NMR Investigations of Host–Guest Interactions in MOFs and COFs
Bibliographic record
Abstract
Host–guest interactions in porous metal–organic frameworks (MOFs) and covalent-organic frameworks (COFs) play a key role in enhancing the performance of these materials for practical applications; however, it is often very challenging to investigate these interactions at the molecular level. In recent years, many solid-state NMR (SSNMR) approaches, including in situ variable temperature (VT), 2D correlation, and pulsed field gradient (PFG) experiments, have offered unique insights into the local structure and dynamics of adsorbed guest molecules in MOFs and COFs. Recent SSNMR studies of MOFs and COFs containing guest molecules are summarized in this chapter. These reports encompass a variety of gaseous and liquid guests such as hydrogen, carbon dioxide, water, and methanol. We also highlight studies involving larger guest molecules, drugs, and biomolecules. It is apparent that SSNMR spectroscopy can provide a wealth of data pertaining to host–guest interactions in these materials; the information available commonly includes the number and location of guest adsorption sites, guest binding strengths, guest dynamics and diffusion rates, and guest-induced structural changes in the host. The studies discussed in this chapter illustrate how SSNMR spectroscopy serves as a powerful tool to probe host–guest interactions in MOFs/COFs, especially given the variety of potential target nuclei and the numerous experimental strategies that are available.
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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) | 0.003 | 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".