Mapping Out Chemically Similar, Crystallographically Nonequivalent Hydrogen Sites in Metal–Organic Frameworks by <sup>1</sup>H Solid-State NMR Spectroscopy
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) are important materials with many actual and potential applications. Crystal structure of many MOFs is determined by single-crystal X-ray diffraction. However, due to the inability of XRD to accurately locate hydrogen atoms, the local structures around framework hydrogen are usually poorly characterized even if the overall framework has been accurately determined. 1 H solid-state NMR (SSNMR) spectroscopy should, in principle, be used as a complementary method to XRD for characterizing hydrogen local environments. However, the spectral resolution of 1 H SSNMR is severely limited by the strong 1 H– 1 H homonuclear dipolar coupling. In this work, we demonstrate that high-resolution 1 H MAS spectra of MOF-based material can be obtained by ultrafast sample spinning at high magnetic field in combination with isotopic dilution. In particular, we examined an important MOF, microporous α-Mg 3 (HCOO) 6 and α-Mg 3 (HCOO) 6 in the presence of several guest species. All six chemically very similar, but crystallographically, nonequivalent H sites of these MOFs were resolved in a chemical shift range as small as 0.8 ppm. Although the assignment of 1 H peaks due to crystallographically nonequivalent hydrogens is difficult due to that they all have almost identical chemical environments, we are able to show that they can be assigned from 1 H– 1 H proximity maps obtained from 2D 1 H– 1 H double quantum (DQ) experiments in conjunction with theoretical calculations. 1 H MAS spectra of framework hydrogen are very sensitive to the guest molecules present inside the pores and they provide insight into host–guest interaction and dynamics of guest molecule. The ability of achieving very high resolution for 1 H MAS NMR in MOF-based materials and subsequent spectral assignment demonstrated in this work allows one to obtain new structural information complementary to that obtained from single-crystal XRD.
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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) | 0.000 | 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 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".