<sup>35</sup>Cl NMR of Metal‐Organic Frameworks: What Can We Learn?
Bibliographic record
Abstract
Abstract Metal‐organic frameworks (MOFs) are a diverse class of hybrid organic‐inorganic materials with a wide range of applications. Chlorine often plays a crucial role in MOF structures; the local environment about Cl significantly affects material properties and applications. It is shown that direct characterization of Cl local environments within MOFs using 35Cl wideline solid‐state NMR (SSNMR) provides unique insights into the local electronic and chemical structure, including the Cl bonding mode. 35Cl SSNMR provides clear information regarding hydrogen bonding within MOFs and also yields direct evidence of phase transitions. There is a strong correlation linking 35Cl quadrupolar interaction parameters to local bond lengths and angles. It is also shown that 35Cl SSNMR of MOFs can be effective when paramagnetic centers are directly bound to Cl, greatly expanding the applicability of this approach. Density functional theory calculations of quadrupolar interaction parameters are in good agreement with experimental values, particularly when dispersion corrections are used for geometry optimization. This work highlights the broad potential of 35Cl SSNMR for investigating MOFs and invites further applications in the field.
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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 | 0.002 | 0.002 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".