Characterization of Zn‐Containing Metal–Organic Frameworks by Solid‐State <sup>67</sup>Zn NMR Spectroscopy and Computational Modeling
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are an extremely important class of porous materials with many applications. The metal centers in many important MOFs are zinc cations. However, their Zn environments have not been characterized directly by (67)Zn solid-state NMR (SSNMR) spectroscopy. This is because (67)Zn (I=5/2) is unreceptive with many unfavorable NMR characteristics, leading to very low sensitivity. In this work, we report, for the first time, a (67)Zn natural abundance SSNMR spectroscopic study of several representative zeolitic imidazolate frameworks (ZIFs) and MOFs at an ultrahigh magnetic field of 21.1 T. Our work demonstrates that (67)Zn magic-angle spinning (MAS) NMR spectra are highly sensitive to the local Zn environment and can differentiate non-equivalent Zn sites. The (67)Zn NMR parameters can be predicted by theoretical calculations. Through the study of MOF-5 desolvation, we show that with the aid of computational modeling, (67)Zn NMR spectroscopy can provide valuable structural information on the MOF systems with structures that are not well described. Using ZIF-8 as an example, we further demonstrate that (67)Zn NMR spectroscopy is highly sensitive to the guest molecules present inside the cavities. Our work also shows that a combination of (67)Zn NMR data and molecular dynamics simulation can reveal detailed information on the distribution and the dynamics of the guest species. The present work establishes (67)Zn SSNMR spectroscopy as a new tool complementary to X-ray diffraction for solving outstanding structural problems and for determining the structures of many new MOFs yet to come.
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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 | 0.001 | 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".