Metal‐Organic Frameworks: <scp>NMR</scp> Studies of Quadrupolar Nuclei
Bibliographic record
Abstract
Abstract Metal‐organic frameworks (MOFs) are a novel type of porous materials with many current and potential applications. Solid‐state NMR (SSNMR) spectroscopy is an excellent technique for MOF characterization. It can provide nuclide‐specific information on structure and dynamics, which is complementary to that obtained from X‐ray diffraction. The NMR‐active isotopes of vast majority of metal centers including 27 Al, 71 Ga, 45 Sc, 67 Zn, 25 Mg, and 115 In as well as some framework elements such as 17 O are quadrupolar nuclei. Compared to spin ‐1/2 nuclei such as 13 C, SSNMR spectroscopy of quadrupolar nuclei has traditionally considered very challenging because of the quadrupolar interaction that makes spectral acquisition and interpretation difficult. This is particularly true for those so‐called unreceptive quadrupolar nuclei (i.e., the nuclei with low gyromagnetic ratios, low natural abundances, and large quadrupole moments). However, in recent years, significant progresses have been made in developing SSNMR spectroscopy of quadrupolar nuclei. This chapter begins with a brief introduction of NMR spectroscopy of quadrupolar nuclei; followed by discussion of several very recent examples to illustrate that SSNMR spectroscopy of quadrupolar nuclei can be used as an effective tool to characterize the nuclear environments in MOF‐based materials. It is hoped that this chapter will encourage more researchers to use SSNMR spectroscopy for investigation of MOF properties by interrogating quadrupolar nuclei.
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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) | 0.002 | 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".