction (SCXRD) has been utilized to reveal both the location and dynamics of adsorbed CO 2 within the related PbSDB and CdSDB MOFs, as well as to probe the role of metal centers in CO 2 adsorption. 13 C SSNMR experiments targeting CO 2 reveal the number of unique adsorption sites and the types of CO 2 dynamics present, as well as their associated motional rates and angles. 111 Cd and 207 Pb SSNMR methods are used to probe the influence of CO 2 adsorption on the MOF metal centers, and also to investigate the possibility of any metal–guest interactions. SCXRD experiments yield the exact locations and occupancies of adsorbed CO 2 in both MOFs; by pairing this information with SSNMR data, a comprehensive model of CO 2 adsorption and dynamics in PbSDB and CdSDB has been established. Both MOFs share a common adsorption site in the V-shaped “π-pocket” formed by the phenyl rings of an individual V-shaped organic linker, while CdSDB also features an additional π-pocket adsorption site arising from the phenyl rings of two linkers joined by Cd. SCXRD and SSNMR data indicate that CO 2 adsorbed at the SDB-based π pocket in both MOFs exhibits a local rotation or “wobbling” at an individual adsorption site, as well as a nonlocalized jumping or “hopping” between symmetry-equivalent adsorption sites. The combined analysis of SCXRD and SSNMR data has the potential to yield rich information regarding guest dynamics, adsorption locations, and host–guest interactions in many MOFs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".