NMR of Metal Centers and Doped Metals in MOFs and COFs
Bibliographic record
Abstract
There have been tremendous advances in the fields of metal–organic frameworks (MOFs) and covalent-organic frameworks (COFs) over the past two decades. The rapidly expanding number of MOFs and COFs, along with their various associated properties, has highlighted the need for effective structural characterization routes in order to elucidate structure–property relationships. Solid-state nuclear magnetic resonance (SSNMR) spectroscopy provides unique insights that are helpful for understanding and rationalizing the local structure of various materials. In this chapter, we summarize the significant number of studies from the last 15 years which have used SSNMR to examine incorporated metal centers and dopant metals in MOFs and COFs, with targets ranging from spin-1/2 nuclei such as 111Cd and 207Pb to challenging quadrupolar nuclei including 25Mg, 47/49Ti, 43Ca, 67Zn and 115In. Examples of the detailed information available from metal SSNMR are provided, illustrating how this technique can shed light on the local structure around the target metal, investigate host–guest interactions, and monitor changes in the MOF structure. General acquisition strategies for metal SSNMR spectra of MOFs and COFs are also discussed.
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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".