Structure and properties of materials by solid-state nuclear magnetic resonance
Bibliographic record
Abstract
Since the early days of nuclear magnetic resonance (NMR) spectroscopy, experiments on crystalline materials have provided structural and crystallographic information. The scope of this information may range from a single internuclear distance to a complete structural model. In this lecture, I will provide an overview of the field of NMR Crystallography, a topic on which the IUCr established a Commission in 2014, as well as NMR applications to crystal engineering [1]. NMR crystallographic methods are frequently used in combination with diffraction methods, and offer particular advantages for studying disorder, dynamics, and heterogeneous systems, for example. I will then present a survey of applications of solid-state NMR spectroscopy to the study of various organic and inorganic materials, with an emphasis on work from my own laboratory. For example, we have developed and applied a multinuclear magnetic resonance crystallographic structure refinement and cross-validation protocol using experimental and computed electric field gradients [2]. A second aspect of our work is the characterization of halogen-bonded cocrystals and frameworks, often prepared via mechanochemical approaches. Solid-state NMR spectroscopy is used in this context to provide insights into the formation and structure of cocrystals, as well as the nature of the halogen bond [3]. As a third example, I will describe various intriguing applications of two-dimensional double-quantum filtered NMR experiments. In favourable cases, such experiments may be employed to provide direct information on crystallographic symmetry and on dynamic disorder in solids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".