Structural Dynamics by NMR in the Solid State: II. The MOMD Perspective of the Dynamic Structure of Metal–Organic Frameworks Comprising Several Mobile Components
Bibliographic record
Abstract
We describe the application of the microscopic-order-macroscopic-disorder (MOMD) approach, developed for the analysis of dynamic 2 H NMR lineshapes in the solid state, to unravel interactions among the constituents of metal–organic frameworks (MOFs) that comprise mobile components. MOMD was applied recently to University of Windsor Dynamic Material (UWDM) MOFs with one mobile crown ether per cavity. In this work, we study UWDM-9- d 4, which comprises a mobile 2 H-labeled phenyl-ring residue along with an isotopically unlabeled 24C8 crown ether. We also study UiO-68- d 4, which is structurally similar to UWDM-9- d 4 but lacks the crown ether. The physical picture consists of the NMR probe─the C–D bonds of the phenyl- d 4 rotor─diffusing locally (diffusion tensor R ) in the presence of a local ordering potential, u . For UiO-68- d 4, we find it sufficient to expand u in terms of four real Wigner functions, D 0| K | L, overall 2–3 kT in magnitude, with R ∥ relatively fast, and R ⊥ in the (2.8–5.0) × 10 2 s –1 range. For UWDM-9- d 4, u requires only two terms 2–3 kT in magnitude and slower rate constants R ∥ and R ⊥ . In the more crowded macrocycle-containing UWDM-9- d 4 cavity, phenyl- d 4 dynamics is more isotropic and is described by a simpler ordering potential. This is ascribed to cooperative phenyl-ring/macrocycle motion, which yields a dynamic structure more uniform in character. The experimental 2 H spectra used here were analyzed previously with a multi-simple-mode (MSM) approach where several independent simple motional modes are combined. Where possible, similar features have been identified and used to compare the two approaches.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".