Spies Within Metal-Organic Frameworks: Investigating Metal Centers Using Solid-State NMR
Bibliographic record
Abstract
Structural characterization of metal–organic frameworks (MOFs) is crucial, since an understanding of the relationship between the macroscopic properties of these industrially relevant materials and their molecular-level structures allows for the development of new applications and improvements in current performance. In many MOFs, the incorporated metal centers dictate the short- and long-range structure and porosity of the material. Here we demonstrate that solid-state NMR (SSNMR) spectroscopy targeting NMR-active metal centers at natural abundance, in concert with ab initio density functional theory (DFT) calculations and X-ray diffraction (XRD), is a powerful tool for elucidating the molecular-level structure of MOFs. 91 Zr SSNMR experiments on MIL-140A are paired with DFT calculations and geometry optimizations in order to detect inaccuracies in the reported powder XRD crystal structure. 115 In and 139 La SSNMR experiments on sets of related MOFs at two different magnetic fields illustrate the sensitivity of the 115 In/ 139 La electric field gradient tensors to subtle differences in coordination, bond length distribution, and ligand geometry about the metal center. 47/49 Ti SSNMR experiments reflect the presence or absence of guest solvent in MIL-125(Ti), and when combined with DFT calculations, these SSNMR experiments permit the study of local hydroxyl group configurations within the MOF channels. 67 Zn SSNMR experiments and DFT calculations are also used to explore the geometry near Zn within a set of four MOFs as well as local disordering caused by distributions of different linkers around the metal. SSNMR spectroscopy of metal centers offers an impressive addition to the arsenal of techniques for MOF characterization and is particularly useful in cases where XRD information may be ambiguous, incomplete, or unavailable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".