Investigating the Behavior of Adsorbed CO2 in Metal-Organic Frameworks via 13C Solid-state NMR Spectroscopy
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are a class of microporous materials with lots of unique properties that make them promising candidates for carbon dioxide (CO2) capture and storage. In this thesis, the adsorption behavior of CO2 in MOF UTSA-74 (a framework isomer of a well-known MOF, MOF-74-Zn) is studied at a molecular level as it is a promising material for CO2 storage. It has a distinct binuclear secondary building unit (SBU) that one of metal ions (Zn1) is in a tetrahedral coordination with no binding sites, while the other one (Zn2) is in an octahedral geometry with two open metal sites (OMSs) upon activation. Explicitly, variable temperature (VT) 13C static solid-state nuclear magnetic resonance spectroscopy is used to investigate the behavior of 13CO2 in UTSA-74 at low, moderate and high loading levels of 13CO2 (i.e. 0.30, 0.54, 0.90 and 1.48 13CO2/ Zn2). The results reveal that all 13CO2 molecules undergo localized wobbling. At low loading, some 13CO2 molecules jump among three Zn2 OMSs in the cross-section of the channel, while others hop back and forth between the two neighbouring OMSs. At high loading, the three-site jumping has ceased, but two-site hopping persists. The dynamical behavior of 13CO2 in UTSA-74 results from the unique Zn2 coordination environment. It was also discovered that 13CO2 is less mobile in UTSA-74 than in its framework isomer, MOF-74-Zn.
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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".