NMR of Organic Linkers in MOFs and COFs
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) and covalent-organic frameworks (COFs) are excellent candidates for many practical applications due to their advantageous features, such as high porosities and selective guest adsorption. Organic ligands play a crucial role in both MOFs and COFs as versatile structural linkers and as sites for introducing or tailoring functionalities. Comprehensive characterization of the environments of organic ligands in MOFs and COFs is necessary for further understanding of structure–property relationships and for the rational design of future materials. Solid-state nuclear magnetic resonance (SSNMR) can provide detailed molecular-level information regarding the chemical environment around a target linker atom. Given that organic linkers in MOFs and COFs typically feature many NMR-active nuclei such as 1H, 11B, 13C, 15N, 17O, 19F, and 31P, SSNMR can yield detailed insights into these systems. SSNMR of MOFs and COFs can be used to ascertain the number of unique atoms in the crystal structure, map the distribution of organic linkers, investigate structural defects, probe ligand dynamics, and study guest binding locations. In this chapter, we review reports regarding SSNMR characterization of organic linkers in MOFs and COFs, along with the effects of paramagnetic centers near the surrounding organic linkers on SSNMR spectra. The advantages and drawbacks of various SSNMR methods and approaches for linker characterization in 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".